From 2f5367408d2531be2224fa949bfaee031932ea72 Mon Sep 17 00:00:00 2001
From: reveurmichael <reveurmichael@users.noreply.github.com>
Date: Fri, 27 Oct 2023 11:03:49 +0000
Subject: [PATCH] deploy: 43f79316defc736de55ee54f2447ae0ae7b7e980

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diff --git a/_sources/assignments/ml-fundamentals/create-a-regression-model.ipynb b/_sources/assignments/ml-fundamentals/create-a-regression-model.ipynb
index 182590436c..46a090c45e 100644
--- a/_sources/assignments/ml-fundamentals/create-a-regression-model.ipynb
+++ b/_sources/assignments/ml-fundamentals/create-a-regression-model.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "c796e548",
+   "id": "375b52a9",
    "metadata": {},
    "source": [
     "# Create a regression model\n",
diff --git a/_sources/assignments/ml-fundamentals/exploring-visualizations.ipynb b/_sources/assignments/ml-fundamentals/exploring-visualizations.ipynb
index 19f8f88390..f0d003eb32 100644
--- a/_sources/assignments/ml-fundamentals/exploring-visualizations.ipynb
+++ b/_sources/assignments/ml-fundamentals/exploring-visualizations.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "b274befa",
+   "id": "4073789d",
    "metadata": {},
    "source": [
     "# Exploring visualizations\n",
diff --git a/_sources/assignments/ml-fundamentals/linear-regression/linear-regression-from-scratch.ipynb b/_sources/assignments/ml-fundamentals/linear-regression/linear-regression-from-scratch.ipynb
index d8bf93b6fa..e4095fad3d 100644
--- a/_sources/assignments/ml-fundamentals/linear-regression/linear-regression-from-scratch.ipynb
+++ b/_sources/assignments/ml-fundamentals/linear-regression/linear-regression-from-scratch.ipynb
@@ -17,12 +17,29 @@
    ]
   },
   {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In previous sections, we have gained some understanding of linear regression, gradient descent, evaluation metrics, and the role of the loss function in this regression technique. In summary, linear regression utilizes gradient descent to optimize the model's parameters by minimizing the loss function. This enables it to establish a linear relationship between the input features and the target variable, making it a powerful algorithm for predicting continuous values."
+   ]
+  },
+  {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "Linear regression is a widely used method in data analysis to describe the relationship between independent variables and a dependent variable using a linear equation. It aims to minimize the error between predicted and actual values by finding the best-fit line or surface. Linear regression can be used for predicting trends, exploring relationships, and identifying patterns in the data."
    ]
   },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This chapter will apply the previously learnt knowledge to implement a linear regression model from scratch. The chapter includes steps for data preparation, model development, and model evaluation, and ultimately summarises the process of developing and evaluating linear regression models."
+   ]
+  },
   {
    "attachments": {},
    "cell_type": "markdown",
@@ -33,12 +50,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -48,6 +65,7 @@
     }
    ],
    "source": [
+    "import os\n",
     "import numpy as np\n",
     "import pandas as pd\n",
     "import matplotlib.pyplot as plt\n",
@@ -60,8 +78,12 @@
     "# Create a DataFrame from the data\n",
     "data = pd.DataFrame(np.concatenate([X, y], axis=1), columns=['X', 'y'])\n",
     "\n",
-    "# Save the data to a CSV file\n",
-    "data.to_csv('data.csv', index=False)\n",
+    "# Create the 'Temp' directory if it doesn't exist\n",
+    "if not os.path.exists('./tmp'):\n",
+    "    os.makedirs('./tmp')\n",
+    "\n",
+    "# Save the data to a CSV file in the 'Temp' directory\n",
+    "data.to_csv('./tmp/data.csv', index=False)\n",
     "\n",
     "# Plot the data\n",
     "plt.scatter(X, y)\n",
@@ -83,7 +105,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 41,
    "metadata": {},
    "outputs": [
     {
@@ -95,20 +117,26 @@
       "1  1.901429  9.405278\n",
       "2  1.463988  8.483724\n",
       "3  1.197317  5.604382\n",
-      "4  0.312037  4.716440\n",
-      "X的缺失值数量： 0\n",
-      "y的缺失值数量： 0\n"
+      "4  0.312037  4.716440\n"
      ]
     },
     {
      "data": {
-      "image/png": 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U/DVG7iZudDWTJD377LNyHEeDBg3S22+/rZtuukmFhYXasGGDunXr1un3U80EAKisqdMtT76iz774ut3X4kHOgkuH+9ZbxrSqq1Tl7F6+VoGpZpKkhoYGXXfddRo8eLAuu+wyjRo1SqtWrUorkAEAQJLGlJeoqGvyW178hj13xXbf8lNMqrpqanY0d8X2pMtyJrxWyRifMzN58mRNnjzZ72EAACy2acce1ccaU379wGChYmBv7wb2LZOqrtwEVn68VskYH8wAAOxmQhKpScFCMiZVXZn+WiVDMAMAyBtTqnNMChaS6azqKp4z40XVlemvVTLG58wAAOxkUnVOPFhINR8UUX5KtNPtGdNR1ZXXu3j79Vplg2AGAJBzpiWR+hEsuC1tHj+kVAsuHa6SaOKMR0m0yNNKK5MCq3QZX5qdLUqzAcB7VbW7NeXBjZ0e9/hVIz1NIvVq2Sub0mYTcowk/5cI3dy/yZkBAOScqUmk44eUakx5SV6Dhc5mpSJqmZUaU16S9P8t6BIxokrIi9cqVwhmAAA5Z3ISab6DBRtLm1MxJbDqDMEMAHzLlOn9IDCpOsdrps5KBRnBDADI//yAoIknkV6zeKsiUkJAY2oSaa6YPCsVVFQzAQg9k0qIg8SU6hyv2VjabDtmZgCEWrbJmmGWzrJcPIl04zu7VVW7W5Kjiu8eppEW5GFkKsyzUn4hmAEQakFK1vSSm2W51dvrE4697/laq5bwMsmlis9KtX2NSiw6b5sQzAAINZI13UvVQyW+LHfgEpKbY02UTS6VTaXNtiNnBkCokazpjpvOvqZ1AXYrF7lU8dLmSUP7qWJgbwKZPCGYARBqJGu642ZZzs2xprE9EAsbghkAoWbjPjR+crMsZ/MSns2BWBgRzAAIvbCWEGfCzbKczUt4NgdiYUQCMACIZM10ue3sm20XYL+6MtsciIURwQwAfMuWfWj85LaHSjb9VtxWEuUy8Anzdgw2ijiOE+jsJTdbiAMA0uMm0MikvDlVSXc8NGm7/JeP7SjiY5CSB2IsQeaXm/s3wQwAICNuZkLcHjtq/pqUCbjxWZENM89SQZeI68DHDfbs8o+b+zfLTACAjLhZlnNzrJtKohEDeuV1OwpyqexAMAMABvMrAdZPbiqJvNiOglwq8xHMAIChwrrE4aaSiBJqSPSZAQAj5aKVvq3cdGWmhBoSwQwAGCfsrfTddGVmOwpIBDMAYBxa6afflZntKCCRMwMAxiEPpEVHlURtE6Pvv3iYbnvmtYQgsCQE+UVoQTADAIYhD+RvklUSpUqM/tWEch3ao3uoKr/QgmUmADAMeSCpdZQYfd1jW9Xw5VeaNLSfKgb2JpAJEYIZADAMeSDJhT0xGqkRzACAgdJNgA0TEqORitE5M998843mzJmjf//3f1d9fb1KS0t1+eWX65e//KW6dCEOAxAsbZNax5SX0Er/ACRGIxWjg5n58+frgQce0J///Gcdf/zx2rJli6644gpFo1HdcMMNfg8PAHImrN1+3SAxGqkYPb1RVVWlSZMmacKECTr66KP1ox/9SGPHjtWWLVv8HhoA5EyYu/268em+r9TRpFSYE6PDzuhgZtSoUfrv//5vvfnmm5Kkl19+WRs2bNC5556b8nsaGxsVi8USPgDAVKYltTY1O6qq3a2nqz9UVe1uY5JpK2vqdN1jW9XZcMKYGA3Dl5lmzpyphoYGDR48WAUFBWpqatJvfvMbTZkyJeX3zJs3T3PnzvVwlACQOS92fU6XqUtdHQV8cV0i0n1ThrEklyO27dZudDDzxBNPaPHixXrsscd0/PHHq7q6WtOnT1ffvn01bdq0pN8za9YszZgxo/XzWCymsrIyr4YMIAC8/EVuSlJrfKmrbcAQX+rys4Kqs4BPkpod6dAehR6NKNhMDWo7YnQwc9NNN+mWW27RRRddJEn6/ve/r/fee0/z5s1LGcwUFhaqsJAfaACZ8foXuQlJrZ0tdUXUstQ1przEl7/OTQn4wsDkoLYjRufMfPHFF+1KsAsKCtTc3OzTiAAEmR+JuCZ0+zW9f4sJAV8YmJa/5YbRwczEiRP1m9/8Rs8884zeffddLVu2THfddZf+4R/+we+hAQgYv36Rm9Dt1/SZDxMCvjAwPajtiNHBzB/+8Af96Ec/0rXXXqvjjjtOv/jFL3T11Vfrtttu83toAALGz1/kfnf7NX3mw4SALwxMD2o7YnTOTM+ePXXPPffonnvu8XsoAALO71/k44eU+tbtNz7zUd+wP+nMVEQtgVXbmQ8vE6XjAd+c5a+qPtbY+vgRxYWa8/fHG5nHYRvTg9qOGB3MAIBXTPhFXtAlkvfy61T/7+yJ5bpm8VZFpISAJtXMh38VL6nmZpCtTINaExi9zAQAXgl7XoabpS4/EqXj/2d9LPH//DhGl+RcsXk5L+I4jnlpyTkUi8UUjUbV0NCg4uJiv4cDwGDxG6aUfHbC1LLUXOps6aip2dGo+WtS5hfF/3rfMPOsnN30/Pg/w8yUPjNu7t8sMwHAt+KzE21/kZcY3jAslzpb6vKjY7FJXZLDwM/8rUwRzADAAWz8Re4lPxKl/U7ODiO/8rcyRTADAG3Y9ovcS34kSpuQnA2zkQAMAEibH4nSYU/ORucIZgAAafOj4sXmKht4g2AGAOCKHx2L/e6SDLNRmg34zMsuqkAu+fGzy/USHpRmA5YwpZ8DkAk/EqVJzkYyLDMBPvGjiyoABBHBDOCDpmZHc1dsT7r/SfyxuSu2q6k50KvAAJATBDOAD9x0NAUAdIycGcAHdDQNHxJXc4fXEm0RzAA+oKNpuJDonTu8lkiGZSbAB3Q0DQ8SvXOH1xKpEMwAPqCjaTiQ6J07vJboCMEMfNXU7Kiqdreerv5QVbW7Q/WLiI6mwUeid+7wWqIj5MzAN2Fa+06VsDh+SKnGlJeQzBhQJHrnDq8lOkIwA1/E177bzsPE176DNDPRWdBGR9O/CVqVConeucNriY4QzMBzna19R9Sy9j2mvMTqG5kUrqAtW0GcqYsnetc37E/68x5Ry7Iiid6d47VER8iZgefCsvZNwmL6glqlQqJ37vBaoiMEM/BcWNa+wxK0ZSvoQR+J3rnDa4lUWGaC58Ky9h2WoC1bboI+W3OLSPTOHV5LJEMwA8+FZe07LEFbtsIS9JHonTu8lmiLZSZ4Lixr33T5TQ9BH4BsEczAF2FY+w5L0JYtgj4A2Yo4juMqq+7yyy/XT37yE51++un5GlNOxWIxRaNRNTQ0qLi42O/hoI2g9RVJJoglx7kWr2aSlLD0GP9JyEWAG4aftY6E/fxhHzf3b9fBzAUXXKBnnnlGZWVluuKKKzRt2jT169cvqwHnU9iDGX6BmYH3oXP5DPrCHlCG/fxhp7wGM5K0e/duLV68WIsWLVJNTY1Gjx6tK6+8UpMmTVK3bt0yHng+hDmY4RcYbJOPoC9V48JczvqYLOznD3vlPZg50LZt2/Twww/rT3/6k77zne/o0ksv1bXXXqtjjjkmm6fNmbAGM/wCA1qCo1Hz16Qs/Y5Xzm2YeZaVM2WdBX9BP38Em5v7d1YJwHV1dVq1apVWrVqlgoICnXvuuXr11VdVXl6uu+++O5unbnX00UcrEom0+7juuuty8vxBFPQmZEC6gty4sLKmTqPmr9GUBzfqhiXVmvLgRo2avyahW3KQzx84kOtg5uuvv9bSpUt13nnnqX///vrLX/6iG2+8UXV1dfrzn/+sVatW6dFHH9Wvf/3rnAxw8+bNqqura/1YvXq1JOnCCy/MyfMHEb/A4KemZkdVtbv1dPWHqqrd7WvQHNQeNulu/xDU8wfact00r7S0VM3NzZoyZYo2bdqkoUOHtjtm3LhxOuSQQ3IwPOnwww9P+PzOO+/UwIEDdcYZZ+Tk+YOIX2Dwi2l5WkHsYeNmo9Ygnj+QjOtg5u6779aFF16ooqLUP/yHHnqoduzYkdXAkvnqq6+0ePFizZgxQ5FI8vXdxsZGNTY2tn4ei8VyPg7T8QsMXmibr/HpvkZd99g2o3YID2K3aTczr0E8fyAZ18HM1KlT8zGOtDz11FP67LPPdPnll6c8Zt68eZo7d653gzIQv8CQb8lmYLpElNZsgZeJpvHGhdcs3qqIkvewsa1xoZuZ1yCeP5CMVR2AH3roIZ1zzjnq27dvymNmzZqlhoaG1o+dO3d6OEIz0HkW+ZQqX6Oj1Bg/87SC1m3a7cxr0M4fSMaajSbfe+89Pffcc3ryySc7PK6wsFCFhYUejcpc8V9gbf96LqHPDLLQUb5GOvzK0wrSTsuZzLwG6fyBZKwJZh555BH16dNHEyZM8Hso1uAXGHKts3yNzviZp1XQJaIRA3q1Xg/xnBLbrodMl47YaRpBZkUw09zcrEceeUTTpk1T165WDNkY/AJDLmU6s2JCnpZplVbZdDtm5hVIZEVk8Nxzz+n999/XT37yE7+HAoRaJjMrJuRppeqI7VelVS4CK2Zegb/JejsD04V1OwPYwbYNKOPt8VPla0gtVU0HJgP7vR+YaS392WoESI+b+7cVMzNAEJm27JGOdPI17psyXIf26G5MgOamL0u+l2TdNLwzOagFTGNVaTYQFOm2ozdRZ6W+555QqoqBvTVpaD9VDOzt+03ZpI7YbDUC5AczM4DHgvDXuU35GiZ1xDYpsAKChGAG8JhJyx7ZsKVSzqSO2CYFVkCQsMwEeIy/zr1lUkfseGCV6n+KqCVviq1GAHcIZgCP8de593LZ0r+p2VFV7W49Xf2hqmp3q6mjfRzaMCmwAoKEZSbAYyYte4RJLvJ8ctUfhoZ3QG7RZwbwQbyaSUpe3kyvEfPkuj+MbT2GAK+5uX+zzAT4gJ2M7dJZBZrUUoHmdsnJpBJ2wGYsMwE+sam8OeyCUoEGBBXBDOAjW8qbw44KNMBsLDMBQCeoQAPMxswMYDGSSL1BBRpgNoIZwFI2blRpq3Q22KQ/DOAflpkAC9m8UaWtqEADzMXMDGCZIGxUaSsq0AAzEcwAsiv3hDJhf1GBBpiHYAahZ1vuSbrlv/Wx/aqq3W1FgAYA2SCYQailalEfzz0xMRci3fLf2/7vq9qz7+vWz00O0AAgGyQAI7Ty0aLeC/Ey4c7mWA4MZKTwJQdns7s1ALswM4PQsjX3pLMy4VS37DAlB9u2dAggO8zMILRsblGfqky4V4/uHX7fgQFaUFG2DoQPMzMILdtb1CcrE65v+FI3/sfLnX5vrgM0U6rBKFsHwolgBqEVhBb1bcuEq2p3p/V9uQzQTFrSsXXpEEB2WGZCaMVzTyS1S6a1tUV9Z8nBEbUEGrkK0Exb0rF56RBA5ghmEGpBa1HvZYDmZzVYqkol25cOAWSGZSaEXtBa1McDtLZLPyU5Xvrxa0mno2WtMeUl1i8dAnCPYAZQ8FrUexGg+bGkk06TQ3a3BsKHZSb4hqZm+RUP0CYN7aeKgb1zfgP3ekkn3WWtMeUlgVo6BNA5ZmbgC5MqYJAZr6vB3Cxrmb50aEopOxAUBDPwnI37IaG9zjoRS7ld0nG7rGXq0iGBPJB7LDPBU7buh4TkvKwGC0Klkmml7EBQGD8z8+GHH2rmzJlauXKlvvzySx177LF66KGHdNJJJ/k9NGSApmbB49WSju1NDulODOSP0cHMp59+qtNOO01nnnmmVq5cqT59+qi2tlaHHHKI30NDhmhqlj9+5mF4saTj9bJWrhHIA/ljdDAzf/58lZWV6ZFHHml97Oijj/ZvQMhaEJYKTBSWPAyveujkA4E8kD9GBzPLly/XuHHjdOGFF2rdunXq16+frr32Wl111VUpv6exsVGNjY2tn8diMS+GijQFYanAtCqUsCVUm16plAqBPJA/Rgcz77zzjhYsWKAZM2bo1ltv1aZNm3T99dersLBQl112WdLvmTdvnubOnevxSJEum5cK3M5+eBH4hDUPw9RKpY7YHsgDJos4jmNs2Uj37t118skn64UXXmh97Prrr9fmzZtVVVWV9HuSzcyUlZWpoaFBxcXFeR8z0mPbskiq2Y94eNB29sOr86uq3a0pD27s9LjHrxpp3c0/iOI/R1LyQD5os2hANmKxmKLRaFr3b6NnZkpLS1VeXp7w2HHHHaelS5em/J7CwkIVFhbme2jIkk1LBW5nP7xc9iEPwy425/wAJjM6mDnttNP0xhtvJDz25ptvqn///j6NyDsm5mbkmi1LBW6qUEYM6OXpsg95GPaxKZAHbGF0MHPjjTfq1FNP1R133KHJkydr06ZNWrhwoRYuXOj30PLKtiUYr3kd6LmZ/fC6/DbMeRg2B/y2BPKALYwOZk455RQtW7ZMs2bN0q9//WsNGDBA99xzjy655BK/h5Y3YatMccuPQC/dWY23Pt6rtz7+PK1jc7XsY3NCdTYI+AEcyOgE4Fxwk0Dkt6ZmR6Pmr0n5l338r+wNM88K3M0pHW6TcHMl/r6kmv3IRK4TcsN0c/fr5wCAtwKTABw2dAhNzc8S5I5mP9zK17JPWPIwMv05sHlJCkDnCGYMQmVKan4HeqmqUNzI97JPGPIwMvk5CNOsFRBW7JptECpTUjMh0Bs/pFQbZp6lx68aqZ+d+T3X35+PnaTDxu3PAbtUA+HAzIxBwlyZcqBkSwKmBHrx2Y90b6o/O3OgjjmiJ0sbOeLm5yCs3ZGBMCKYMUhYK1MOlGpJ4FcTyo0K9NK9qZ72vcMDv/TjJTcBv99LkwC8wzKTYeK5GSXRxJtlGJYoOloSuO6xrfr7E1vOvW0o50egF7+ppvrfImoJwoI+i+a1eMAvdf5zYMLSZCpNzY6qanfr6eoPVVW7W03NgS4qBfKOmRkDhaUy5UDpLAksf7lO9188TLc985rvreCZRfNPulsCmLI02RYJyUDu0WcGRnCzYWJ8CcGEQM/WG1MQSpU7O4fO+gP50beJHjlA+ugzA9f8vrm5WRIwqQTZxlk0WwOwtjr7OTBt9oyEZCB/CGZgxM3N1CWBdJgUXHUmbNtlmLRLNQnJQP4QzIScKTc3ytLzL6wzA6bMnpmckAzYjmqmEIpXUizb9qFuXfZKypub1HJz86LSwk2VCjLjZmYgaOKzZ5OG9lPFwN6+/BzZPPsImI6ZmZBJtqSUitfT3iYtCQQRMwP+YvYRyB+CmRBJtaTUGS9vbqYsCQQRMwP+Mi0hGQgSlplCoqN8ic54fXMzYUkgiGj0578wN8UE8omZmZDoLF8iGaa9g4WZATMw+wjkHsGMh/zs5eJ2qYibWzCRl2QGm8r5ARsQzHjE714ubpeKuLkFl98zA343aAQQPAQzHjChl0s6lRS9enTXLyccp5LoQRndYIJ4kwriOUn+zQz4HdQDCCb2Zsqz+P4wqfJVvNwfJh5UScnzJbIJqoJ4kwriOfmJfYkAuOHm/k01U56Z1KgsX5UU8ZtU2/OMzzxV1tRlPGa/BPGc/NRZ92HJuwaNAIKHZaY8M61RWa7zJYLYIj+I5+Q39iUCkE8EM3lmYqOyXOZLBPEmFcRz8ptpQT2AYCGYybOgtzBP9+bz17f/X7uZIFOTa7nx5p6JQT2A4CCYybOgNypL9+Zz3/O1rf8ujRbp708s1fKX64xMruXGm3tBD+oB+IsEYA8EuYV5Zy3yk6lr2K8/rt9hbHItbf9zj13RAeQTpdkeMnVZJVupSr4z4WWpekdyUcYe1Pc7G5S7A0iXm/s3wQzS1tHNOdlNKhuPXzXS9+TabG683LRTI8gDkA6CmQMQzORGOjfnA29Sb338ue57/u2M/7/fXzRUk4b2y3rc2crkxktzOADInpv7NwnAOZbrvzpN+Cs23e0YDiz5rqrdnVUwY0pyrdsydnrUAID3CGZyKNdLCyYsVWR6c+6seiUV26ta6FEDAN6jmilHct3+3pR2+plux9BR9UoqQahqoUcNAHjP6GBmzpw5ikQiCR8lJSV+D6udXO87Y9I+NtncnFOVpJdGi3T16QNUGsBSdXrUAID3jF9mOv744/Xcc8+1fl5QUODjaJLL9dKCSUsV2d6cO9oL6ubxx/meD5RrNIcDAO8ZH8x07drVyNmYA+V6acGkpYpc3JxTJdHmco8oUwS94zMAmMjoZSZJeuutt9S3b18NGDBAF110kd55550Oj29sbFQsFkv4yLdcLy2YtFRB51b3vOr43NTsqKp2t56u/lBVtbs9WXYEABMZ3Wdm5cqV+uKLL3Tsscfq448/1u23367XX39dr776qnr3Tv4X/Zw5czR37tx2j+ezz0xTs6NR89d0OnuRblfbXD9fLphQWWWbA8vqD+tRKEWkTz5vzMmSGu8HgKALbNO8ffv2aeDAgbr55ps1Y8aMpMc0NjaqsbGx9fNYLKaysrK8N83LRfv7fD5fLnjd88aEHju5kI+SfZryAQi6wAYzkjRmzBh973vf04IFC9I63ssOwEHsM+OXoJx7rgOP+KxdqgRxU/a2AoBsBbYDcGNjo1577TX98Ic/9HsoSXVUuWPC89ki3Y7DpstHN2CTKt0AwBRGBzO/+MUvNHHiRB111FHatWuXbr/9dsViMU2bNs3voaWU6wqdIFb8dCRI2wG4DTzSWVYzqdINAExhdDDzwQcfaMqUKfrkk090+OGHa+TIkdq4caP69+/v99CQJ0GaeXATeKS7rGZSpRsAmMLoYGbJkiV+DwEeC9LMQ7oBxbuf7NM9z72V1rIaTfkAoD3j+8zAHrnoexKkmYd44JFqMSyiltmXxze9n/bWFfT9AYD2CGYslK9madk8b2VNnUbNX6MpD27UDUuqNeXBjRo1f43rDTHTDQBsmHlIJ/C46JSjVB9rVCrJNvL0qikfANjC6GWmMHDbS6Wypk5zlr+acAMsKS7UnL8/PqubWDal0LmsPgradgDxwKPta1vy7Wvb+E1zWs/TdlktrJVuAJCMdX1m3PKyz4xb6QYQ8YBn9fZ6PfzXd1M+3wMZ/lWeTS+UfPU9CUqfmbhUQWtV7W5NeXBjp9//+FUjjU94BoBcCmyfmSBJdzYj2U09lVuefMV1yXK2pdD5qj4K2sxDqhJ7EnoBIHvkzPigswBCagkg/ut/WwKedAIZSfrsi6+1sXa3q7G4CUaSyWf1UTwAmDS0nyoG9rY2kOkICb0AkD2CGR+kG0D88umapAFPR6re+cTV8dkGI0GqPvILCb0AkB2WmTKUzSaI6QYQe/Z9lcHI3P0Fn20wwjJJbgRtWQ0AvEQwk4Fsk1PzOUvhNkk022AkaNVHfgrb1hUAkCssM7kUT9xtu0wUT9xNp69KOr1UevXo5npshx7cTSO/6+5mmIucDZZJAAB+ojTbhVyWIceDIin5bMb9Fw/Tbc+8lnLGJJlMS7Pj48m2FDqbpTcAAA5EaXae5LIMubNmauOHlKpLl0jS5Zu2ctF/JRc5GyyTAAD8QDDjQq7LkDsLIFIGPMWFmjLiKB19WI+czoAQjAAAbEQw44Lbyp90ll06CyCocgEAoGMEMy64qfzJZTt+ZkwAAEiNaiYX0q38Wb29PuuKJwAAkB6CGZc6K0MeU16S1lYFTc2BLiIDAMAzLDNloKM8lqra3XnZeBEAACRHMJOhVHks+dx4EQAAtEcwk2NsvBg+NAsEAH8RzOQYGy+GSy6r1gAAmSEBOMdysdcR7JCLfboAANkjmMkDNl4MvqZmh6o1ADAEy0x5QufeYMvlPl0AgOwQzOQRnXuDi6o1ADAHy0xABqhaAwBzEMwAGYhXraVaNIyopaqJqjUAyD+CGSADVK0BgDkIZoAMUbUGAGYgARjIAlVrAOA/ghkgS1StAYC/WGYCAABWY2Ym5NgkEQBgO6tmZubNm6dIJKLp06f7PZRAqKyp06j5azTlwY26YUm1pjy4UaPmr2FPIQCAVawJZjZv3qyFCxfqhBNO8HsogcAmiQCAoLAimPn88891ySWX6MEHH9Shhx7a4bGNjY2KxWIJH0jEJokAgCCxIpi57rrrNGHCBI0ePbrTY+fNm6doNNr6UVZW5sEI7eJmk0QAAExnfALwkiVLtHXrVm3evDmt42fNmqUZM2a0fh6LxQho2rBhk0QSkwEA6TI6mNm5c6duuOEGrVq1SkVF6W3YV1hYqMLCwjyPzG6mb5JYWVOnuSu2J8welUaLNHtiOV11AQDtGL3M9NJLL2nXrl066aST1LVrV3Xt2lXr1q3Tvffeq65du6qpqcnvIVrJ5E0SSUwGALhldDBz9tln65VXXlF1dXXrx8knn6xLLrlE1dXVKigo8HuIVjJ1k0QSkwEAmTB6malnz54aMmRIwmM9evRQ79692z0Od+KbJLZdzinxcTnHTWIy2wcAAOKMDmaQX6ZtkmhDYjIAwDzWBTNr1671ewiBYtImiaYnJgMAzGR0zgzCxeTEZACAuQhmYAxTE5MBAGYjmIFR4onJJdHEpaSSaJEWXDqcPjMAgHasy5lB8JmWmAwAMBvBDIxkUmIyAMBsLDMBAACrEcwAAACrEcwAAACrEcwAAACrEcwAAACrUc0UME3NDiXNAIBQIZgJkMqauna7YJf6uAs2AABeYJkpICpr6nTN4q0JgYwk1Tfs1zWLt6qyps6nkQEAkF8EMwHQ1Oxo7ortcpJ8Lf7Y3BXb1dSc7AgAAOxGMBMAm3bsaTcjcyBHUl3Dfm3asce7QQEA4BFyZnyWi4TdXXtTBzKZHAcAgE0IZnyUq4TdPj2LOj/IxXEAANiEZSaf5DJhd8SAXiqNFinVfE5ELUHSiAG9Mh8wAACGIpjxQa4Tdgu6RDR7YrkktQto4p/PnlhOvxkAQCARzPggHwm744eUasGlw1USTVxKKokWacGlw+kzAwAILHJmfJCvhN3xQ0o1prwkrx2A6TAMADANwYwP8pmwW9AlooqBvV1/XzroMAwAMBHLTD6wMWGXDsMAAFMRzPjAtoRdOgwDAExGMOMTmxJ26TAMADAZOTM+8iJhNxfoMAwAMBnBjM/ymbCbK3QYBgCYjGUmdMrGhGUAQHgQzKBTtiUsAwDChWAGabEpYRkAEC7kzCBttiQsAwDChWAGrtiQsAwACBejl5kWLFigE044QcXFxSouLlZFRYVWrlzp97AAAIBBjA5mjjzySN15553asmWLtmzZorPOOkuTJk3Sq6++6vfQAACAISKO41jVg75Xr176l3/5F1155ZVJv97Y2KjGxsbWz2OxmMrKytTQ0KDi4mKvhgkAALIQi8UUjUbTun8bPTNzoKamJi1ZskT79u1TRUVFyuPmzZunaDTa+lFWVubhKAEAgNeMn5l55ZVXVFFRof379+s73/mOHnvsMZ177rkpj2dmBgAA+7mZmTG+mmnQoEGqrq7WZ599pqVLl2ratGlat26dysvLkx5fWFiowsJCj0cJAAD8YvzMTFujR4/WwIED9cc//jGt491EdgAAwAyBzJmJcxwnYRkJAACEm9HLTLfeeqvOOecclZWVae/evVqyZInWrl2ryspKv4cGAAAMYXQw8/HHH2vq1Kmqq6tTNBrVCSecoMrKSo0ZMybt54ivosVisXwNEwAA5Fj8vp1ONox1OTNuffDBB5RnAwBgqZ07d+rII4/s8JjABzPNzc366KOP1LNnT0Ui2W+IGC/13rlzZ2ATisNwjlI4zjMM5yiF4zzDcI5SOM4zDOcoZX+ejuNo79696tu3r7p06TjF1+hlplzo0qVLpxFdJuL7RQVZGM5RCsd5huEcpXCcZxjOUQrHeYbhHKXszjMajaZ1nHXVTAAAAAcimAEAAFYjmHGpsLBQs2fPDnSX4TCcoxSO8wzDOUrhOM8wnKMUjvMMwzlK3p5n4BOAAQBAsDEzAwAArEYwAwAArEYwAwAArEYwAwAArBb6YObf/u3fNGDAABUVFemkk07S//zP/3R4/Lp163TSSSepqKhI3/3ud/XAAw+0O2bp0qUqLy9XYWGhysvLtWzZsnwNP21uzvPJJ5/UmDFjdPjhh6u4uFgVFRV69tlnE45ZtGiRIpFIu4/9+/fn+1RScnOOa9euTTr+119/PeE429/Lyy+/POl5Hn/88a3HmPZerl+/XhMnTlTfvn0ViUT01FNPdfo9tl2Xbs/R1mvS7XnaeF26PUcbr8l58+bplFNOUc+ePdWnTx+df/75euONNzr9Pi+vy1AHM0888YSmT5+uf/qnf9K2bdv0wx/+UOecc47ef//9pMfv2LFD5557rn74wx9q27ZtuvXWW3X99ddr6dKlrcdUVVXpxz/+saZOnaqXX35ZU6dO1eTJk/Xiiy96dVrtuD3P9evXa8yYMfqv//ovvfTSSzrzzDM1ceJEbdu2LeG44uJi1dXVJXwUFRV5cUrtuD3HuDfeeCNh/Mccc0zr14LwXv7+979POL+dO3eqV69euvDCCxOOM+m93Ldvn0488UTdd999aR1v43Xp9hxtvCYl9+cZZ9N16fYcbbwm161bp+uuu04bN27U6tWr9c0332js2LHat29fyu/x/Lp0QmzEiBHOP/7jPyY8NnjwYOeWW25JevzNN9/sDB48OOGxq6++2hk5cmTr55MnT3bGjx+fcMy4ceOciy66KEejds/teSZTXl7uzJ07t/XzRx55xIlGo7kaYtbcnuPzzz/vSHI+/fTTlM8ZxPdy2bJlTiQScd59993Wx0x7Lw8kyVm2bFmHx9h6Xcalc47JmH5NtpXOedp6XcZl8l7adk06juPs2rXLkeSsW7cu5TFeX5ehnZn56quv9NJLL2ns2LEJj48dO1YvvPBC0u+pqqpqd/y4ceO0ZcsWff311x0ek+o58y2T82yrublZe/fuVa9evRIe//zzz9W/f38deeSROu+889r9leiVbM5x2LBhKi0t1dlnn63nn38+4WtBfC8feughjR49Wv3790943JT3MhM2XpfZMv2azJZN12W2bLwmGxoaJKndz9+BvL4uQxvMfPLJJ2pqatIRRxyR8PgRRxyh+vr6pN9TX1+f9PhvvvlGn3zySYfHpHrOfMvkPNv63e9+p3379mny5Mmtjw0ePFiLFi3S8uXL9fjjj6uoqEinnXaa3nrrrZyOPx2ZnGNpaakWLlyopUuX6sknn9SgQYN09tlna/369a3HBO29rKur08qVK/XTn/404XGT3stM2HhdZsv0azJTNl6X2bDxmnQcRzNmzNCoUaM0ZMiQlMd5fV0GftfszkQikYTPHcdp91hnx7d93O1zeiHTMT3++OOaM2eOnn76afXp06f18ZEjR2rkyJGtn5922mkaPny4/vCHP+jee+/N3cBdcHOOgwYN0qBBg1o/r6io0M6dO/Wv//qvOv300zN6Tq9kOqZFixbpkEMO0fnnn5/wuInvpVu2XpeZsOmadMvm6zITNl6TP/vZz/S///u/2rBhQ6fHenldhnZm5rDDDlNBQUG7CHDXrl3tIsW4kpKSpMd37dpVvXv37vCYVM+Zb5mcZ9wTTzyhK6+8Uv/xH/+h0aNHd3hsly5ddMopp/jyl0M253igkSNHJow/SO+l4zh6+OGHNXXqVHXv3r3DY/18LzNh43WZKVuuyVwy/brMlI3X5M9//nMtX75czz//vI488sgOj/X6ugxtMNO9e3eddNJJWr16dcLjq1ev1qmnnpr0eyoqKtodv2rVKp188snq1q1bh8ekes58y+Q8pZa//i6//HI99thjmjBhQqf/j+M4qq6uVmlpadZjdivTc2xr27ZtCeMPynsptVQjvP3227ryyis7/X/8fC8zYeN1mQmbrslcMv26zJRN16TjOPrZz36mJ598UmvWrNGAAQM6/R7Pr0vXKcMBsmTJEqdbt27OQw895Gzfvt2ZPn2606NHj9as8ltuucWZOnVq6/HvvPOOc/DBBzs33nijs337duehhx5yunXr5vznf/5n6zF//etfnYKCAufOO+90XnvtNefOO+90unbt6mzcuNHz84tze56PPfaY07VrV+f+++936urqWj8+++yz1mPmzJnjVFZWOrW1tc62bducK664wunatavz4osven5+juP+HO+++25n2bJlzptvvunU1NQ4t9xyiyPJWbp0aesxQXgv4y699FLnBz/4QdLnNO293Lt3r7Nt2zZn27ZtjiTnrrvucrZt2+a89957juME47p0e442XpOO4/48bbwu3Z5jnE3X5DXXXONEo1Fn7dq1CT9/X3zxResxfl+XoQ5mHMdx7r//fqd///5O9+7dneHDhyeUmk2bNs0544wzEo5fu3atM2zYMKd79+7O0Ucf7SxYsKDdc/7lL39xBg0a5HTr1s0ZPHhwwoXoFzfnecYZZziS2n1Mmzat9Zjp06c7Rx11lNO9e3fn8MMPd8aOHeu88MILHp5Re27Ocf78+c7AgQOdoqIi59BDD3VGjRrlPPPMM+2e0/b30nEc57PPPnMOOuggZ+HChUmfz7T3Ml6em+rnLwjXpdtztPWadHueNl6Xmfy82nZNJjs/Sc4jjzzSeozf12Xk24ECAABYKbQ5MwAAIBgIZgAAgNUIZgAAgNUIZgAAgNUIZgAAgNUIZgAAgNUIZgAAgNUIZgAAgNUIZgAAgNUIZgBYpampSaeeeqouuOCChMcbGhpUVlamX/7ylz6NDIBf2M4AgHXeeustDR06VAsXLtQll1wiSbrsssv08ssva/PmzerevbvPIwTgJYIZAFa69957NWfOHNXU1Gjz5s268MILtWnTJg0dOtTvoQHwGMEMACs5jqOzzjpLBQUFeuWVV/Tzn/+cJSYgpAhmAFjr9ddf13HHHafvf//72rp1q7p27er3kAD4gARgANZ6+OGHdfDBB2vHjh364IMP/B4OAJ8wMwPASlVVVTr99NO1cuVK/fa3v1VTU5Oee+45RSIRv4cGwGPMzACwzpdffqlp06bp6quv1ujRo/WnP/1Jmzdv1h//+Ee/hwbABwQzAKxzyy23qLm5WfPnz5ckHXXUUfrd736nm266Se+++66/gwPgOZaZAFhl3bp1Ovvss7V27VqNGjUq4Wvjxo3TN998w3ITEDIEMwAAwGosMwEAAKsRzAAAAKsRzAAAAKsRzAA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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training set size: (80, 1) (80,)\n",
+      "Testing set size: (20, 1) (20,)\n"
+     ]
     }
    ],
    "source": [
@@ -116,23 +144,42 @@
     "import numpy as np\n",
     "import pandas as pd\n",
     "import matplotlib.pyplot as plt\n",
+    "from sklearn.model_selection import train_test_split\n",
     "\n",
     "# Load the dataset\n",
-    "data = pd.read_csv('data.csv')\n",
+    "data = pd.read_csv('./Temp/data.csv')\n",
     "\n",
     "# Perform exploratory data analysis\n",
     "# Display the first few rows of the data\n",
     "print(data.head())\n",
     "\n",
-    "# Check for missing values\n",
-    "print(\"X的缺失值数量：\", data['X'].isnull().sum())\n",
-    "print(\"y的缺失值数量：\", data['y'].isnull().sum())\n",
+    "# Split the dataset into training and testing sets\n",
+    "X = data[['X']]\n",
+    "y = data['y']\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
     "\n",
     "# Visualize the relationship between features and target variable\n",
-    "plt.scatter(data['X'], data['y'])\n",
+    "plt.scatter(X_train, y_train, label='Training Set')\n",
+    "plt.scatter(X_test, y_test, label='Testing Set')\n",
     "plt.xlabel('X')\n",
     "plt.ylabel('y')\n",
-    "plt.show()"
+    "plt.legend()\n",
+    "plt.show()\n",
+    "\n",
+    "# Print the sizes of training and testing sets\n",
+    "print(\"Training set size:\", X_train.shape, y_train.shape)\n",
+    "print(\"Testing set size:\", X_test.shape, y_test.shape)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Model Development\n",
+    "Once we have prepared the data, we can proceed with developing the linear regression model. This involves deriving the mathematical formula for linear regression, implementing the formula in code, defining a cost function, and implementing the gradient descent algorithm to train the model.\n",
+    "\n",
+    "Let's take a look at the code snippet below to understand how we can develop the linear regression model:"
    ]
   },
   {
@@ -140,13 +187,29 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "In this code snippet, we first import the necessary libraries, such as NumPy and Pandas. Then, we load the dataset using the read_csv() function from Pandas, replacing 'data.csv' with the actual path to your dataset.\n",
+    "### Define loss function\n",
     "\n",
-    "Next, we perform exploratory data analysis by printing the first few rows of the dataset using head(), displaying the shape of the dataset using shape, and printing summary statistics using describe().\n",
+    "    Calculate the loss between the true target variable and the predicted target variable.\n",
     "\n",
-    "After that, we check for missing values in the dataset using isnull().sum(). If there are missing values, we can handle them accordingly. In this example, we simply drop rows with missing values using dropna().\n",
+    "    Parameters:\n",
+    "    - y_true: The true target variable (numpy array or pandas Series)\n",
+    "    - y_pred: The predicted target variable (numpy array or pandas Series)\n",
     "\n",
-    "Then, we perform any necessary preprocessing steps, such as feature scaling or encoding categorical variables. Finally, we split the dataset into input features (X) and the target variable (y), convert them to NumPy arrays using np.array(), and verify their dimensions using shape."
+    "    Returns:\n",
+    "    - loss: The calculated loss (float)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define the loss function\n",
+    "def loss_function(y_true, y_pred):\n",
+    "    # Calculate the mean squared error\n",
+    "    mse = np.mean((y_true - y_pred) ** 2)\n",
+    "    return mse"
    ]
   },
   {
@@ -154,62 +217,66 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Model Development\n",
-    "Once we have prepared the data, we can proceed with developing the linear regression model. This involves deriving the mathematical formula for linear regression, implementing the formula in code, defining a cost function, and implementing the gradient descent algorithm to train the model.\n",
+    "### Use gradient descent to train the model\n",
     "\n",
-    "Let's take a look at the code snippet below to understand how we can develop the linear regression model:"
+    "The `gradient_descent` function is responsible for performing gradient descent to optimize the coefficients of the linear regression model. Here is a breakdown of the steps involved:\n",
+    "\n",
+    "1. Scale the data: The input features (`X_train` and `X_test`) are scaled using the `StandardScaler` from scikit-learn. This ensures that all features have a similar scale, which can improve the performance of the gradient descent algorithm.\n",
+    "\n",
+    "2. Add a bias term: A column of ones is added to the input features (`X_train`) to account for the bias term in the linear regression model.\n",
+    "\n",
+    "3. Initialize coefficients: The coefficients are initialized with zeros. The number of coefficients is equal to the number of features plus one (including the bias term).\n",
+    "\n",
+    "4. Perform gradient descent: The function iterates over a specified number of iterations. In each iteration, the following steps are performed:\n",
+    "   - Compute the predictions (`y_pred`) by multiplying the input features (`X_train_with_bias`) with the coefficients.\n",
+    "   - Compute the gradients by taking the dot product of the transposed input features (`X_train_with_bias.T`) and the difference between the predictions and the true target variable (`y_train`).\n",
+    "   - Update the coefficients by subtracting the learning rate multiplied by the gradients.\n",
+    "   - Compute the loss by calculating the mean squared error between the true target variable (`y_train`) and the predictions (`y_pred`).\n",
+    "\n",
+    "5. Make predictions on the test set: The function applies the same preprocessing steps to the test set (`X_test`) and computes the predictions (`y_pred_test`) using the updated coefficients.\n",
+    "\n",
+    "This function is a key component in training the linear regression model from scratch. It allows us to iteratively update the coefficients based on the gradients, gradually improving the model's performance."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 43,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Mean Squared Error: 0.8065845639670534\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "# Deriving the mathematical formula for linear regression\n",
-    "# Implementing the formula in code\n",
-    "class LinearRegression:\n",
-    "    def __init__(self):\n",
-    "        self.coefficients = None\n",
+    "# Define the gradient descent function\n",
+    "def gradient_descent(X_train, y_train, X_test, y_test, learning_rate, num_iterations):\n",
+    "    # Scale the data\n",
+    "    scaler = StandardScaler()\n",
+    "    X_train_scaled = scaler.fit_transform(X_train.values.reshape(-1, 1))\n",
+    "    X_test_scaled = scaler.transform(X_test.values.reshape(-1, 1))\n",
     "\n",
-    "    def fit(self, X, y):\n",
-    "        # Add a column of ones to X for the bias term\n",
-    "        X = np.c_[np.ones((X.shape[0], 1)), X]\n",
+    "    # Add a column of ones to X for the bias term\n",
+    "    X_train_with_bias = np.c_[np.ones((X_train_scaled.shape[0], 1)), X_train_scaled]\n",
     "\n",
-    "        # Compute the coefficients using the normal equation\n",
-    "        self.coefficients = np.linalg.inv(X.T.dot(X)).dot(X.T).dot(y)\n",
+    "    # Initialize the coefficients with zeros\n",
+    "    coefficients = np.zeros((X_train_with_bias.shape[1], 1))\n",
     "\n",
-    "    def predict(self, X):\n",
-    "        # Add a column of ones to X for the bias term\n",
-    "        X = np.c_[np.ones((X.shape[0], 1)), X]\n",
+    "    # Perform gradient descent\n",
+    "    for i in range(num_iterations):\n",
+    "        # Compute the predictions\n",
+    "        y_pred = X_train_with_bias.dot(coefficients)\n",
     "\n",
-    "        # Predict the target variable\n",
-    "        y_pred = X.dot(self.coefficients)\n",
+    "        # Compute the gradients\n",
+    "        gradients = 2 * X_train_with_bias.T.dot(y_pred - y_train.values.reshape(-1, 1)) \n",
     "\n",
-    "        return y_pred\n",
+    "        # Update the coefficients\n",
+    "        coefficients -= learning_rate * gradients\n",
     "\n",
-    "# Defining cost function\n",
-    "def mean_squared_error(y_true, y_pred):\n",
-    "    return np.mean((y_true - y_pred) ** 2)\n",
+    "        # Compute the loss\n",
+    "        loss = np.mean((y_train.values.reshape(-1, 1) - y_pred) ** 2)\n",
+    "        print(\"Iteration:\", i+1, \"Loss:\", loss)\n",
     "\n",
-    "# 训练模型\n",
-    "regressor = LinearRegression()\n",
-    "regressor.fit(X, y)\n",
+    "    # Make predictions on the test set\n",
+    "    X_test_with_bias = np.c_[np.ones((X_test_scaled.shape[0], 1)), X_test_scaled]\n",
+    "    y_pred_test = X_test_with_bias.dot(coefficients)\n",
     "\n",
-    "# 使用训练好的模型进行预测\n",
-    "y_pred = regressor.predict(X)\n",
-    "\n",
-    "# Evaluating the model\n",
-    "mse = mean_squared_error(y, y_pred)\n",
-    "print('Mean Squared Error:', mse)"
+    "    return coefficients, y_pred_test"
    ]
   },
   {
@@ -217,11 +284,1035 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "In this code snippet, we first derive the mathematical formula for linear regression. Then, we implement the formula in code by creating a `LinearRegression` class. The `fit()` method is used to train the model using the normal equation, and the `predict()` method is used to make predictions on new data points.\n",
+    "Next, we train the linear regression model and make predictions on the test set. \n",
     "\n",
-    "We also define a cost function called `mean_squared_error()` to evaluate the performance of the model. Additionally, we can implement the gradient descent algorithm to train the model iteratively if desired.\n",
+    "First, we import the required library and set the learning rate and number of iterations for gradient descent. We then call the gradient descent function and pass in the training and test data, the learning rate, and the number of iterations. The function returns the optimised coefficients and predictions for the test set. Finally, we store the returned results in the variables coefficients and ypred test. \n",
     "\n",
-    "Finally, we train the model using the `fit()` method, make predictions using the `predict()` method, and evaluate the model's performance using the mean squared error (MSE)."
+    "By running this code, we can train a linear regression model using gradient descent and get the prediction results on the test set to further analyse and evaluate the performance of the model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
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+      "Iteration: 976 Loss: 0.8476788564209705\n",
+      "Iteration: 977 Loss: 0.8476788564209705\n",
+      "Iteration: 978 Loss: 0.8476788564209705\n",
+      "Iteration: 979 Loss: 0.8476788564209705\n",
+      "Iteration: 980 Loss: 0.8476788564209705\n",
+      "Iteration: 981 Loss: 0.8476788564209705\n",
+      "Iteration: 982 Loss: 0.8476788564209705\n",
+      "Iteration: 983 Loss: 0.8476788564209705\n",
+      "Iteration: 984 Loss: 0.8476788564209705\n",
+      "Iteration: 985 Loss: 0.8476788564209705\n",
+      "Iteration: 986 Loss: 0.8476788564209705\n",
+      "Iteration: 987 Loss: 0.8476788564209705\n",
+      "Iteration: 988 Loss: 0.8476788564209705\n",
+      "Iteration: 989 Loss: 0.8476788564209705\n",
+      "Iteration: 990 Loss: 0.8476788564209705\n",
+      "Iteration: 991 Loss: 0.8476788564209705\n",
+      "Iteration: 992 Loss: 0.8476788564209705\n",
+      "Iteration: 993 Loss: 0.8476788564209705\n",
+      "Iteration: 994 Loss: 0.8476788564209705\n",
+      "Iteration: 995 Loss: 0.8476788564209705\n",
+      "Iteration: 996 Loss: 0.8476788564209705\n",
+      "Iteration: 997 Loss: 0.8476788564209705\n",
+      "Iteration: 998 Loss: 0.8476788564209705\n",
+      "Iteration: 999 Loss: 0.8476788564209705\n",
+      "Iteration: 1000 Loss: 0.8476788564209705\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "# Perform gradient descent to train the model\n",
+    "learning_rate = 0.01\n",
+    "num_iterations = 1000\n",
+    "coefficients, y_pred_test = gradient_descent(X_train, y_train, X_test, y_test, learning_rate, num_iterations)"
    ]
   },
   {
@@ -232,39 +1323,56 @@
     "## Model Evaluation\n",
     "After training the linear regression model, it is important to evaluate its performance to assess how well it is able to make predictions. In this section, we will discuss some commonly used evaluation metrics for regression models.\n",
     "\n",
-    "When evaluating a machine learning model, we often use certain metrics to measure its performance. Here are some commonly used metrics and plotting methods:\n",
-    "\n",
-    "1. **Mean Absolute Error (MAE):** MAE is the average absolute difference between the predicted values and the actual values. It measures the average magnitude of the model's errors. A lower MAE value indicates better predictive performance of the model.\n",
-    "\n",
-    "2. **Root Mean Squared Error (RMSE):** RMSE is the square root of the average of the squared differences between the predicted values and the actual values. Compared to MAE, RMSE is more sensitive to large errors because it penalizes squared errors. Similar to MAE, a lower RMSE value indicates better predictive performance of the model.\n",
-    "\n",
-    "3. **R-squared:** R-squared measures the proportion of the variance in the dependent variable that is predictable from the independent variables. It ranges from 0 to 1. A higher R-squared value indicates a better fit of the model to the data.\n",
-    "\n",
-    "4. **Mean Squared Error (MSE):** MSE is the average of the squared differences between the predicted values and the actual values. Unlike RMSE, MSE does not take the square root, so its values are typically larger.\n",
-    "\n",
-    "Let's take a look at the code snippet below to understand how we can evaluate the linear regression model:"
+    "When evaluating a machine learning model, we often use certain metrics to measure its performance. Here are some commonly used metrics and plotting methods:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 45,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Mean Absolute Error: 0.7010426719637757\n",
-      "Root Mean Squared Error: 0.8981005311027566\n",
-      "R-squared: 0.7692735413614223\n",
-      "Mean Squared Error: 0.8065845639670534\n",
-      "Shape of X: (100, 1)\n",
-      "Shape of y: (100, 1)\n"
+      "Mean Squared Error: 0.6536995137169997\n",
+      "Mean Absolute Error: 0.5913425779189757\n",
+      "R-squared: 0.8072059636181399\n"
      ]
-    },
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score\n",
+    "\n",
+    "# Make predictions on the test set\n",
+    "y_pred = model.predict(X_test)\n",
+    "\n",
+    "# Compute the evaluation metrics\n",
+    "mse = mean_squared_error(y_test, y_pred)\n",
+    "mae = mean_absolute_error(y_test, y_pred)\n",
+    "r2 = r2_score(y_test, y_pred)\n",
+    "\n",
+    "print(\"Mean Squared Error:\", mse)\n",
+    "print(\"Mean Absolute Error:\", mae)\n",
+    "print(\"R-squared:\", r2)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The scatter plot shows the relationship between the features (X) and the actual values (blue dots), along with the predicted values (red line). This visualization helps us understand how well the model captures the underlying patterns in the data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -274,46 +1382,29 @@
     }
    ],
    "source": [
-    "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n",
     "import matplotlib.pyplot as plt\n",
     "\n",
-    "# Evaluating the model\n",
-    "# Mean Absolute Error (MAE)\n",
-    "mae = mean_absolute_error(y, y_pred)\n",
-    "print('Mean Absolute Error:', mae)\n",
-    "\n",
-    "# Root Mean Squared Error (RMSE)\n",
-    "rmse = np.sqrt(mean_squared_error(y, y_pred))\n",
-    "print('Root Mean Squared Error:', rmse)\n",
-    "\n",
-    "# R-squared\n",
-    "r2 = r2_score(y, y_pred)\n",
-    "print('R-squared:', r2)\n",
+    "# Plot the scatter plot of the training set\n",
+    "plt.scatter(X_train, y_train, label='Training Set')\n",
     "\n",
-    "mse = mean_squared_error(y, y_pred)\n",
-    "print('Mean Squared Error:', mse)\n",
+    "# Plot the scatter plot of the testing set\n",
+    "plt.scatter(X_test, y_test, label='Testing Set')\n",
     "\n",
-    "# Print shapes of X and y\n",
-    "print('Shape of X:', X.shape)\n",
-    "print('Shape of y:', y.shape)\n",
+    "# Plot the line representing the predicted results\n",
+    "plt.plot(X_test, y_pred_test, color='red', label='Predictions')\n",
     "\n",
-    "# Plot the data\n",
-    "plt.scatter(X[:, 0], y, color='blue', label='Actual')\n",
-    "plt.plot(X[:, 0], y_pred, color='red', label='Predicted')\n",
+    "# Set the title and labels for the chart\n",
+    "plt.title('Linear Regression')\n",
     "plt.xlabel('X')\n",
     "plt.ylabel('y')\n",
+    "\n",
+    "# Add a legend\n",
     "plt.legend()\n",
+    "\n",
+    "# Display the chart\n",
     "plt.show()"
    ]
   },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The scatter plot shows the relationship between the features (X) and the actual values (blue dots), along with the predicted values (red line). This visualization helps us understand how well the model captures the underlying patterns in the data."
-   ]
-  },
   {
    "attachments": {},
    "cell_type": "markdown",
@@ -335,16 +1426,6 @@
     "\n",
     "In conclusion, implementing a linear regression model from scratch is a great way to gain a deeper understanding of how machine learning algorithms work. We hope that this assignment has helped you to better understand the concepts and techniques involved in building and training machine learning models."
    ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Acknowledgments\n",
-    "\n",
-    "Thanks to the ChatGPT platform for providing the inspiration and guidance throughout this assignment."
-   ]
   }
  ],
  "metadata": {
@@ -363,7 +1444,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.5"
+   "version": "3.9.18"
   }
  },
  "nbformat": 4,
diff --git a/_sources/assignments/ml-fundamentals/linear-regression/loss-function.ipynb b/_sources/assignments/ml-fundamentals/linear-regression/loss-function.ipynb
index a517f8e3da..ee88281cd0 100644
--- a/_sources/assignments/ml-fundamentals/linear-regression/loss-function.ipynb
+++ b/_sources/assignments/ml-fundamentals/linear-regression/loss-function.ipynb
@@ -32,22 +32,6 @@
     "— Deep Learning, Ian Goodfellow, Yoshua Bengio, Aaron Courville"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Difference between a Loss Function and a Cost Function\n",
-    "\n",
-    "A loss function evaluates the error for a single training example, and it is occasionally referred to as an error function. In contrast, a cost function represents the **average loss** across the entire training dataset. Optimization strategies are designed to minimize this cost function.\n",
-    "\n",
-    "For a simple sample:\n",
-    "\n",
-    "The corresponding cost function of L1 Loss is the Mean of these Squared Errors (MSE).\n",
-    "You can see the difference of [Mathematical Expression](#regression-loss-functions)\n",
-    "\n",
-    "However, these terms are frequently used interchangeably in practical settings, they aren't precisely equivalent. From a definitional standpoint, the cost function represents an aggregation or average of the loss functions."
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -89,81 +73,6 @@
     "- L2 Regularization (Ridge): Curbs the unchecked growth of model parameters without nullifying them, ensuring the model remains generalized without undue complexity."
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Empirical Risk and Structural Risk\n",
-    "\n",
-    "### Definition\n",
-    "\n",
-    "Perhaps you've heard of these two concepts before. In the realms of machine learning and statistics, the concepts of empirical risk and structural risk are intricately tied to loss functions. **However, these terms aren't directly categories of loss functions perse.** Let's first clarify these concepts:\n",
-    "\n",
-    "1. **Empirical Risk:** Refers to the average loss of a model over a given dataset. Minimizing empirical risk focuses on reducing errors explicitly on the training data.\n",
-    "2. **Structural Risk:** Introduces a regularization term in addition to empirical risk, aiming to prevent overfitting. Minimizing structural risk strikes a balance between the empirical risk and the complexity of the model.\n",
-    "\n",
-    "Given these definitions:\n",
-    "\n",
-    "- **Empirical Risk:** Loss functions directly related to dataset performance fall under this category. From the ones been listed, regression losses (e.g., MSE, MAE, Huber Loss, L1 Loss, L2 Loss, Smooth L1 Loss), classification losses (e.g., Cross Entropy Loss, Hinge Loss, Log Loss), and structured losses (e.g., CTC or Image Segmentation Loss) can be seen as manifestations of empirical risk.\n",
-    "\n",
-    "- **Structural Risk:** Regularization losses, like L1 and L2 regularization, form part of structural risk. They don't measure the model's performance on the data directly but rather serve to rein in model complexity.\n",
-    "\n",
-    "### A Detaphor\n",
-    "\n",
-    "Maybe it's still abtract. So, now imagine you're a tailor trying to make a dress for a client.\n",
-    "\n",
-    "- **Empirical Risk:** This is like ensuring the dress fits the client perfectly based on a single fitting session. You measure every contour and make the dress to match those exact measurements. The dress is a perfect fit for the client on that particular day.\n",
-    "\n",
-    "However, what if the client gains or loses a little weight or wants to move more comfortably? A dress tailored too tightly to the exact measurements might not be very adaptable or comfortable in various situations.\n",
-    "\n",
-    "- **Structural Risk:** Now, consider that you decide to allow a bit more flexibility in the dress. You make it slightly adjustable, perhaps with some elastic portions. This way, even if the client's measurements change a bit, the dress will still fit comfortably. You're sacrificing a tiny bit of the \"perfect\" fit for the adaptability and general comfort.\n",
-    "\n",
-    "In the context of machine learning:\n",
-    "\n",
-    "Relying solely on **Empirical Risk** would be like fitting the dress exactly to the client's measurements, risking overfitting. If the data changes slightly, the model might perform poorly.\n",
-    "\n",
-    "Factoring in **Structural Risk** ensures the model isn't overly tailored to the training data and can generalize well to new, unseen data. It's about ensuring a balance between a perfect fit and adaptability.\n",
-    "\n",
-    "### Mathematical Explanation\n",
-    "\n",
-    "Now you have a general understanding of the meaning of empirical risk and structural risk. Let's delve into a more mathematical perspective:\n",
-    "\n",
-    "Given a dataset $\\mathcal{D}$ comprising input-output pairs $(x_1, y_1)$, $(x_2, y_2)$, ... $(x_n, y_n)$ and a model $f$ parameterized by $\\theta$, the empirical risk and structural risk can be formally defined as follows:\n",
-    "\n",
-    "**Empirical Risk(Cost Function):**\n",
-    "$$\n",
-    "R_{emp}(f) = \\frac{1}{n} \\sum_{i=1}^{n} L(y_i, f(x_i; \\theta))\n",
-    "$$\n",
-    "Where:\n",
-    "\n",
-    "- **$L$ is the loss function**, measuring the discrepancy between the predicted value $f(x_i; \\theta)$ and the actual output $y_i$.\n",
-    "\n",
-    "Empirical risk quantifies how well the model fits the given dataset, representing the average loss of the model on the training data.\n",
-    "\n",
-    "**Structural Risk(Objective Function):**\n",
-    "$$\n",
-    "R_{struc}(f) = R_{emp}(f) + \\lambda R_{reg}(\\theta)\n",
-    "$$\n",
-    "Where:\n",
-    "- $R_{reg}(\\theta)$ is the regularization term, penalizing the complexity of the model.\n",
-    "- $\\lambda$ is a regularization coefficient determining the weight of the regularization term relative to the empirical risk.\n",
-    "\n",
-    "Structural risk is a combination of the empirical risk and a penalty for model complexity. It strikes a balance between fitting the training data (empirical risk) and ensuring the model isn't overly complex (which can lead to overfitting).\n",
-    "\n",
-    "**Differences and Relations:**\n",
-    "\n",
-    "1. **Empirical Risk** focuses solely on minimizing the error on the training data without considering model complexity or how it generalizes to unseen data.\n",
-    "2. **Structural Risk** takes into account both the empirical risk and the complexity of the model. By introducing a regularization term, it ensures that the model doesn't become overly complex and overfit the training data. Thus, it balances performance on training data with generalization to new data.\n",
-    "\n",
-    "In essence, while empirical risk aims for performance on the current dataset, structural risk aims for good performance on new data by penalizing overly complex models.\n",
-    "\n",
-    "### Cost Function and Objective Function\n",
-    "\n",
-    "The empirical risk and cost functions are in many cases the same and represent the average loss on the training data.\n",
-    "\n",
-    "Structural risk is often viewed as an objective function, especially when regularization is considered. But the term \"objective function\" is broader and is not limited to structural risk but can also include other optimization objectives."
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -271,34 +180,14 @@
    "metadata": {},
    "source": [
     "### Classification Loss Functions\n",
-    "\n",
-    "1. **Cross Entropy Loss**\n",
-    "\n",
-    "$$\n",
-    "L(y, p) = - \\sum_{i=1}^{C} y_i \\log(p_i)\n",
-    "$$\n",
-    "\n",
-    "Where $y_i$ is the actual label (0 or 1) and $p_i$ is the predicted probability for the respective class."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "y_true = tf.constant([[0, 1], [1, 0], [1, 0]])\n",
-    "y_pred = tf.constant([[0.05, 0.95], [0.1, 0.9], [0.8, 0.2]])\n",
-    "loss = tf.keras.losses.CategoricalCrossentropy()(y_true, y_pred)\n",
-    "\n",
-    "print(loss.numpy())"
+    "\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "2. **Hinge Loss**\n",
+    "1. **Hinge Loss**\n",
     "\n",
     "$$\n",
     "L(y, \\hat{y}) = \\max(0, 1 - y \\cdot \\hat{y})\n",
@@ -320,205 +209,6 @@
     "print(loss.numpy())"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "3. **Binary Cross Entropy(Log Loss)**\n",
-    "\n",
-    "Mathematically, it is the preferred loss function under the inference framework of maximum likelihood. It is the loss function to be evaluated first and only changed if you have a good reason.\n",
-    "\n",
-    "Cross-entropy will calculate a score that summarizes the average difference between the actual and predicted probability distributions for predicting class 1. The score is minimized and a perfect cross-entropy value is 0.\n",
-    "\n",
-    "This YouTube video by Andrew Ng explains very well Binary Cross Entropy Loss (make sure that you have access to YouTube for this web page to render correctly):"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from IPython.display import HTML\n",
-    "\n",
-    "display(HTML(\n",
-    "  \"\"\"\n",
-    "    <iframe src=\"https://www.youtube.com/embed/SHEPb1JHw5o\" allowfullscreen></iframe>\n",
-    "  \"\"\"\n",
-    "))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "y_true = tf.constant([0, 1, 0])\n",
-    "y_pred = tf.constant([0.05, 0.95, 0.1])\n",
-    "loss = tf.keras.losses.BinaryCrossentropy()(y_true, y_pred)\n",
-    "\n",
-    "print(loss.numpy())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "4. **Multi-Class Cross-Entropy Loss**\n",
-    "\n",
-    "Mathematically, it is the preferred loss function under the inference framework of maximum likelihood. It is the loss function to be evaluated first and only changed if you have a good reason.\n",
-    "\n",
-    "Cross-entropy will calculate a score that summarizes the average difference between the actual and predicted probability distributions for all classes in the problem. The score is minimized and a perfect cross-entropy value is 0."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "y_true = [[1, 0, 0],\n",
-    "          [0, 1, 0],\n",
-    "          [0, 0, 1],\n",
-    "          [1, 0, 0],\n",
-    "          [0, 1, 0]]\n",
-    "\n",
-    "# Mock predicted probabilities from a model\n",
-    "y_pred = [[0.7, 0.2, 0.1],\n",
-    "          [0.2, 0.5, 0.3],\n",
-    "          [0.1, 0.2, 0.7],\n",
-    "          [0.6, 0.3, 0.1],\n",
-    "          [0.1, 0.6, 0.3]]\n",
-    "\n",
-    "y_true = tf.constant(y_true, dtype=tf.float32)\n",
-    "y_pred = tf.constant(y_pred, dtype=tf.float32)\n",
-    "\n",
-    "loss = tf.reduce_mean(-tf.reduce_sum(y_true * tf.math.log(y_pred), axis=1))\n",
-    "\n",
-    "print(\"Multi-Class Cross-Entropy Loss:\", loss.numpy())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Structured Loss Functions\n",
-    "\n",
-    "1. **CTC Loss (Connectionist Temporal Classification)**\n",
-    "\n",
-    "Used for sequence-to-sequence problems, like speech recognition."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "\n",
-    "y_true = np.array([[1, 2]])  # (batch, timesteps)\n",
-    "y_pred = np.array([[[0.1, 0.6, 0.3], [0.3, 0.1, 0.6]]])  # (batch, timesteps, num_classes)\n",
-    "logit_length = [2]\n",
-    "label_length = [2]\n",
-    "loss = tf.keras.backend.ctc_batch_cost(y_true, y_pred, logit_length, label_length)\n",
-    "\n",
-    "print(loss.numpy())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "2. **Dice Loss, IoU Loss**\n",
-    "\n",
-    "Used for image segmentation tasks."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def dice_loss(y_true, y_pred):\n",
-    "    numerator = 2 * tf.reduce_sum(y_true * y_pred, axis=-1)\n",
-    "    denominator = tf.reduce_sum(y_true + y_pred, axis=-1)\n",
-    "    return 1 - (numerator + 1) / (denominator + 1)\n",
-    "\n",
-    "y_true = tf.constant([[1, 0, 1], [0, 1, 0]])\n",
-    "y_pred = tf.constant([[0.8, 0.2, 0.6], [0.3, 0.7, 0.1]])\n",
-    "loss = dice_loss(y_true, y_pred)\n",
-    "\n",
-    "print(loss.numpy())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def iou_loss(y_true, y_pred):\n",
-    "    intersection = tf.reduce_sum(y_true * y_pred, axis=[1, 2, 3])\n",
-    "    union = tf.reduce_sum(y_true, axis=[1, 2, 3]) + tf.reduce_sum(y_pred, axis=[1, 2, 3]) - intersection\n",
-    "    return 1. - (intersection + 1) / (union + 1)\n",
-    "\n",
-    "# For simplicity, using 2D tensors. Typically, these are images (3D tensors).\n",
-    "y_true = tf.constant([[1, 0, 1], [0, 1, 0]])\n",
-    "y_pred = tf.constant([[0.8, 0.2, 0.6], [0.3, 0.7, 0.1]])\n",
-    "loss = iou_loss(y_true[tf.newaxis, ...], y_pred[tf.newaxis, ...])  # Add batch dimension\n",
-    "\n",
-    "print(loss.numpy())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Regularization\n",
-    "\n",
-    "1. **L1 Regularization (Lasso)**\n",
-    "\n",
-    "Produces sparse model parameters."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from keras.regularizers import l1\n",
-    "\n",
-    "model = tf.keras.models.Sequential([\n",
-    "    tf.keras.layers.Dense(64, activation='relu', kernel_regularizer=l1(0.01), input_shape=(10,))\n",
-    "])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "2. **L2 Regularization (Ridge)**\n",
-    "\n",
-    "Prevents model parameters from becoming too large but doesn't force them to become exactly zero."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from keras.regularizers import l2\n",
-    "\n",
-    "model = tf.keras.models.Sequential([\n",
-    "    tf.keras.layers.Dense(64, activation='relu', kernel_regularizer=l2(0.01), input_shape=(10,))\n",
-    "])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
diff --git a/_sources/assignments/ml-fundamentals/parameter-play.ipynb b/_sources/assignments/ml-fundamentals/parameter-play.ipynb
index b04c71b96d..f9901629d1 100644
--- a/_sources/assignments/ml-fundamentals/parameter-play.ipynb
+++ b/_sources/assignments/ml-fundamentals/parameter-play.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "77832121",
+   "id": "c4f4e7c1",
    "metadata": {},
    "source": [
     "# Parameter play\n",
diff --git a/_sources/assignments/ml-fundamentals/regression-with-scikit-learn.ipynb b/_sources/assignments/ml-fundamentals/regression-with-scikit-learn.ipynb
index 4113d54808..69b059d9ad 100644
--- a/_sources/assignments/ml-fundamentals/regression-with-scikit-learn.ipynb
+++ b/_sources/assignments/ml-fundamentals/regression-with-scikit-learn.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "304850dd",
+   "id": "be4b65ba",
    "metadata": {},
    "source": [
     "# Regression with Scikit-learn\n",
diff --git a/_sources/assignments/ml-fundamentals/retrying-some-regression.ipynb b/_sources/assignments/ml-fundamentals/retrying-some-regression.ipynb
index bbff63991c..da1cb3d3f6 100644
--- a/_sources/assignments/ml-fundamentals/retrying-some-regression.ipynb
+++ b/_sources/assignments/ml-fundamentals/retrying-some-regression.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "7798e2d4",
+   "id": "46278fc3",
    "metadata": {},
    "source": [
     "# Retrying some regression\n",
diff --git a/_sources/data-science/data-science-in-the-wild.ipynb b/_sources/data-science/data-science-in-the-wild.ipynb
index 6a1f23755f..2b44b0528b 100644
--- a/_sources/data-science/data-science-in-the-wild.ipynb
+++ b/_sources/data-science/data-science-in-the-wild.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "4711b4dd",
+   "id": "c32a0c5d",
    "metadata": {},
    "source": [
     "# Data Science in the real world\n",
diff --git a/_sources/data-science/data-science-lifecycle/analyzing.ipynb b/_sources/data-science/data-science-lifecycle/analyzing.ipynb
index c45d86aa10..59c15936b5 100644
--- a/_sources/data-science/data-science-lifecycle/analyzing.ipynb
+++ b/_sources/data-science/data-science-lifecycle/analyzing.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "9f8e437d",
+   "id": "d0d48596",
    "metadata": {},
    "source": [
     "# Analyzing\n",
diff --git a/_sources/data-science/data-science-lifecycle/communication.ipynb b/_sources/data-science/data-science-lifecycle/communication.ipynb
index 1ea8616853..b888232a70 100644
--- a/_sources/data-science/data-science-lifecycle/communication.ipynb
+++ b/_sources/data-science/data-science-lifecycle/communication.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "74fb713d",
+   "id": "b5a78097",
    "metadata": {},
    "source": [
     "# Communication\n",
diff --git a/_sources/data-science/data-science-lifecycle/data-science-lifecycle.ipynb b/_sources/data-science/data-science-lifecycle/data-science-lifecycle.ipynb
index 545b88d296..fac1fc5c10 100644
--- a/_sources/data-science/data-science-lifecycle/data-science-lifecycle.ipynb
+++ b/_sources/data-science/data-science-lifecycle/data-science-lifecycle.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "97a4e306",
+   "id": "6b16932a",
    "metadata": {},
    "source": [
     "# Data Science lifecycle\n",
diff --git a/_sources/data-science/data-science-lifecycle/introduction.ipynb b/_sources/data-science/data-science-lifecycle/introduction.ipynb
index a47666df39..182a9afdaf 100644
--- a/_sources/data-science/data-science-lifecycle/introduction.ipynb
+++ b/_sources/data-science/data-science-lifecycle/introduction.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "bb0c4c27",
+   "id": "fedb6c3e",
    "metadata": {},
    "source": [
     "# Introduction to the Data Science lifecycle\n",
diff --git a/_sources/data-science/working-with-data/data-preparation.ipynb b/_sources/data-science/working-with-data/data-preparation.ipynb
index 3466d07656..1d7c650a0b 100644
--- a/_sources/data-science/working-with-data/data-preparation.ipynb
+++ b/_sources/data-science/working-with-data/data-preparation.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "09851747",
+   "id": "21daa8ac",
    "metadata": {},
    "source": [
     "# Data preparation\n",
@@ -36,7 +36,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "38e2bb96",
+   "id": "16500be0",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -49,7 +49,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b201151b",
+   "id": "64b3fc70",
    "metadata": {},
    "source": [
     "|                                        |sepal length (cm)|sepal width (cm)|petal length (cm)|petal width (cm)|\n",
@@ -66,7 +66,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "77406b1b",
+   "id": "6b41c90b",
    "metadata": {},
    "outputs": [
     {
@@ -93,7 +93,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d0a6b8d5",
+   "id": "543fc221",
    "metadata": {},
    "source": [
     "From this, we know that the *Iris* dataset has 150 entries in four columns with no null entries. All of the data is stored as 64-bit floating-point numbers.\n",
@@ -104,7 +104,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "b6a1c99c",
+   "id": "b87fe8e4",
    "metadata": {},
    "outputs": [
     {
@@ -194,7 +194,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e222c9ef",
+   "id": "aedb888e",
    "metadata": {},
    "source": [
     "**`DataFrame.tail()`**: Conversely, to check the last few rows of the `DataFrame`, we use the `tail()` method:"
@@ -203,7 +203,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "8ba627d6",
+   "id": "71fca0c9",
    "metadata": {},
    "outputs": [
     {
@@ -293,7 +293,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f2054712",
+   "id": "2e2c75e8",
    "metadata": {},
    "source": [
     "```{note}\n",
@@ -320,7 +320,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "2435e012",
+   "id": "81fb278e",
    "metadata": {},
    "outputs": [
     {
@@ -347,7 +347,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "305f61ef",
+   "id": "f08ff137",
    "metadata": {},
    "source": [
     "Look closely at the output. Does any of it surprise you? While `0` is an arithmetic null, it's nevertheless a perfectly good integer and pandas treats it as such. `''` is a little more subtle. While we used it in Section 1 to represent an empty string value, it is nevertheless a string object and not a representation of null as far as pandas is concerned.\n",
@@ -364,7 +364,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "69ec9897",
+   "id": "8361e2db",
    "metadata": {},
    "outputs": [
     {
@@ -387,7 +387,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8070079f",
+   "id": "ab1e1bab",
    "metadata": {},
    "source": [
     "Note that this should look like your output from `example3[example3.notnull()]`. The difference here is that, rather than just indexing on the masked values, `dropna` has removed those missing values from the `Series` `example1`.\n",
@@ -398,7 +398,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "aaa37f62",
+   "id": "d11e19d7",
    "metadata": {},
    "outputs": [
     {
@@ -471,7 +471,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "858462b7",
+   "id": "37e2f592",
    "metadata": {},
    "source": [
     "|      | 0 | 1 | 2 |\n",
@@ -488,7 +488,7 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "5af424df",
+   "id": "5c6a37d8",
    "metadata": {},
    "outputs": [
     {
@@ -544,7 +544,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "762af56b",
+   "id": "3969ef65",
    "metadata": {},
    "source": [
     "If necessary, you can drop NA values from columns. Use `axis=1` to do so:"
@@ -553,7 +553,7 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "8405d655",
+   "id": "76785e9e",
    "metadata": {},
    "outputs": [
     {
@@ -615,7 +615,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "47d24051",
+   "id": "c8e3ae25",
    "metadata": {},
    "source": [
     "Notice that this can drop a lot of data that you might want to keep, particularly in smaller datasets. What if you just want to drop rows or columns that contain several or even just all null values? You specify those setting in `dropna` with the `how` and `thresh` parameters.\n",
@@ -626,7 +626,7 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "f2f3fe5c",
+   "id": "d25d8cef",
    "metadata": {},
    "outputs": [
     {
@@ -701,7 +701,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fda7d3dd",
+   "id": "2c8b3f9c",
    "metadata": {},
    "source": [
     "|      |0  |1  |2  |3  |\n",
@@ -716,7 +716,7 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "id": "e5370e2c",
+   "id": "371c6912",
    "metadata": {},
    "outputs": [
     {
@@ -774,7 +774,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1956fcb4",
+   "id": "c4670e87",
    "metadata": {},
    "source": [
     "Here, the first and last rows have been dropped, because they contain only two non-null values.\n",
@@ -785,7 +785,7 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "id": "39f47e18",
+   "id": "88ac8026",
    "metadata": {},
    "outputs": [
     {
@@ -811,7 +811,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5c82bfa0",
+   "id": "f60e5bc1",
    "metadata": {},
    "source": [
     "You can fill all of the null entries with a single value, such as `0`:"
@@ -820,7 +820,7 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "id": "e7f5acc0",
+   "id": "a5fb5460",
    "metadata": {},
    "outputs": [
     {
@@ -845,7 +845,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e43ba12b",
+   "id": "6aaf28b7",
    "metadata": {},
    "source": [
     "You can **forward-fill** null values, which is to use the last valid value to fill a null:"
@@ -854,7 +854,7 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "id": "ae2b0a45",
+   "id": "15f3cd14",
    "metadata": {},
    "outputs": [
     {
@@ -879,7 +879,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a969d708",
+   "id": "f8fb5572",
    "metadata": {},
    "source": [
     "You can also **back-fill** to propagate the next valid value backward to fill a null:"
@@ -888,7 +888,7 @@
   {
    "cell_type": "code",
    "execution_count": 15,
-   "id": "4f4f80c3",
+   "id": "2a8d2da3",
    "metadata": {},
    "outputs": [
     {
@@ -913,7 +913,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4e6715d4",
+   "id": "ce05fef8",
    "metadata": {},
    "source": [
     "As you might guess, this works the same with `DataFrame`s, but you can also specify an `axis` along which to fill null values. taking the previously used `example2` again:"
@@ -922,7 +922,7 @@
   {
    "cell_type": "code",
    "execution_count": 16,
-   "id": "47d64ae3",
+   "id": "bf109926",
    "metadata": {},
    "outputs": [
     {
@@ -996,7 +996,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a4958cb1",
+   "id": "cd45335d",
    "metadata": {},
    "source": [
     "Notice that when a previous value is not available for forward-filling, the null value remains.\n",
@@ -1019,7 +1019,7 @@
   {
    "cell_type": "code",
    "execution_count": 17,
-   "id": "e08a6c74",
+   "id": "2e9225db",
    "metadata": {},
    "outputs": [
     {
@@ -1099,7 +1099,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4488d9d7",
+   "id": "d9e30710",
    "metadata": {},
    "source": [
     "|      |letters|numbers|\n",
@@ -1114,7 +1114,7 @@
   {
    "cell_type": "code",
    "execution_count": 18,
-   "id": "1c1c523a",
+   "id": "851d33b1",
    "metadata": {},
    "outputs": [
     {
@@ -1139,7 +1139,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "290bf846",
+   "id": "e66e3835",
    "metadata": {},
    "source": [
     "**Dropping duplicates: `drop_duplicates`:** simply returns a copy of the data for which all of the `duplicated` values are `False`:"
@@ -1148,7 +1148,7 @@
   {
    "cell_type": "code",
    "execution_count": 19,
-   "id": "328c28ac",
+   "id": "4f6253a5",
    "metadata": {},
    "outputs": [
     {
@@ -1214,7 +1214,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "436a1bd9",
+   "id": "d11a81f5",
    "metadata": {},
    "source": [
     "Both `duplicated` and `drop_duplicates` default to consider all columns but you can specify that they examine only a subset of columns in your `DataFrame`:"
@@ -1223,7 +1223,7 @@
   {
    "cell_type": "code",
    "execution_count": 20,
-   "id": "2638ef59",
+   "id": "e46cee94",
    "metadata": {},
    "outputs": [
     {
@@ -1283,7 +1283,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f282d259",
+   "id": "28d1210c",
    "metadata": {},
    "source": [
     "```{note}\n",
diff --git a/_sources/data-science/working-with-data/numpy.ipynb b/_sources/data-science/working-with-data/numpy.ipynb
index bccbf7605c..7e3415eb5f 100644
--- a/_sources/data-science/working-with-data/numpy.ipynb
+++ b/_sources/data-science/working-with-data/numpy.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "f7cab3d2",
+   "id": "26df044f",
    "metadata": {},
    "source": [
     "# NumPy\n",
@@ -19,7 +19,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "6f7c517f",
+   "id": "639f7e9c",
    "metadata": {},
    "outputs": [
     {
@@ -40,7 +40,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0e86be5b",
+   "id": "58c3425f",
    "metadata": {},
    "source": [
     "### Create a basic array\n",
@@ -53,7 +53,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "6e939e45",
+   "id": "67a8279a",
    "metadata": {},
    "outputs": [
     {
@@ -75,7 +75,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2afea2c2",
+   "id": "1eb6110e",
    "metadata": {},
    "source": [
     "Besides creating an array from a sequence of elements, you can easily create an array filled with `0`’s:"
@@ -84,7 +84,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "c675041c",
+   "id": "9f2e614a",
    "metadata": {},
    "outputs": [
     {
@@ -104,7 +104,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "eca650ce",
+   "id": "33c11942",
    "metadata": {},
    "source": [
     "Or an array filled with 1’s:"
@@ -113,7 +113,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "5a98112d",
+   "id": "013a4d32",
    "metadata": {},
    "outputs": [
     {
@@ -133,7 +133,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "31f9eab9",
+   "id": "e06dd41e",
    "metadata": {},
    "source": [
     "Or even an empty array! The function `empty` creates an array whose initial content is random and depends on the state of the memory. The reason to use `empty` over `zeros` (or something similar) is speed - just make sure to fill every element afterwards!"
@@ -142,7 +142,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "0de9b575",
+   "id": "320cff03",
    "metadata": {},
    "outputs": [
     {
@@ -162,7 +162,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8e309a63",
+   "id": "6b88c494",
    "metadata": {},
    "source": [
     "You can create an array with a range of elements:"
@@ -171,7 +171,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "6c2e5e51",
+   "id": "1a2ed2bd",
    "metadata": {},
    "outputs": [
     {
@@ -191,7 +191,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "eb1226c8",
+   "id": "421ef3f3",
    "metadata": {},
    "source": [
     "And even an array that contains a range of evenly spaced intervals. To do this, you will specify the **first number**, **last number**, and the **step size**."
@@ -200,7 +200,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "20a297e1",
+   "id": "348232bf",
    "metadata": {},
    "outputs": [
     {
@@ -220,7 +220,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7b5c110c",
+   "id": "2ab9b967",
    "metadata": {},
    "source": [
     "You can also use `np.linspace()` to create an array with values that are spaced linearly in a specified interval:"
@@ -229,7 +229,7 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "28e1bc9f",
+   "id": "726ba671",
    "metadata": {},
    "outputs": [
     {
@@ -249,7 +249,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b8b50ace",
+   "id": "bb24eef2",
    "metadata": {},
    "source": [
     "While the default data type is floating point (`np.float64`), you can explicitly specify which data type you want using the `dtype` keyword."
@@ -258,7 +258,7 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "68ae3d79",
+   "id": "a56da42a",
    "metadata": {},
    "outputs": [
     {
@@ -278,7 +278,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2ef3f3a0",
+   "id": "3ab57724",
    "metadata": {},
    "source": [
     "### Adding, removing, and sorting elements\n",
@@ -291,7 +291,7 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "427a2549",
+   "id": "68c60214",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -300,7 +300,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0c27e861",
+   "id": "dd36db00",
    "metadata": {},
    "source": [
     "You can quickly sort the numbers in ascending order with:"
@@ -309,7 +309,7 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "id": "30d691db",
+   "id": "3b445999",
    "metadata": {},
    "outputs": [
     {
@@ -329,7 +329,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6eb422f9",
+   "id": "6ae4729f",
    "metadata": {},
    "source": [
     "In addition to sort, which returns a sorted copy of an array, you can use:\n",
@@ -345,7 +345,7 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "id": "7fbbcd5c",
+   "id": "89e299c5",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -355,7 +355,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7cc16126",
+   "id": "3d7be007",
    "metadata": {},
    "source": [
     "You can concatenate them with `np.concatenate()`."
@@ -364,7 +364,7 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "id": "89e86081",
+   "id": "204b53fe",
    "metadata": {},
    "outputs": [
     {
@@ -384,7 +384,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "243f380f",
+   "id": "4d2ed678",
    "metadata": {},
    "source": [
     "Or, if you start with these arrays:"
@@ -393,7 +393,7 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "id": "e3a0d001",
+   "id": "27e18841",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -403,7 +403,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1dbcd3bb",
+   "id": "94de63b3",
    "metadata": {},
    "source": [
     "You can concatenate them with:"
@@ -412,7 +412,7 @@
   {
    "cell_type": "code",
    "execution_count": 15,
-   "id": "e2230971",
+   "id": "e2d73dd8",
    "metadata": {},
    "outputs": [
     {
@@ -434,7 +434,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9635d315",
+   "id": "f0732cdf",
    "metadata": {},
    "source": [
     "In order to remove elements from an array, it’s simple to use indexing to select the elements that you want to keep.\n",
@@ -450,7 +450,7 @@
   {
    "cell_type": "code",
    "execution_count": 16,
-   "id": "afd751ab",
+   "id": "c4b07961",
    "metadata": {},
    "outputs": [
     {
@@ -475,7 +475,7 @@
   {
    "cell_type": "code",
    "execution_count": 17,
-   "id": "ab067237",
+   "id": "256397e6",
    "metadata": {},
    "outputs": [
     {
@@ -495,7 +495,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a5847d23",
+   "id": "dc18dd33",
    "metadata": {},
    "source": [
     "- ndarray.shape\n",
@@ -505,7 +505,7 @@
   {
    "cell_type": "code",
    "execution_count": 18,
-   "id": "08566ba6",
+   "id": "77dc06bc",
    "metadata": {},
    "outputs": [
     {
@@ -525,7 +525,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9c082db2",
+   "id": "2a12dfb6",
    "metadata": {},
    "source": [
     "- ndarray.size\n",
@@ -535,7 +535,7 @@
   {
    "cell_type": "code",
    "execution_count": 19,
-   "id": "d6777ead",
+   "id": "bf368775",
    "metadata": {},
    "outputs": [
     {
@@ -555,7 +555,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e31d3983",
+   "id": "3d62ef04",
    "metadata": {},
    "source": [
     "- ndarray.dtype\n",
@@ -565,7 +565,7 @@
   {
    "cell_type": "code",
    "execution_count": 20,
-   "id": "e4f9349e",
+   "id": "f00abe42",
    "metadata": {},
    "outputs": [
     {
@@ -586,7 +586,7 @@
   {
    "cell_type": "code",
    "execution_count": 21,
-   "id": "50441d36",
+   "id": "b5504fbd",
    "metadata": {},
    "outputs": [
     {
@@ -606,7 +606,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b9a4e3df",
+   "id": "ad5f7ff9",
    "metadata": {},
    "source": [
     "- ndarray.itemsize\n",
@@ -616,7 +616,7 @@
   {
    "cell_type": "code",
    "execution_count": 22,
-   "id": "bc91818e",
+   "id": "f26d8693",
    "metadata": {},
    "outputs": [
     {
@@ -636,7 +636,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6001b4e7",
+   "id": "5b83c510",
    "metadata": {},
    "source": [
     "- ndarray.data\n",
@@ -646,13 +646,13 @@
   {
    "cell_type": "code",
    "execution_count": 23,
-   "id": "550fcac2",
+   "id": "e9c1f03b",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<memory at 0x7f556a5e1ad0>"
+       "<memory at 0x7f258349da00>"
       ]
      },
      "execution_count": 23,
@@ -666,7 +666,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9e7c4466",
+   "id": "0ba8edc5",
    "metadata": {},
    "source": [
     "### Reshape an array\n",
@@ -679,7 +679,7 @@
   {
    "cell_type": "code",
    "execution_count": 24,
-   "id": "3b635c98",
+   "id": "652d07a5",
    "metadata": {},
    "outputs": [
     {
@@ -700,7 +700,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "edd82c34",
+   "id": "757b63fc",
    "metadata": {},
    "source": [
     "You can use `reshape()` to reshape your array. For example, you can reshape this array to an array with three rows and two columns:"
@@ -709,7 +709,7 @@
   {
    "cell_type": "code",
    "execution_count": 25,
-   "id": "70a82cec",
+   "id": "a157fc71",
    "metadata": {},
    "outputs": [
     {
@@ -732,7 +732,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c97b8591",
+   "id": "475d7643",
    "metadata": {},
    "source": [
     "With `np.reshape`, you can specify a few optional parameters:"
@@ -741,7 +741,7 @@
   {
    "cell_type": "code",
    "execution_count": 26,
-   "id": "e3394a1f",
+   "id": "cbe5f4e1",
    "metadata": {},
    "outputs": [
     {
@@ -761,7 +761,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d3b4a6fa",
+   "id": "a118cd19",
    "metadata": {},
    "source": [
     "`a` is the array to be reshaped.\n",
@@ -782,7 +782,7 @@
   {
    "cell_type": "code",
    "execution_count": 27,
-   "id": "067a7a2d",
+   "id": "f34cf4c0",
    "metadata": {},
    "outputs": [
     {
@@ -803,7 +803,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f988b32e",
+   "id": "dc77089a",
    "metadata": {},
    "source": [
     "You can use `np.newaxis` to add a new axis:"
@@ -812,7 +812,7 @@
   {
    "cell_type": "code",
    "execution_count": 28,
-   "id": "4896f3e9",
+   "id": "61c3e030",
    "metadata": {},
    "outputs": [
     {
@@ -833,7 +833,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "60466c47",
+   "id": "5a32c35b",
    "metadata": {},
    "source": [
     "You can explicitly convert a 1D array with either a row vector or a column vector using `np.newaxis`. For example, you can convert a 1D array to a row vector by inserting an axis along the first dimension:"
@@ -842,7 +842,7 @@
   {
    "cell_type": "code",
    "execution_count": 29,
-   "id": "3bdb4dc9",
+   "id": "ce884611",
    "metadata": {},
    "outputs": [
     {
@@ -863,7 +863,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "682ec8fd",
+   "id": "e97dd5b8",
    "metadata": {},
    "source": [
     "Or, for a column vector, you can insert an axis along the second dimension:"
@@ -872,7 +872,7 @@
   {
    "cell_type": "code",
    "execution_count": 30,
-   "id": "6e2e6dcc",
+   "id": "43528ccc",
    "metadata": {},
    "outputs": [
     {
@@ -893,7 +893,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9a533818",
+   "id": "7f51fd26",
    "metadata": {},
    "source": [
     "You can also expand an array by inserting a new axis at a specified position with `np.expand_dims`.\n",
@@ -904,7 +904,7 @@
   {
    "cell_type": "code",
    "execution_count": 31,
-   "id": "f231c757",
+   "id": "0cadc802",
    "metadata": {},
    "outputs": [
     {
@@ -925,7 +925,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "dae2ba71",
+   "id": "a500dcf6",
    "metadata": {},
    "source": [
     "You can use np.expand_dims to add an axis at index position 1 with:"
@@ -934,7 +934,7 @@
   {
    "cell_type": "code",
    "execution_count": 32,
-   "id": "fa9a1754",
+   "id": "b38d7e48",
    "metadata": {},
    "outputs": [
     {
@@ -955,7 +955,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "61e387bd",
+   "id": "8f724e1d",
    "metadata": {},
    "source": [
     "You can add an axis at index position 0 with:"
@@ -964,7 +964,7 @@
   {
    "cell_type": "code",
    "execution_count": 33,
-   "id": "afa5e2ee",
+   "id": "76687b8f",
    "metadata": {},
    "outputs": [
     {
@@ -985,7 +985,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2c2b789e",
+   "id": "f82ab1ce",
    "metadata": {},
    "source": [
     "### Indexing and slicing\n",
@@ -996,7 +996,7 @@
   {
    "cell_type": "code",
    "execution_count": 34,
-   "id": "2b8d66d7",
+   "id": "f319d9ae",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1006,7 +1006,7 @@
   {
    "cell_type": "code",
    "execution_count": 35,
-   "id": "040987c4",
+   "id": "3e1b50c8",
    "metadata": {},
    "outputs": [
     {
@@ -1027,7 +1027,7 @@
   {
    "cell_type": "code",
    "execution_count": 36,
-   "id": "25f88150",
+   "id": "0eff095e",
    "metadata": {},
    "outputs": [
     {
@@ -1048,7 +1048,7 @@
   {
    "cell_type": "code",
    "execution_count": 37,
-   "id": "83acd0d4",
+   "id": "e83b0b8e",
    "metadata": {},
    "outputs": [
     {
@@ -1069,7 +1069,7 @@
   {
    "cell_type": "code",
    "execution_count": 38,
-   "id": "add70cd7",
+   "id": "e7cee670",
    "metadata": {},
    "outputs": [
     {
@@ -1089,7 +1089,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e2c6aaf2",
+   "id": "1da34809",
    "metadata": {},
    "source": [
     "You may want to take a section of your array or specific array elements to use in further analysis or additional operations. To do that, you’ll need to subset, slice, and/or index your arrays.\n",
@@ -1102,7 +1102,7 @@
   {
    "cell_type": "code",
    "execution_count": 39,
-   "id": "9a604b67",
+   "id": "bb1892ab",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1111,7 +1111,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7cbab311",
+   "id": "beb77fe9",
    "metadata": {},
    "source": [
     "You can easily print all of the values in the array that are less than 5."
@@ -1120,7 +1120,7 @@
   {
    "cell_type": "code",
    "execution_count": 40,
-   "id": "00776007",
+   "id": "7e705a02",
    "metadata": {},
    "outputs": [
     {
@@ -1140,7 +1140,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "eeddf726",
+   "id": "18704444",
    "metadata": {},
    "source": [
     "You can also select, for example, numbers that are equal to or greater than 5, and use that condition to index an array."
@@ -1149,7 +1149,7 @@
   {
    "cell_type": "code",
    "execution_count": 41,
-   "id": "e64cd122",
+   "id": "6d575ea6",
    "metadata": {},
    "outputs": [
     {
@@ -1170,7 +1170,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c512e667",
+   "id": "9b58de66",
    "metadata": {},
    "source": [
     "You can select elements that are divisible by 2:"
@@ -1179,7 +1179,7 @@
   {
    "cell_type": "code",
    "execution_count": 42,
-   "id": "2d394b78",
+   "id": "26718b28",
    "metadata": {},
    "outputs": [
     {
@@ -1200,7 +1200,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d419ba95",
+   "id": "66545416",
    "metadata": {},
    "source": [
     "Or you can select elements that satisfy two conditions using the `&` and `|` operators:"
@@ -1209,7 +1209,7 @@
   {
    "cell_type": "code",
    "execution_count": 43,
-   "id": "7c8a86d0",
+   "id": "0ecca13b",
    "metadata": {},
    "outputs": [
     {
@@ -1230,7 +1230,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "16846ba4",
+   "id": "a208682c",
    "metadata": {},
    "source": [
     "You can also make use of the logical operators `&` and `|` in order to return boolean values that specify whether or not the values in an array fulfill a certain condition. This can be useful with arrays that contain names or other categorical values."
@@ -1239,7 +1239,7 @@
   {
    "cell_type": "code",
    "execution_count": 44,
-   "id": "f9b179f8",
+   "id": "d485f8cb",
    "metadata": {},
    "outputs": [
     {
@@ -1262,7 +1262,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a5f1f1c0",
+   "id": "183145c5",
    "metadata": {},
    "source": [
     "You can also use `np.nonzero()` to select elements or indices from an array.\n",
@@ -1273,7 +1273,7 @@
   {
    "cell_type": "code",
    "execution_count": 45,
-   "id": "ee7506d5",
+   "id": "e3bc0fce",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1282,7 +1282,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "399658e4",
+   "id": "720f93cd",
    "metadata": {},
    "source": [
     "You can use `np.nonzero()` to print the indices of elements that are, for example, less than 5:"
@@ -1291,7 +1291,7 @@
   {
    "cell_type": "code",
    "execution_count": 46,
-   "id": "f7cf6627",
+   "id": "9aa61cae",
    "metadata": {},
    "outputs": [
     {
@@ -1312,7 +1312,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "084590fc",
+   "id": "53405376",
    "metadata": {},
    "source": [
     "In this example, a tuple of arrays was returned: one for each dimension. The first array represents the row indices where these values are found, and the second array represents the column indices where the values are found.\n",
@@ -1323,7 +1323,7 @@
   {
    "cell_type": "code",
    "execution_count": 47,
-   "id": "aceb6426",
+   "id": "31099d2e",
    "metadata": {},
    "outputs": [
     {
@@ -1345,7 +1345,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e51358aa",
+   "id": "514076ac",
    "metadata": {},
    "source": [
     "You can also use `np.nonzero()` to print the elements in an array that are less than 5 with:"
@@ -1354,7 +1354,7 @@
   {
    "cell_type": "code",
    "execution_count": 48,
-   "id": "7b98ca49",
+   "id": "802d7f0e",
    "metadata": {},
    "outputs": [
     {
@@ -1374,7 +1374,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6f97c2d9",
+   "id": "7d7ab3f2",
    "metadata": {},
    "source": [
     "If the element you’re looking for doesn’t exist in the array, then the returned array of indices will be empty. For example:"
@@ -1383,7 +1383,7 @@
   {
    "cell_type": "code",
    "execution_count": 49,
-   "id": "2d55de2f",
+   "id": "00a394e1",
    "metadata": {},
    "outputs": [
     {
@@ -1404,7 +1404,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e061db46",
+   "id": "72131317",
    "metadata": {},
    "source": [
     "### Create an array from existing data\n",
@@ -1417,7 +1417,7 @@
   {
    "cell_type": "code",
    "execution_count": 50,
-   "id": "311d5d3a",
+   "id": "fe21870f",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1426,7 +1426,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d9435eaf",
+   "id": "62e2f1fe",
    "metadata": {},
    "source": [
     "You can create a new array from a section of your array any time by specifying where you want to slice your array."
@@ -1435,7 +1435,7 @@
   {
    "cell_type": "code",
    "execution_count": 51,
-   "id": "a8a9081f",
+   "id": "feb14035",
    "metadata": {},
    "outputs": [
     {
@@ -1456,7 +1456,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "33bacf86",
+   "id": "e7ecf728",
    "metadata": {},
    "source": [
     "Here, you grabbed a section of your array from index position 3 through index position 8.\n",
@@ -1467,7 +1467,7 @@
   {
    "cell_type": "code",
    "execution_count": 52,
-   "id": "8d0bf3a7",
+   "id": "6f3e926d",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1479,7 +1479,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "913ae369",
+   "id": "fb7c6e4f",
    "metadata": {},
    "source": [
     "You can stack them vertically with `vstack`:"
@@ -1488,7 +1488,7 @@
   {
    "cell_type": "code",
    "execution_count": 53,
-   "id": "a57f1398",
+   "id": "937ec97f",
    "metadata": {},
    "outputs": [
     {
@@ -1511,7 +1511,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c93a3213",
+   "id": "89d736b6",
    "metadata": {},
    "source": [
     "Or stack them horizontally with hstack:"
@@ -1520,7 +1520,7 @@
   {
    "cell_type": "code",
    "execution_count": 54,
-   "id": "3e483055",
+   "id": "f7150024",
    "metadata": {},
    "outputs": [
     {
@@ -1541,7 +1541,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "122119f3",
+   "id": "e6205d9d",
    "metadata": {},
    "source": [
     "You can split an array into several smaller arrays using `hsplit`. You can specify either the number of equally shaped arrays to return or the columns after which the division should occur.\n",
@@ -1552,7 +1552,7 @@
   {
    "cell_type": "code",
    "execution_count": 55,
-   "id": "47bb86da",
+   "id": "c29fe1cb",
    "metadata": {},
    "outputs": [
     {
@@ -1574,7 +1574,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9d15976b",
+   "id": "a3633f0b",
    "metadata": {},
    "source": [
     "If you wanted to split this array into three equally shaped arrays, you would run:"
@@ -1583,7 +1583,7 @@
   {
    "cell_type": "code",
    "execution_count": 56,
-   "id": "4c8cf233",
+   "id": "4dc14ead",
    "metadata": {},
    "outputs": [
     {
@@ -1608,7 +1608,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e207d574",
+   "id": "cdcfce2f",
    "metadata": {},
    "source": [
     "If you wanted to split your array after the third and fourth column, you’d run:"
@@ -1617,7 +1617,7 @@
   {
    "cell_type": "code",
    "execution_count": 57,
-   "id": "0cf0f3b0",
+   "id": "a015db75",
    "metadata": {},
    "outputs": [
     {
@@ -1642,7 +1642,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "35e63b64",
+   "id": "3c353310",
    "metadata": {},
    "source": [
     "You can use the `view` method to create a new array object that looks at the same data as the original array (a shallow copy).\n",
@@ -1655,7 +1655,7 @@
   {
    "cell_type": "code",
    "execution_count": 58,
-   "id": "6fa2958b",
+   "id": "3907e2f0",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1664,7 +1664,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7eed264f",
+   "id": "66acf107",
    "metadata": {},
    "source": [
     "Now we create an array `b1` by slicing `a` and modify the first element of `b1`. This will modify the corresponding element in `a` as well!"
@@ -1673,7 +1673,7 @@
   {
    "cell_type": "code",
    "execution_count": 59,
-   "id": "97791347",
+   "id": "fa17b307",
    "metadata": {},
    "outputs": [
     {
@@ -1695,7 +1695,7 @@
   {
    "cell_type": "code",
    "execution_count": 60,
-   "id": "8a740070",
+   "id": "cb6e8bbf",
    "metadata": {},
    "outputs": [
     {
@@ -1717,7 +1717,7 @@
   {
    "cell_type": "code",
    "execution_count": 61,
-   "id": "aa1fb7c6",
+   "id": "3bd99881",
    "metadata": {},
    "outputs": [
     {
@@ -1739,7 +1739,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c702d31a",
+   "id": "29b22c76",
    "metadata": {},
    "source": [
     "Using the `copy` method will make a complete copy of the array and its data (a deep copy). To use this on your array, you could run:"
@@ -1748,7 +1748,7 @@
   {
    "cell_type": "code",
    "execution_count": 62,
-   "id": "59bcbef2",
+   "id": "8aa9494f",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1757,7 +1757,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3085413e",
+   "id": "98b56cf8",
    "metadata": {},
    "source": [
     "## Array operations\n",
@@ -1770,7 +1770,7 @@
   {
    "cell_type": "code",
    "execution_count": 63,
-   "id": "695ac3a0",
+   "id": "5d271f03",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1780,7 +1780,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2554cc86",
+   "id": "d394adbb",
    "metadata": {},
    "source": [
     "You can add the arrays together with the plus sign."
@@ -1789,7 +1789,7 @@
   {
    "cell_type": "code",
    "execution_count": 64,
-   "id": "6226d0de",
+   "id": "8589a275",
    "metadata": {},
    "outputs": [
     {
@@ -1809,7 +1809,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "494e35b2",
+   "id": "0258b325",
    "metadata": {},
    "source": [
     "You can, of course, do more than just addition!"
@@ -1818,7 +1818,7 @@
   {
    "cell_type": "code",
    "execution_count": 65,
-   "id": "31fe29d8",
+   "id": "e215cc4c",
    "metadata": {},
    "outputs": [
     {
@@ -1839,7 +1839,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "707e85aa",
+   "id": "2cd070df",
    "metadata": {},
    "source": [
     "Basic operations are simple with NumPy. If you want to find the sum of the elements in an array, you’d use `sum()`. This works for 1D arrays, 2D arrays, and arrays in higher dimensions."
@@ -1848,7 +1848,7 @@
   {
    "cell_type": "code",
    "execution_count": 66,
-   "id": "3fd4efa4",
+   "id": "e2b98e6b",
    "metadata": {},
    "outputs": [
     {
@@ -1869,7 +1869,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "428a46f8",
+   "id": "e9e4b2d1",
    "metadata": {},
    "source": [
     "To add the rows or the columns in a 2D array, you would specify the axis.\n",
@@ -1880,7 +1880,7 @@
   {
    "cell_type": "code",
    "execution_count": 67,
-   "id": "08182aa5",
+   "id": "1f6517bb",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1889,7 +1889,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f7d16cb5",
+   "id": "9a0afdb4",
    "metadata": {},
    "source": [
     "You can sum over the axis of rows with:"
@@ -1898,7 +1898,7 @@
   {
    "cell_type": "code",
    "execution_count": 68,
-   "id": "1c6723a8",
+   "id": "9ebfebfb",
    "metadata": {},
    "outputs": [
     {
@@ -1918,7 +1918,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6a2c2f26",
+   "id": "48d3cf45",
    "metadata": {},
    "source": [
     "You can sum over the axis of columns with:"
@@ -1927,7 +1927,7 @@
   {
    "cell_type": "code",
    "execution_count": 69,
-   "id": "0128ba3d",
+   "id": "c03d1d11",
    "metadata": {},
    "outputs": [
     {
@@ -1947,7 +1947,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a9c96f50",
+   "id": "da66d9dc",
    "metadata": {},
    "source": [
     "### Universal functions(ufunc)\n",
@@ -2038,7 +2038,7 @@
   {
    "cell_type": "code",
    "execution_count": 70,
-   "id": "ef248869",
+   "id": "9c5ec5fb",
    "metadata": {},
    "outputs": [
     {
@@ -2060,7 +2060,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5862d7c2",
+   "id": "e4650d70",
    "metadata": {},
    "source": [
     "NumPy’s broadcasting rule relaxes this constraint when the arrays’ shapes meet certain constraints. The simplest broadcasting example occurs when an array and a scalar value are combined in an operation:"
@@ -2069,7 +2069,7 @@
   {
    "cell_type": "code",
    "execution_count": 71,
-   "id": "39173cbb",
+   "id": "69d37f65",
    "metadata": {},
    "outputs": [
     {
@@ -2091,7 +2091,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "28c3594f",
+   "id": "363ade7d",
    "metadata": {},
    "source": [
     "The result is equivalent to the previous example where `b` was an array.  NumPy is smart enough to use the original scalar value without actually making copies so that broadcasting operations are as memory and computationally efficient as possible.\n",
@@ -2133,7 +2133,7 @@
   {
    "cell_type": "code",
    "execution_count": 72,
-   "id": "c628a1b5",
+   "id": "d42ca203",
    "metadata": {},
    "outputs": [
     {
@@ -2154,7 +2154,7 @@
   {
    "cell_type": "code",
    "execution_count": 73,
-   "id": "3b8afcfe",
+   "id": "17730997",
    "metadata": {},
    "outputs": [
     {
@@ -2175,7 +2175,7 @@
   {
    "cell_type": "code",
    "execution_count": 74,
-   "id": "e5ceffd7",
+   "id": "118b3a70",
    "metadata": {},
    "outputs": [
     {
@@ -2195,7 +2195,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f8e1ea6f",
+   "id": "0cc1a266",
    "metadata": {},
    "source": [
     "Let’s start with this array, called “a”."
@@ -2204,7 +2204,7 @@
   {
    "cell_type": "code",
    "execution_count": 75,
-   "id": "f11b6aab",
+   "id": "f5ff443c",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2215,7 +2215,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1c77e02d",
+   "id": "9853e0d5",
    "metadata": {},
    "source": [
     "It’s very common to want to aggregate along a row or column. By default, every NumPy aggregation function will return the aggregate of the entire array. To find the sum or the minimum of the elements in your array, run:"
@@ -2224,7 +2224,7 @@
   {
    "cell_type": "code",
    "execution_count": 76,
-   "id": "4672a4f7",
+   "id": "c6f4ca78",
    "metadata": {},
    "outputs": [
     {
@@ -2244,7 +2244,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6de9c70a",
+   "id": "ce7ec19c",
    "metadata": {},
    "source": [
     "Or:"
@@ -2253,7 +2253,7 @@
   {
    "cell_type": "code",
    "execution_count": 77,
-   "id": "15df4134",
+   "id": "de936b74",
    "metadata": {},
    "outputs": [
     {
@@ -2273,7 +2273,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6ac7fa2c",
+   "id": "07b818fa",
    "metadata": {},
    "source": [
     "You can specify on which axis you want the aggregation function to be computed. For example, you can find the minimum value within each column by specifying `axis=0`."
@@ -2282,7 +2282,7 @@
   {
    "cell_type": "code",
    "execution_count": 78,
-   "id": "9e8daa12",
+   "id": "a9919b51",
    "metadata": {},
    "outputs": [
     {
@@ -2302,7 +2302,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d6f0bfbd",
+   "id": "fdb24859",
    "metadata": {},
    "source": [
     "The four values listed above correspond to the number of columns in your array. With a four-column array, you will get four values as your result.\n",
@@ -2325,7 +2325,7 @@
   {
    "cell_type": "code",
    "execution_count": 79,
-   "id": "4450838a",
+   "id": "891f20eb",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2335,7 +2335,7 @@
   {
    "cell_type": "code",
    "execution_count": 80,
-   "id": "10190d22",
+   "id": "430f370c",
    "metadata": {},
    "outputs": [
     {
@@ -2356,7 +2356,7 @@
   {
    "cell_type": "code",
    "execution_count": 81,
-   "id": "1853031a",
+   "id": "c8f13aaa",
    "metadata": {},
    "outputs": [
     {
@@ -2376,7 +2376,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4c2c3f7e",
+   "id": "78b23e98",
    "metadata": {},
    "source": [
     "It is not necessary to separate each dimension’s index into its own set of square brackets."
@@ -2385,7 +2385,7 @@
   {
    "cell_type": "code",
    "execution_count": 82,
-   "id": "d02e2655",
+   "id": "5c473d42",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2395,7 +2395,7 @@
   {
    "cell_type": "code",
    "execution_count": 83,
-   "id": "90cc832e",
+   "id": "c2371626",
    "metadata": {},
    "outputs": [
     {
@@ -2416,7 +2416,7 @@
   {
    "cell_type": "code",
    "execution_count": 84,
-   "id": "a10c38da",
+   "id": "8440c8d4",
    "metadata": {},
    "outputs": [
     {
@@ -2436,7 +2436,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f4f08161",
+   "id": "cbd77d4e",
    "metadata": {},
    "source": [
     "Note that If one indexes a multidimensional array with fewer indices than dimensions, one gets a subdimensional array. For example:"
@@ -2445,7 +2445,7 @@
   {
    "cell_type": "code",
    "execution_count": 85,
-   "id": "eb3a616a",
+   "id": "43465e15",
    "metadata": {},
    "outputs": [
     {
@@ -2465,7 +2465,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "377b05fe",
+   "id": "a66843c9",
    "metadata": {},
    "source": [
     "That is, each index specified selects the array corresponding to the rest of the dimensions selected. In the above example, choosing 0 means that the remaining dimension of length 5 is being left unspecified, and that what is returned is an array of that dimensionality and size. It must be noted that the returned array is a view, i.e., it is not a copy of the original, but points to the same values in memory as does the original array. In this case, the 1-D array at the first position (0) is returned. So using a single index on the returned array, results in a single element being returned. That is:"
@@ -2474,7 +2474,7 @@
   {
    "cell_type": "code",
    "execution_count": 86,
-   "id": "6537ef11",
+   "id": "bd21f5c4",
    "metadata": {},
    "outputs": [
     {
@@ -2494,7 +2494,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "48837a75",
+   "id": "c0c7ccde",
    "metadata": {},
    "source": [
     "So note that `x[0, 2] == x[0][2]` though the second case is more inefficient as a new temporary array is created after the first index that is subsequently indexed by 2.\n",
@@ -2521,7 +2521,7 @@
   {
    "cell_type": "code",
    "execution_count": 87,
-   "id": "14dd9f2f",
+   "id": "07bd56ef",
    "metadata": {},
    "outputs": [
     {
@@ -2542,7 +2542,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "16627f0f",
+   "id": "fcfac40f",
    "metadata": {},
    "source": [
     "- Negative *i* and *j* are interpreted as *n + i* and *n + j* where *n* is the number of elements in the corresponding dimension. Negative *k* makes stepping go towards smaller indices. From the above example:"
@@ -2551,7 +2551,7 @@
   {
    "cell_type": "code",
    "execution_count": 88,
-   "id": "0a0b3c3d",
+   "id": "048b59f4",
    "metadata": {},
    "outputs": [
     {
@@ -2572,7 +2572,7 @@
   {
    "cell_type": "code",
    "execution_count": 89,
-   "id": "80e5ec1b",
+   "id": "84757c4d",
    "metadata": {},
    "outputs": [
     {
@@ -2592,7 +2592,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3e1c8d05",
+   "id": "3b2fdb43",
    "metadata": {},
    "source": [
     "- Assume *n* is the number of elements in the dimension being sliced. Then, if *i* is not given it defaults to 0 for *k > 0* and *n - 1* for *k < 0*. If *j* is not given it defaults to *n* for *k > 0* and *-n-1* for *k < 0*. If *k* is not given it defaults to 1. Note that `::` is the same as : and means select all indices along this axis. From the above example:"
@@ -2601,7 +2601,7 @@
   {
    "cell_type": "code",
    "execution_count": 90,
-   "id": "51c83c7c",
+   "id": "30695935",
    "metadata": {},
    "outputs": [
     {
@@ -2621,7 +2621,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fdc8656b",
+   "id": "330bd5ac",
    "metadata": {},
    "source": [
     "- If the number of objects in the selection tuple is less than N, then `:` is assumed for any subsequent dimensions. For example:"
@@ -2630,7 +2630,7 @@
   {
    "cell_type": "code",
    "execution_count": 91,
-   "id": "5162f77e",
+   "id": "9838bfd2",
    "metadata": {},
    "outputs": [
     {
@@ -2652,7 +2652,7 @@
   {
    "cell_type": "code",
    "execution_count": 92,
-   "id": "a57b6bca",
+   "id": "0e244e06",
    "metadata": {},
    "outputs": [
     {
@@ -2674,7 +2674,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "944e3a00",
+   "id": "4d559829",
    "metadata": {},
    "source": [
     "- An integer, *i*, returns the same values as `i:i+1` **except** the dimensionality of the returned object is reduced by 1. In particular, a selection tuple with the *p*-th element an integer (and all other entries *:*) returns the corresponding sub-array with dimension *N - 1*. If *N = 1* then the returned object is an array scalar.\n",
@@ -2699,7 +2699,7 @@
   {
    "cell_type": "code",
    "execution_count": 93,
-   "id": "d313c9c9",
+   "id": "9a50f69f",
    "metadata": {},
    "outputs": [
     {
@@ -2720,7 +2720,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "accce0d4",
+   "id": "3168d462",
    "metadata": {},
    "source": [
     "This is equivalent to:"
@@ -2729,7 +2729,7 @@
   {
    "cell_type": "code",
    "execution_count": 94,
-   "id": "962ce5bd",
+   "id": "c171f937",
    "metadata": {},
    "outputs": [
     {
@@ -2750,7 +2750,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d124c0c9",
+   "id": "f10eca8b",
    "metadata": {},
    "source": [
     "Each `newaxis` object in the selection tuple serves to expand the dimensions of the resulting selection by one unit-length dimension. The added dimension is the position of the `newaxis` object in the selection tuple. `newaxis` is an alias for `None`, and `None` can be used in place of this with the same result. From the above example:"
@@ -2759,7 +2759,7 @@
   {
    "cell_type": "code",
    "execution_count": 95,
-   "id": "721c8994",
+   "id": "e45841c7",
    "metadata": {},
    "outputs": [
     {
@@ -2780,7 +2780,7 @@
   {
    "cell_type": "code",
    "execution_count": 96,
-   "id": "c113639e",
+   "id": "eef9df8e",
    "metadata": {},
    "outputs": [
     {
@@ -2800,7 +2800,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "358ff1fa",
+   "id": "745aba87",
    "metadata": {},
    "source": [
     "This can be handy to combine two arrays in a way that otherwise would require explicit reshaping operations. For example:"
@@ -2809,7 +2809,7 @@
   {
    "cell_type": "code",
    "execution_count": 97,
-   "id": "c5a789a0",
+   "id": "62848397",
    "metadata": {},
    "outputs": [
     {
@@ -2834,7 +2834,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fbffca75",
+   "id": "f86ca8b2",
    "metadata": {},
    "source": [
     "### Advanced indexing\n",
@@ -2857,7 +2857,7 @@
   {
    "cell_type": "code",
    "execution_count": 98,
-   "id": "8b144bd4",
+   "id": "40894d5e",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2867,7 +2867,7 @@
   {
    "cell_type": "code",
    "execution_count": 99,
-   "id": "5cc04bed",
+   "id": "e23dde7a",
    "metadata": {},
    "outputs": [
     {
@@ -2888,7 +2888,7 @@
   {
    "cell_type": "code",
    "execution_count": 100,
-   "id": "f562a26b",
+   "id": "a78aed57",
    "metadata": {},
    "outputs": [
     {
@@ -2909,7 +2909,7 @@
   {
    "cell_type": "code",
    "execution_count": 101,
-   "id": "d4269301",
+   "id": "f17ca511",
    "metadata": {},
    "outputs": [
     {
@@ -2929,7 +2929,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a9125b73",
+   "id": "8802458d",
    "metadata": {},
    "source": [
     "If the index values are out of bounds then an `IndexError` is thrown:"
@@ -2938,7 +2938,7 @@
   {
    "cell_type": "code",
    "execution_count": 102,
-   "id": "76b7edb0",
+   "id": "ad1e9a5a",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2948,7 +2948,7 @@
   {
    "cell_type": "code",
    "execution_count": 103,
-   "id": "b9cbd184",
+   "id": "e6cb7d13",
    "metadata": {},
    "outputs": [
     {
@@ -2969,7 +2969,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e395168c",
+   "id": "e234e871",
    "metadata": {},
    "source": [
     "```py\n",
@@ -2999,7 +2999,7 @@
   {
    "cell_type": "code",
    "execution_count": 104,
-   "id": "8ab458d8",
+   "id": "9fdb98e0",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3009,7 +3009,7 @@
   {
    "cell_type": "code",
    "execution_count": 105,
-   "id": "1f2bcb11",
+   "id": "43d82062",
    "metadata": {},
    "outputs": [
     {
@@ -3034,7 +3034,7 @@
   {
    "cell_type": "code",
    "execution_count": 106,
-   "id": "820062fc",
+   "id": "f0a2dfa4",
    "metadata": {},
    "outputs": [
     {
@@ -3054,7 +3054,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "db53daac",
+   "id": "aab0d978",
    "metadata": {},
    "source": [
     "In this case, if the index arrays have a matching shape, and there is an index array for each dimension of the array being indexed, the resultant array has the same shape as the index arrays, and the values correspond to the index set for each position in the index arrays. In this example, the first index value is 0 for both index arrays, and thus the first value of the resultant array is `y[0, 0]`. The next value is `y[2, 1]`, and the last is `y[4, 2]`.\n",
@@ -3077,7 +3077,7 @@
   {
    "cell_type": "code",
    "execution_count": 107,
-   "id": "1b1a1b5a",
+   "id": "715e99c5",
    "metadata": {},
    "outputs": [
     {
@@ -3097,7 +3097,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0cee103d",
+   "id": "d63b4484",
    "metadata": {},
    "source": [
     "Jumping to the next level of complexity, it is possible to only partially index an array with index arrays. It takes a bit of thought to understand what happens in such cases. For example if we just use one index array with y:"
@@ -3106,7 +3106,7 @@
   {
    "cell_type": "code",
    "execution_count": 108,
-   "id": "8406aaa9",
+   "id": "8a16e9f8",
    "metadata": {},
    "outputs": [
     {
@@ -3128,7 +3128,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5df0f65d",
+   "id": "66b89963",
    "metadata": {},
    "source": [
     "It results in the construction of a new array where each value of the index array selects one row from the array being indexed and the resultant array has the resulting shape (number of index elements, size of row).\n",
@@ -3143,7 +3143,7 @@
   {
    "cell_type": "code",
    "execution_count": 109,
-   "id": "93d197df",
+   "id": "b5946079",
    "metadata": {},
    "outputs": [
     {
@@ -3164,7 +3164,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "477e572b",
+   "id": "39398bc5",
    "metadata": {},
    "source": [
     "To achieve a behaviour similar to the basic slicing above, broadcasting can be used. The function `ix_` can help with this broadcasting. This is best understood with an example.\n",
@@ -3177,7 +3177,7 @@
   {
    "cell_type": "code",
    "execution_count": 110,
-   "id": "33c0ea04",
+   "id": "98399336",
    "metadata": {},
    "outputs": [
     {
@@ -3206,7 +3206,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4fba67c2",
+   "id": "32a705f6",
    "metadata": {},
    "source": [
     "However, since the indexing arrays above just repeat themselves, broadcasting can be used (compare operations such as `rows[:, np.newaxis] + columns`) to simplify this:"
@@ -3215,7 +3215,7 @@
   {
    "cell_type": "code",
    "execution_count": 111,
-   "id": "4445fc8a",
+   "id": "bd6be710",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3226,7 +3226,7 @@
   {
    "cell_type": "code",
    "execution_count": 112,
-   "id": "690e98fd",
+   "id": "e805c484",
    "metadata": {},
    "outputs": [
     {
@@ -3248,7 +3248,7 @@
   {
    "cell_type": "code",
    "execution_count": 113,
-   "id": "ac5928f6",
+   "id": "02b07429",
    "metadata": {},
    "outputs": [
     {
@@ -3269,7 +3269,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c2e7743d",
+   "id": "ca461196",
    "metadata": {},
    "source": [
     "This broadcasting can also be achieved using the function `ix_`:"
@@ -3278,7 +3278,7 @@
   {
    "cell_type": "code",
    "execution_count": 114,
-   "id": "8b37f49c",
+   "id": "5ed1a21e",
    "metadata": {},
    "outputs": [
     {
@@ -3299,7 +3299,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8df9fdc5",
+   "id": "9c5e611f",
    "metadata": {},
    "source": [
     "Note that without the `np.ix_` call, only the diagonal elements would be selected:"
@@ -3308,7 +3308,7 @@
   {
    "cell_type": "code",
    "execution_count": 115,
-   "id": "60cd151b",
+   "id": "bf36f567",
    "metadata": {},
    "outputs": [
     {
@@ -3328,7 +3328,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "40cb41d0",
+   "id": "ed63cbac",
    "metadata": {},
    "source": [
     "This difference is the most important thing to remember about indexing with multiple advanced indices.\n",
@@ -3349,7 +3349,7 @@
   {
    "cell_type": "code",
    "execution_count": 116,
-   "id": "034f6e88",
+   "id": "32c43872",
    "metadata": {},
    "outputs": [
     {
@@ -3370,7 +3370,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7ec2b126",
+   "id": "34831f87",
    "metadata": {},
    "source": [
     "Or wish to add a constant to all negative elements:"
@@ -3379,7 +3379,7 @@
   {
    "cell_type": "code",
    "execution_count": 117,
-   "id": "776e538b",
+   "id": "111aa78d",
    "metadata": {},
    "outputs": [
     {
@@ -3401,7 +3401,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c48ff2a1",
+   "id": "b4755dfa",
    "metadata": {},
    "source": [
     "In general if an index includes a Boolean array, the result will be identical to inserting `obj.nonzero()` into the same position and using the integer array indexing mechanism described above. `x[ind_1, boolean_array, ind_2]` is equivalent to `x[(ind_1,) + boolean_array.nonzero() + (ind_2,)]`.\n",
@@ -3414,7 +3414,7 @@
   {
    "cell_type": "code",
    "execution_count": 118,
-   "id": "3f3b00b5",
+   "id": "676a24a0",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3425,7 +3425,7 @@
   {
    "cell_type": "code",
    "execution_count": 119,
-   "id": "4db6538a",
+   "id": "9342d31e",
    "metadata": {},
    "outputs": [
     {
@@ -3446,7 +3446,7 @@
   {
    "cell_type": "code",
    "execution_count": 120,
-   "id": "f2b3c754",
+   "id": "d280361a",
    "metadata": {},
    "outputs": [
     {
@@ -3467,7 +3467,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "67b5790c",
+   "id": "50ea3c69",
    "metadata": {},
    "source": [
     "Here the 4th and 5th rows are selected from the indexed array and combined to make a 2-D array.\n",
@@ -3480,7 +3480,7 @@
   {
    "cell_type": "code",
    "execution_count": 121,
-   "id": "ac321cea",
+   "id": "68f60c9e",
    "metadata": {},
    "outputs": [
     {
@@ -3503,7 +3503,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5d2c03c1",
+   "id": "b0642557",
    "metadata": {},
    "source": [
     "Combining multiple Boolean indexing arrays or a Boolean with an integer indexing array can best be understood with the `obj.nonzero()` analogy. The function `ix_` also supports boolean arrays and will work without any surprises.\n",
@@ -3516,7 +3516,7 @@
   {
    "cell_type": "code",
    "execution_count": 122,
-   "id": "f806b15f",
+   "id": "0d307621",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3530,7 +3530,7 @@
   {
    "cell_type": "code",
    "execution_count": 123,
-   "id": "e7bf6120",
+   "id": "bc3aa448",
    "metadata": {},
    "outputs": [
     {
@@ -3551,7 +3551,7 @@
   {
    "cell_type": "code",
    "execution_count": 124,
-   "id": "21dd0d79",
+   "id": "bdb37b53",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3561,7 +3561,7 @@
   {
    "cell_type": "code",
    "execution_count": 125,
-   "id": "2b9faab4",
+   "id": "5806efee",
    "metadata": {},
    "outputs": [
     {
@@ -3582,7 +3582,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "495bb350",
+   "id": "444524a9",
    "metadata": {},
    "source": [
     "Without the n`p.ix_` call, only the diagonal elements would be selected.\n",
@@ -3593,7 +3593,7 @@
   {
    "cell_type": "code",
    "execution_count": 126,
-   "id": "964c9d9e",
+   "id": "9c49d837",
    "metadata": {},
    "outputs": [
     {
@@ -3615,7 +3615,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "66bcdf85",
+   "id": "09ef8785",
    "metadata": {},
    "source": [
     "##### Example 3\n",
@@ -3626,7 +3626,7 @@
   {
    "cell_type": "code",
    "execution_count": 127,
-   "id": "a31b7d5a",
+   "id": "3ea8713e",
    "metadata": {},
    "outputs": [
     {
@@ -3654,7 +3654,7 @@
   {
    "cell_type": "code",
    "execution_count": 128,
-   "id": "ae893b90",
+   "id": "6dfc825d",
    "metadata": {},
    "outputs": [
     {
@@ -3678,7 +3678,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c8f5879e",
+   "id": "e19fe24c",
    "metadata": {},
    "source": [
     "#### Combining advanced and basic indexing\n",
@@ -3691,7 +3691,7 @@
   {
    "cell_type": "code",
    "execution_count": 129,
-   "id": "edd90b6c",
+   "id": "e4e47f80",
    "metadata": {},
    "outputs": [
     {
@@ -3714,7 +3714,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1ec3cf31",
+   "id": "5251af62",
    "metadata": {},
    "source": [
     "In effect, the slice and index array operation are independent. The slice operation extracts columns with index 1 and 2, (i.e. the 2nd and 3rd columns), followed by the index array operation which extracts rows with index 0, 2 and 4 (i.e the first, third and fifth rows). This is equivalent to:"
@@ -3723,7 +3723,7 @@
   {
    "cell_type": "code",
    "execution_count": 130,
-   "id": "fff9b422",
+   "id": "5a59149e",
    "metadata": {},
    "outputs": [
     {
@@ -3745,7 +3745,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9ec5d536",
+   "id": "e6cb1904",
    "metadata": {},
    "source": [
     "A single advanced index can, for example, replace a slice and the result array will be the same. However, it is a copy and may have a different memory layout. A slice is preferable when it is possible. For example:"
@@ -3754,7 +3754,7 @@
   {
    "cell_type": "code",
    "execution_count": 131,
-   "id": "750ad1c4",
+   "id": "b30046a1",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3767,7 +3767,7 @@
   {
    "cell_type": "code",
    "execution_count": 132,
-   "id": "d9ba63fc",
+   "id": "96db8003",
    "metadata": {},
    "outputs": [
     {
@@ -3788,7 +3788,7 @@
   {
    "cell_type": "code",
    "execution_count": 133,
-   "id": "5baf9d82",
+   "id": "f808257c",
    "metadata": {},
    "outputs": [
     {
@@ -3808,7 +3808,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "07e4d279",
+   "id": "dc1581b2",
    "metadata": {},
    "source": [
     "The easiest way to understand a combination of multiple advanced indices may be to think in terms of the resulting shape. There are two parts to the indexing operation, the subspace defined by the basic indexing (excluding integers) and the subspace from the advanced indexing part. Two cases of index combination need to be distinguished:\n",
@@ -3834,7 +3834,7 @@
   {
    "cell_type": "code",
    "execution_count": 134,
-   "id": "31fa379d",
+   "id": "d32641f8",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3845,7 +3845,7 @@
   {
    "cell_type": "code",
    "execution_count": 135,
-   "id": "b10397fd",
+   "id": "5f1e8a7f",
    "metadata": {},
    "outputs": [
     {
@@ -3870,7 +3870,7 @@
   {
    "cell_type": "code",
    "execution_count": 136,
-   "id": "256d7468",
+   "id": "ec68edc1",
    "metadata": {},
    "outputs": [
     {
@@ -3891,7 +3891,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ed03ee54",
+   "id": "a39ed51a",
    "metadata": {},
    "source": [
     "### Field access\n",
@@ -3908,7 +3908,7 @@
   {
    "cell_type": "code",
    "execution_count": 137,
-   "id": "88408175",
+   "id": "1411547e",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3918,7 +3918,7 @@
   {
    "cell_type": "code",
    "execution_count": 138,
-   "id": "5ab1560f",
+   "id": "cdecac44",
    "metadata": {},
    "outputs": [
     {
@@ -3939,7 +3939,7 @@
   {
    "cell_type": "code",
    "execution_count": 139,
-   "id": "ec1a8c24",
+   "id": "7418c946",
    "metadata": {},
    "outputs": [
     {
@@ -3960,7 +3960,7 @@
   {
    "cell_type": "code",
    "execution_count": 140,
-   "id": "7f9eaba3",
+   "id": "45aa035e",
    "metadata": {},
    "outputs": [
     {
@@ -3981,7 +3981,7 @@
   {
    "cell_type": "code",
    "execution_count": 141,
-   "id": "026e6417",
+   "id": "2c608a95",
    "metadata": {},
    "outputs": [
     {
@@ -4001,7 +4001,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e70c7aa1",
+   "id": "afee0e0c",
    "metadata": {},
    "source": [
     "### Flat Iterator indexing\n",
@@ -4016,7 +4016,7 @@
   {
    "cell_type": "code",
    "execution_count": 142,
-   "id": "f01bf460",
+   "id": "742df6d4",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -4026,7 +4026,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "12d0c0d7",
+   "id": "5b426150",
    "metadata": {},
    "source": [
     "Or an array of the right size:"
@@ -4035,7 +4035,7 @@
   {
    "cell_type": "code",
    "execution_count": 143,
-   "id": "83d436ff",
+   "id": "137fb0fd",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -4044,7 +4044,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "506ceb99",
+   "id": "50845ef8",
    "metadata": {},
    "source": [
     "Note that assignments may result in changes if assigning higher types to lower types (like floats to ints) or even exceptions (assigning complex to floats or ints):"
@@ -4053,7 +4053,7 @@
   {
    "cell_type": "code",
    "execution_count": 144,
-   "id": "c247723c",
+   "id": "0447411c",
    "metadata": {},
    "outputs": [
     {
@@ -4074,7 +4074,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "58afbf47",
+   "id": "e03449ca",
    "metadata": {},
    "source": [
     "```py\n",
@@ -4093,7 +4093,7 @@
   {
    "cell_type": "code",
    "execution_count": 145,
-   "id": "4f172c9c",
+   "id": "4c3fc47b",
    "metadata": {},
    "outputs": [
     {
@@ -4115,7 +4115,7 @@
   {
    "cell_type": "code",
    "execution_count": 146,
-   "id": "e0469b3d",
+   "id": "7d7f9edc",
    "metadata": {},
    "outputs": [
     {
@@ -4136,7 +4136,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9c6e003b",
+   "id": "778839a8",
    "metadata": {},
    "source": [
     "Where people expect that the 1st location will be incremented by 3. In fact, it will only be incremented by 1. The reason is that a new array is extracted from the original (as a temporary) containing the values at 1, 1, 3, 1, then the value 1 is added to the temporary, and then the temporary is assigned back to the original array. Thus the value of the array at `x[1] + 1` is assigned to `x[1]` three times, rather than being incremented 3 times.\n",
@@ -4149,7 +4149,7 @@
   {
    "cell_type": "code",
    "execution_count": 147,
-   "id": "10981ef7",
+   "id": "a5f016a2",
    "metadata": {},
    "outputs": [
     {
@@ -4171,7 +4171,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9794d68a",
+   "id": "15de32de",
    "metadata": {},
    "source": [
     "So one can use code to construct tuples of any number of indices and then use these within an index.\n",
@@ -4182,7 +4182,7 @@
   {
    "cell_type": "code",
    "execution_count": 148,
-   "id": "7abf8d4f",
+   "id": "e1bd432a",
    "metadata": {},
    "outputs": [
     {
@@ -4203,7 +4203,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e5546b1b",
+   "id": "78e5454c",
    "metadata": {},
    "source": [
     "Likewise, ellipsis can be specified by code by using the Ellipsis object:"
@@ -4212,7 +4212,7 @@
   {
    "cell_type": "code",
    "execution_count": 149,
-   "id": "f17ac336",
+   "id": "c153c815",
    "metadata": {},
    "outputs": [
     {
@@ -4235,7 +4235,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "782b633e",
+   "id": "3a79c78b",
    "metadata": {},
    "source": [
     "For this reason, it is possible to use the output from the `np.nonzero()` function directly as an index since it always returns a tuple of index arrays.\n",
@@ -4246,7 +4246,7 @@
   {
    "cell_type": "code",
    "execution_count": 150,
-   "id": "32df210d",
+   "id": "709c07b2",
    "metadata": {},
    "outputs": [
     {
@@ -4316,7 +4316,7 @@
   {
    "cell_type": "code",
    "execution_count": 151,
-   "id": "ae43b357",
+   "id": "486b3c0c",
    "metadata": {},
    "outputs": [
     {
@@ -4336,7 +4336,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e8da70c1",
+   "id": "cd9fb5e8",
    "metadata": {},
    "source": [
     "## Structured arrays\n",
@@ -4349,7 +4349,7 @@
   {
    "cell_type": "code",
    "execution_count": 152,
-   "id": "5cefcabd",
+   "id": "55995518",
    "metadata": {},
    "outputs": [
     {
@@ -4372,7 +4372,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "55522e67",
+   "id": "59e7d7ce",
    "metadata": {},
    "source": [
     "Here `x` is a one-dimensional array of length two whose datatype is a structure with three fields: 1. A string of length 10 or less named `'name'`, 2. a 32-bit integer named `'age'`, and 3. a 32-bit float named `'weight'`.\n",
@@ -4383,7 +4383,7 @@
   {
    "cell_type": "code",
    "execution_count": 153,
-   "id": "26d5311d",
+   "id": "dc7d3bec",
    "metadata": {},
    "outputs": [
     {
@@ -4403,7 +4403,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6a829e6d",
+   "id": "b1549776",
    "metadata": {},
    "source": [
     "You can access and modify individual fields of a structured array by indexing with the field name:"
@@ -4412,7 +4412,7 @@
   {
    "cell_type": "code",
    "execution_count": 154,
-   "id": "99add1a0",
+   "id": "79707978",
    "metadata": {},
    "outputs": [
     {
@@ -4433,7 +4433,7 @@
   {
    "cell_type": "code",
    "execution_count": 155,
-   "id": "0549b6f6",
+   "id": "62e8ddc0",
    "metadata": {},
    "outputs": [
     {
@@ -4454,7 +4454,7 @@
   {
    "cell_type": "code",
    "execution_count": 156,
-   "id": "a46b7c66",
+   "id": "dde264d9",
    "metadata": {},
    "outputs": [
     {
@@ -4475,7 +4475,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "421614cf",
+   "id": "21fcf439",
    "metadata": {},
    "source": [
     "Structured datatypes are designed to be able to mimic 'structs' in the C language, and share a similar memory layout. They are meant for interfacing with C code and for low-level manipulation of structured buffers, for example for interpreting binary blobs. For these purposes they support specialized features such as subarrays, nested datatypes, and unions, and allow control over the memory layout of the structure.\n",
@@ -4498,7 +4498,7 @@
   {
    "cell_type": "code",
    "execution_count": 157,
-   "id": "ac3ad698",
+   "id": "bb25d3fb",
    "metadata": {},
    "outputs": [
     {
@@ -4518,7 +4518,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "256d4b96",
+   "id": "99121b0c",
    "metadata": {},
    "source": [
     "If `fieldname` is the empty string `''`, the field will be given a default name of the form `f#`, where `#` is the integer index of the field, counting from 0 from the left:"
@@ -4527,7 +4527,7 @@
   {
    "cell_type": "code",
    "execution_count": 158,
-   "id": "1d16916c",
+   "id": "fa2b7b4a",
    "metadata": {},
    "outputs": [
     {
@@ -4547,7 +4547,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5c1fea12",
+   "id": "938fbce7",
    "metadata": {},
    "source": [
     "The byte offsets of the fields within the structure and the total structure itemsize are determined automatically.\n",
@@ -4560,7 +4560,7 @@
   {
    "cell_type": "code",
    "execution_count": 159,
-   "id": "4ac7166d",
+   "id": "b90c587c",
    "metadata": {},
    "outputs": [
     {
@@ -4581,7 +4581,7 @@
   {
    "cell_type": "code",
    "execution_count": 160,
-   "id": "a97720c4",
+   "id": "fe1304fe",
    "metadata": {},
    "outputs": [
     {
@@ -4601,7 +4601,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a8b0360e",
+   "id": "fac5bb80",
    "metadata": {},
    "source": [
     "- A dictionary of field parameter arrays\n",
@@ -4614,7 +4614,7 @@
   {
    "cell_type": "code",
    "execution_count": 161,
-   "id": "6aa4b702",
+   "id": "d1e31afb",
    "metadata": {},
    "outputs": [
     {
@@ -4635,7 +4635,7 @@
   {
    "cell_type": "code",
    "execution_count": 162,
-   "id": "9c0848da",
+   "id": "7a016d54",
    "metadata": {},
    "outputs": [
     {
@@ -4658,7 +4658,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "054943d8",
+   "id": "40cabea2",
    "metadata": {},
    "source": [
     "Offsets may be chosen such that the fields overlap, though this will mean that assigning to one field may clobber any overlapping field’s data. As an exception, fields of `numpy.object_` type cannot overlap with other fields, because of the risk of clobbering the internal object pointer and then dereferencing it.\n",
@@ -4673,7 +4673,7 @@
   {
    "cell_type": "code",
    "execution_count": 163,
-   "id": "ab5f6e64",
+   "id": "99cb1995",
    "metadata": {},
    "outputs": [
     {
@@ -4693,7 +4693,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "54333b97",
+   "id": "70491a6d",
    "metadata": {},
    "source": [
     "#### Manipulating and Displaying Structured Datatypes\n",
@@ -4704,7 +4704,7 @@
   {
    "cell_type": "code",
    "execution_count": 164,
-   "id": "0b1c3e34",
+   "id": "f02aa357",
    "metadata": {},
    "outputs": [
     {
@@ -4725,7 +4725,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e9478ce8",
+   "id": "4adbfcfc",
    "metadata": {},
    "source": [
     "The field names may be modified by assigning to the `names` attribute using a sequence of strings of the same length.\n",
@@ -4736,7 +4736,7 @@
   {
    "cell_type": "code",
    "execution_count": 165,
-   "id": "2cec035d",
+   "id": "86ed67ad",
    "metadata": {},
    "outputs": [
     {
@@ -4756,7 +4756,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "af50c892",
+   "id": "7963c01d",
    "metadata": {},
    "source": [
     "Both the `names` and `fields` attributes will equal `None` for unstructured arrays. The recommended way to test if a dtype is structured is with `if dt.names is not None` rather than `if dt.names`, to account for dtypes with 0 fields.\n",
@@ -4773,7 +4773,7 @@
   {
    "cell_type": "code",
    "execution_count": 166,
-   "id": "f5386c1b",
+   "id": "d410a8a3",
    "metadata": {},
    "outputs": [
     {
@@ -4793,7 +4793,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "548a48d2",
+   "id": "a196d312",
    "metadata": {},
    "source": [
     "When using the first form of dictionary-based specification, the titles may be supplied as an extra `'titles'` key as described above. When using the second (discouraged) dictionary-based specification, the title can be supplied by providing a 3-element tuple `(datatype, offset, title)` instead of the usual 2-element tuple:"
@@ -4802,7 +4802,7 @@
   {
    "cell_type": "code",
    "execution_count": 167,
-   "id": "2d913a45",
+   "id": "bce8a60a",
    "metadata": {},
    "outputs": [
     {
@@ -4822,7 +4822,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "37eb8002",
+   "id": "d67ced8c",
    "metadata": {},
    "source": [
     "The `dtype.fields` dictionary will contain titles as keys, if any titles are used. This means effectively that a field with a title will be represented twice in the fields dictionary. The tuple values for these fields will also have a third element, the field title. Because of this, and because the `names` attribute preserves the field order while the `fields` attribute may not, it is recommended to iterate through the fields of a dtype using the `names` attribute of the dtype, which will not list titles, as in:"
@@ -4831,7 +4831,7 @@
   {
    "cell_type": "code",
    "execution_count": 168,
-   "id": "d16b0ad2",
+   "id": "a837c3cf",
    "metadata": {},
    "outputs": [
     {
@@ -4850,7 +4850,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8759fab3",
+   "id": "38862aa9",
    "metadata": {},
    "source": [
     "### Indexing and Assignment to Structured arrays\n",
@@ -4867,7 +4867,7 @@
   {
    "cell_type": "code",
    "execution_count": 169,
-   "id": "9d311732",
+   "id": "3bf8d38a",
    "metadata": {},
    "outputs": [
     {
@@ -4890,7 +4890,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "84bab5d9",
+   "id": "992b7664",
    "metadata": {},
    "source": [
     "##### Assignment from Scalars\n",
@@ -4901,7 +4901,7 @@
   {
    "cell_type": "code",
    "execution_count": 170,
-   "id": "100aaf82",
+   "id": "de15da18",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -4911,7 +4911,7 @@
   {
    "cell_type": "code",
    "execution_count": 171,
-   "id": "0048996d",
+   "id": "27427fff",
    "metadata": {},
    "outputs": [
     {
@@ -4934,7 +4934,7 @@
   {
    "cell_type": "code",
    "execution_count": 172,
-   "id": "027b14a6",
+   "id": "3a81a352",
    "metadata": {},
    "outputs": [
     {
@@ -4956,7 +4956,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "28756d42",
+   "id": "0e6f1ac4",
    "metadata": {},
    "source": [
     "Structured arrays can also be assigned to unstructured arrays, but only if the structured datatype has just a single field:"
@@ -4965,7 +4965,7 @@
   {
    "cell_type": "code",
    "execution_count": 173,
-   "id": "41bf2d2f",
+   "id": "070ff231",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -4976,7 +4976,7 @@
   {
    "cell_type": "code",
    "execution_count": 174,
-   "id": "538e2c1f",
+   "id": "b4bc2558",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -4985,7 +4985,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8fe3c666",
+   "id": "26e6623a",
    "metadata": {},
    "source": [
     "```py\n",
@@ -5006,7 +5006,7 @@
   {
    "cell_type": "code",
    "execution_count": 175,
-   "id": "162d0c94",
+   "id": "71d03f9c",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -5017,7 +5017,7 @@
   {
    "cell_type": "code",
    "execution_count": 176,
-   "id": "925e0b34",
+   "id": "c4ec0941",
    "metadata": {},
    "outputs": [
     {
@@ -5039,7 +5039,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a4f69851",
+   "id": "744b22ab",
    "metadata": {},
    "source": [
     "##### Assignment involving subarrays\n",
@@ -5056,7 +5056,7 @@
   {
    "cell_type": "code",
    "execution_count": 177,
-   "id": "860edddb",
+   "id": "993e80d4",
    "metadata": {},
    "outputs": [
     {
@@ -5078,7 +5078,7 @@
   {
    "cell_type": "code",
    "execution_count": 178,
-   "id": "2906edee",
+   "id": "94068fd8",
    "metadata": {},
    "outputs": [
     {
@@ -5099,7 +5099,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "de4288a7",
+   "id": "aa3ae581",
    "metadata": {},
    "source": [
     "The resulting array is a view into the original array. It shares the same memory locations and writing to the view will modify the original array."
@@ -5108,7 +5108,7 @@
   {
    "cell_type": "code",
    "execution_count": 179,
-   "id": "0517ddd7",
+   "id": "5aa890ea",
    "metadata": {},
    "outputs": [
     {
@@ -5130,7 +5130,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6276a2d2",
+   "id": "2b608a9a",
    "metadata": {},
    "source": [
     "This view has the same dtype and itemsize as the indexed field, so it is typically a non-structured array, except in the case of nested structures."
@@ -5139,7 +5139,7 @@
   {
    "cell_type": "code",
    "execution_count": 180,
-   "id": "38039e2e",
+   "id": "eedbd95b",
    "metadata": {},
    "outputs": [
     {
@@ -5159,7 +5159,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9bdaa05a",
+   "id": "13b741c2",
    "metadata": {},
    "source": [
     "If the accessed field is a subarray, the dimensions of the subarray are appended to the shape of the result:"
@@ -5168,7 +5168,7 @@
   {
    "cell_type": "code",
    "execution_count": 181,
-   "id": "e2fe5c45",
+   "id": "16456122",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -5178,7 +5178,7 @@
   {
    "cell_type": "code",
    "execution_count": 182,
-   "id": "dccdc746",
+   "id": "2f1109a9",
    "metadata": {},
    "outputs": [
     {
@@ -5199,7 +5199,7 @@
   {
    "cell_type": "code",
    "execution_count": 183,
-   "id": "2ae5eb1b",
+   "id": "21fc9653",
    "metadata": {},
    "outputs": [
     {
@@ -5219,7 +5219,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f953ef81",
+   "id": "7a8e2427",
    "metadata": {},
    "source": [
     "##### Accessing multiple fields\n",
@@ -5232,7 +5232,7 @@
   {
    "cell_type": "code",
    "execution_count": 184,
-   "id": "fefb4f30",
+   "id": "b2170bad",
    "metadata": {},
    "outputs": [
     {
@@ -5254,7 +5254,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "35fcd7ef",
+   "id": "71392fca",
    "metadata": {},
    "source": [
     "Assignment to the view modifies the original array. The view’s fields will be in the order they were indexed. Note that unlike for single-field indexing, the dtype of the view has the same itemsize as the original array, and has fields at the same offsets as in the original array, and unindexed fields are merely missing.\n",
@@ -5265,7 +5265,7 @@
   {
    "cell_type": "code",
    "execution_count": 185,
-   "id": "02fa87d8",
+   "id": "a1f70879",
    "metadata": {},
    "outputs": [
     {
@@ -5287,7 +5287,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "236cdd99",
+   "id": "ccd52130",
    "metadata": {},
    "source": [
     "This obeys the structured array assignment rules described above. For example, this means that one can swap the values of two fields using appropriate multi-field indexes:"
@@ -5296,7 +5296,7 @@
   {
    "cell_type": "code",
    "execution_count": 186,
-   "id": "6811c266",
+   "id": "e751d459",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -5305,7 +5305,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "321227cd",
+   "id": "17c3ffee",
    "metadata": {},
    "source": [
     "##### Indexing with an integer to get a structured scalar\n",
@@ -5316,7 +5316,7 @@
   {
    "cell_type": "code",
    "execution_count": 187,
-   "id": "99941fac",
+   "id": "c963d6bf",
    "metadata": {},
    "outputs": [
     {
@@ -5339,7 +5339,7 @@
   {
    "cell_type": "code",
    "execution_count": 188,
-   "id": "69f556d9",
+   "id": "b5d0b33e",
    "metadata": {},
    "outputs": [
     {
@@ -5359,7 +5359,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "322f244e",
+   "id": "b2af34f4",
    "metadata": {},
    "source": [
     "Unlike other numpy scalars, structured scalars are mutable and act like views into the original array, such that modifying the scalar will modify the original array. Structured scalars also support access and assignment by field name:"
@@ -5368,7 +5368,7 @@
   {
    "cell_type": "code",
    "execution_count": 189,
-   "id": "275e5aa6",
+   "id": "cde65ded",
    "metadata": {},
    "outputs": [
     {
@@ -5391,7 +5391,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "563981e3",
+   "id": "3843ee4d",
    "metadata": {},
    "source": [
     "Similarly to tuples, structured scalars can also be indexed with an integer:"
@@ -5400,7 +5400,7 @@
   {
    "cell_type": "code",
    "execution_count": 190,
-   "id": "f717fd01",
+   "id": "84a3e293",
    "metadata": {},
    "outputs": [
     {
@@ -5422,7 +5422,7 @@
   {
    "cell_type": "code",
    "execution_count": 191,
-   "id": "d8da4d5a",
+   "id": "0d6c362f",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -5431,7 +5431,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ce864c33",
+   "id": "6b9bbf7e",
    "metadata": {},
    "source": [
     "Thus, tuples might be thought of as the native Python equivalent to numpy’s structured types, much like native python integers are the equivalent to numpy’s integer types. Structured scalars may be converted to a tuple by calling `numpy.ndarray.item`:"
@@ -5440,7 +5440,7 @@
   {
    "cell_type": "code",
    "execution_count": 192,
-   "id": "73160c68",
+   "id": "dba6398a",
    "metadata": {},
    "outputs": [
     {
@@ -5460,7 +5460,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7a4af1a9",
+   "id": "76dfd92d",
    "metadata": {},
    "source": [
     "#### Viewing structured arrays containing objects\n",
@@ -5475,7 +5475,7 @@
   {
    "cell_type": "code",
    "execution_count": 193,
-   "id": "051080bb",
+   "id": "9a9c272d",
    "metadata": {},
    "outputs": [
     {
@@ -5497,7 +5497,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a1efc3ce",
+   "id": "c2166d63",
    "metadata": {},
    "source": [
     "NumPy will promote individual field datatypes to perform the comparison. So the following is also valid (note the `'f4'` dtype for the `'a'` field):"
@@ -5506,7 +5506,7 @@
   {
    "cell_type": "code",
    "execution_count": 194,
-   "id": "0ddc6a84",
+   "id": "80b0d583",
    "metadata": {},
    "outputs": [
     {
@@ -5527,7 +5527,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "86d6712e",
+   "id": "e1bfa6c9",
    "metadata": {},
    "source": [
     "To compare two structured arrays, it must be possible to promote them to a common dtype as returned by `numpy.result_type` and `np.promote_types`. This enforces that the number of fields, the field names, and the field titles must match precisely. When promotion is not possible, for example due to mismatching field names, NumPy will raise an error. Promotion between two structured dtypes results in a canonical dtype that ensures native byte-order for all fields:"
@@ -5536,7 +5536,7 @@
   {
    "cell_type": "code",
    "execution_count": 195,
-   "id": "8525513a",
+   "id": "47f739b4",
    "metadata": {},
    "outputs": [
     {
@@ -5557,7 +5557,7 @@
   {
    "cell_type": "code",
    "execution_count": 196,
-   "id": "ef0a80f4",
+   "id": "aaa0d1ad",
    "metadata": {},
    "outputs": [
     {
@@ -5577,7 +5577,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "29f79250",
+   "id": "2b318e45",
    "metadata": {},
    "source": [
     "The resulting dtype from promotion is also guaranteed to be packed, meaning that all fields are ordered contiguously and any unnecessary padding is removed:"
@@ -5586,7 +5586,7 @@
   {
    "cell_type": "code",
    "execution_count": 197,
-   "id": "78179e76",
+   "id": "a3eb03a4",
    "metadata": {},
    "outputs": [
     {
@@ -5608,7 +5608,7 @@
   {
    "cell_type": "code",
    "execution_count": 198,
-   "id": "2646685d",
+   "id": "e37a6f82",
    "metadata": {},
    "outputs": [
     {
@@ -5628,7 +5628,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e93627e5",
+   "id": "b3bbca7f",
    "metadata": {},
    "source": [
     "Note that the result prints without `offsets` or `itemsize` indicating no additional padding. If a structured dtype is created with `align=True` ensuring that `dtype.isalignedstruct` is true, this property is preserved:"
@@ -5637,7 +5637,7 @@
   {
    "cell_type": "code",
    "execution_count": 199,
-   "id": "95276731",
+   "id": "78dddbf6",
    "metadata": {},
    "outputs": [
     {
@@ -5659,7 +5659,7 @@
   {
    "cell_type": "code",
    "execution_count": 200,
-   "id": "ade96302",
+   "id": "3980a4ee",
    "metadata": {},
    "outputs": [
     {
@@ -5680,7 +5680,7 @@
   {
    "cell_type": "code",
    "execution_count": 201,
-   "id": "8c2516d8",
+   "id": "5c7c5cfa",
    "metadata": {},
    "outputs": [
     {
@@ -5700,7 +5700,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2e1ff208",
+   "id": "24091496",
    "metadata": {},
    "source": [
     "When promoting multiple dtypes, the result is aligned if any of the inputs is:"
@@ -5709,7 +5709,7 @@
   {
    "cell_type": "code",
    "execution_count": 202,
-   "id": "5989c869",
+   "id": "94299542",
    "metadata": {},
    "outputs": [
     {
@@ -5729,7 +5729,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "944fa2b1",
+   "id": "aee3bb68",
    "metadata": {},
    "source": [
     "The `<` and `>` operators always return `False` when comparing void structured arrays, and arithmetic and bitwise operations are not supported.\n",
diff --git a/_sources/data-science/working-with-data/relational-databases.ipynb b/_sources/data-science/working-with-data/relational-databases.ipynb
index 0ca30ae27c..d2671509bb 100644
--- a/_sources/data-science/working-with-data/relational-databases.ipynb
+++ b/_sources/data-science/working-with-data/relational-databases.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "d25f0717",
+   "id": "e8806420",
    "metadata": {},
    "source": [
     "# Relational databases\n",
diff --git a/_sources/data-science/working-with-data/working-with-data.ipynb b/_sources/data-science/working-with-data/working-with-data.ipynb
index 111abc09d8..92873ebf74 100644
--- a/_sources/data-science/working-with-data/working-with-data.ipynb
+++ b/_sources/data-science/working-with-data/working-with-data.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "6ab3d42f",
+   "id": "5f5bdd07",
    "metadata": {},
    "source": [
     "# Working with data\n",
diff --git a/_sources/deep-learning/autoencoder.ipynb b/_sources/deep-learning/autoencoder.ipynb
index 0f598fd884..af722d2e48 100644
--- a/_sources/deep-learning/autoencoder.ipynb
+++ b/_sources/deep-learning/autoencoder.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "26432477",
+   "id": "9c2c6c42",
    "metadata": {},
    "source": [
     "# Autoencoder\n",
@@ -108,7 +108,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "1c39e21d",
+   "id": "53b47613",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -118,7 +118,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "02a7afd0",
+   "id": "574ad4dc",
    "metadata": {},
    "source": [
     "MNIST Dataset parameters."
@@ -127,7 +127,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "2eaf34ef",
+   "id": "aad3812f",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -136,7 +136,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0c62fa63",
+   "id": "9a2e7f92",
    "metadata": {},
    "source": [
     "Training parameters."
@@ -145,7 +145,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "dcd59057",
+   "id": "0246af93",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -157,7 +157,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "376ff0b9",
+   "id": "9c1327af",
    "metadata": {},
    "source": [
     "Network Parameters"
@@ -166,7 +166,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "3c33e257",
+   "id": "1b7fa0cb",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -176,7 +176,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c50d49f0",
+   "id": "4ac530c4",
    "metadata": {},
    "source": [
     "Prepare MNIST data."
@@ -185,7 +185,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "071de12f",
+   "id": "c8c88364",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -201,7 +201,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4566d310",
+   "id": "491c2c40",
    "metadata": {},
    "source": [
     "Store layers weight & bias.\n",
@@ -211,7 +211,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "45bf0285",
+   "id": "552986d1",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -233,7 +233,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1e77c8d0",
+   "id": "19fa6d39",
    "metadata": {},
    "source": [
     "Building the encoder."
@@ -242,7 +242,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "46bf44f0",
+   "id": "dbc1f00b",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -258,7 +258,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "58f6c79f",
+   "id": "b6342243",
    "metadata": {},
    "source": [
     "Building the decoder."
@@ -267,7 +267,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "f357096c",
+   "id": "a46a4512",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -283,7 +283,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4d37abf8",
+   "id": "d115d00b",
    "metadata": {},
    "source": [
     "Mean square loss between original images and reconstructed ones."
@@ -292,7 +292,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "87ef739e",
+   "id": "66a57b39",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -302,7 +302,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "429308e7",
+   "id": "3af38928",
    "metadata": {},
    "source": [
     "Adam optimizer."
@@ -311,7 +311,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "77877aff",
+   "id": "b736b0e9",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -320,7 +320,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "66ab0c6f",
+   "id": "9e9b302e",
    "metadata": {},
    "source": [
     "Optimization process."
@@ -329,7 +329,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "ca1410d9",
+   "id": "1081a567",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -353,7 +353,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "572a7552",
+   "id": "6e27923c",
    "metadata": {},
    "source": [
     "Run training for the given number of steps."
@@ -362,7 +362,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "bee12173",
+   "id": "2b35dbec",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -377,7 +377,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "418fa76b",
+   "id": "1af3920e",
    "metadata": {},
    "source": [
     "Testing and Visualization."
@@ -386,7 +386,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "51737843",
+   "id": "bc5edc94",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -395,7 +395,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2a6fab19",
+   "id": "42d3bde7",
    "metadata": {},
    "source": [
     "Encode and decode images from test set and visualize their reconstruction."
@@ -404,7 +404,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "a3b9d7ca",
+   "id": "29d08251",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -438,7 +438,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "52acb449",
+   "id": "da4c2331",
    "metadata": {},
    "source": [
     "<div hidden>\n",
diff --git a/_sources/deep-learning/cnn.ipynb b/_sources/deep-learning/cnn.ipynb
index a7f1208508..76b39e5281 100644
--- a/_sources/deep-learning/cnn.ipynb
+++ b/_sources/deep-learning/cnn.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "f1c9a833",
+   "id": "639050a3",
    "metadata": {},
    "source": [
     "# Convolutional Neural Networks\n",
@@ -80,7 +80,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "43802b62",
+   "id": "bdc47221",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -278,7 +278,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3c2888f6",
+   "id": "4a94071b",
    "metadata": {},
    "source": [
     "## CIFAR-10\n",
@@ -305,7 +305,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "1cf9eca9",
+   "id": "85a204fe",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -617,7 +617,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "94f7abdb",
+   "id": "9d3a96b6",
    "metadata": {},
    "source": [
     "## How to fine-tune current CNN architectures?\n",
@@ -628,7 +628,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "1218e951",
+   "id": "f3e7bdd2",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -658,7 +658,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "11da34db",
+   "id": "aca00511",
    "metadata": {},
    "source": [
     "### Code"
@@ -667,7 +667,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "69987136",
+   "id": "4872ae61",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -766,7 +766,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2fd96a2e",
+   "id": "0f0df8fa",
    "metadata": {},
    "source": [
     "## Stylenet / Neural-Style\n",
@@ -808,7 +808,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "00b1c701",
+   "id": "04ccad7b",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1015,7 +1015,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "25eebcd0",
+   "id": "d88f18aa",
    "metadata": {},
    "source": [
     "## Deepdream in TensorFlow\n",
@@ -1035,7 +1035,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "53e2849e",
+   "id": "f326eff7",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1235,7 +1235,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "37d29f5a",
+   "id": "ae5f25b3",
    "metadata": {},
    "source": [
     "## Your turn! 🚀\n",
diff --git a/_sources/deep-learning/difussion-model.ipynb b/_sources/deep-learning/difussion-model.ipynb
index 42164a72b2..290d55c8a4 100644
--- a/_sources/deep-learning/difussion-model.ipynb
+++ b/_sources/deep-learning/difussion-model.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "aa18e11d",
+   "id": "332d49d0",
    "metadata": {},
    "source": [
     "# Diffusion Model\n",
@@ -162,7 +162,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "6399d19f",
+   "id": "cf43ffc5",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -182,7 +182,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6c87e664",
+   "id": "53f7613d",
    "metadata": {},
    "source": [
     "### Configuration"
@@ -191,7 +191,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "0f21b5e3",
+   "id": "b70c0b8c",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -209,7 +209,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "52d8860c",
+   "id": "6a0c4747",
    "metadata": {},
    "source": [
     "### Data Loading\n",
@@ -222,7 +222,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "2839a017",
+   "id": "89d20428",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -233,7 +233,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1deed892",
+   "id": "d8e0d2b3",
    "metadata": {},
    "source": [
     "### Data Preprocessing"
@@ -242,7 +242,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "ba26144b",
+   "id": "8206d472",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -252,7 +252,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1fac8d90",
+   "id": "1cbf89ec",
    "metadata": {},
    "source": [
     "### Data Augmentation"
@@ -261,7 +261,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "16205cd4",
+   "id": "0e51bd10",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -271,7 +271,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "33d66751",
+   "id": "40198f5b",
    "metadata": {},
    "source": [
     "### Data"
@@ -280,7 +280,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "0f2fdbe2",
+   "id": "1a5f3d50",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -297,7 +297,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8280ac3b",
+   "id": "94b0d9e5",
    "metadata": {},
    "source": [
     "### Linear schedule-beta"
@@ -306,7 +306,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "72b3a32a",
+   "id": "e1a1e75e",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -325,7 +325,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b87f09fc",
+   "id": "574478e8",
    "metadata": {},
    "source": [
     "### Define the alphas"
@@ -334,7 +334,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "e2aa86dd",
+   "id": "a143cdc8",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -351,7 +351,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "882d0bd1",
+   "id": "210be199",
    "metadata": {},
    "source": [
     "### Getting a sample $(x_t)$"
@@ -360,7 +360,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "11919e11",
+   "id": "f7cd0544",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -381,7 +381,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "67ef8584",
+   "id": "14346c7c",
    "metadata": {},
    "source": [
     "### Modeling"
@@ -390,7 +390,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "2374630d",
+   "id": "63287eca",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -500,7 +500,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d472d817",
+   "id": "cb26006a",
    "metadata": {},
    "source": [
     "### Training"
@@ -509,7 +509,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "e7177f1d",
+   "id": "b0831262",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -564,7 +564,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7984d25e",
+   "id": "6d462d36",
    "metadata": {},
    "source": [
     "#### Testing"
@@ -573,7 +573,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "f2965058",
+   "id": "011f1a83",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -614,7 +614,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7d61b52e",
+   "id": "f53c67b5",
    "metadata": {},
    "source": [
     "#### GIF output"
@@ -623,7 +623,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "1eeba59e",
+   "id": "692fa440",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -643,7 +643,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8967e386",
+   "id": "8b03ae3e",
    "metadata": {},
    "source": [
     "## Your turn! 🚀\n",
diff --git a/_sources/deep-learning/dqn.ipynb b/_sources/deep-learning/dqn.ipynb
index 48afd7fff7..d5d0c95b53 100644
--- a/_sources/deep-learning/dqn.ipynb
+++ b/_sources/deep-learning/dqn.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "40ce9fa0",
+   "id": "7efb4bbe",
    "metadata": {},
    "source": [
     "# Deep Q-learning\n",
@@ -228,7 +228,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "d622b6e2",
+   "id": "74365dad",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -241,7 +241,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "68e5c9aa",
+   "id": "f46fe270",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -562,7 +562,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "fb312bad",
+   "id": "078b90f3",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -576,7 +576,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "18f1aefd",
+   "id": "7d1d2c54",
    "metadata": {},
    "source": [
     "## Your turn! 🚀\n",
diff --git a/_sources/deep-learning/gan.ipynb b/_sources/deep-learning/gan.ipynb
index 8bfc3b53e3..225934e036 100644
--- a/_sources/deep-learning/gan.ipynb
+++ b/_sources/deep-learning/gan.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "91b4e058",
+   "id": "7be4e978",
    "metadata": {},
    "source": [
     "# Generative adversarial networks \n",
@@ -82,7 +82,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "f45e5256",
+   "id": "a2e66cd6",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -123,7 +123,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a41cd6ae",
+   "id": "b5c3f432",
    "metadata": {},
    "source": [
     "Load Data"
@@ -132,7 +132,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "4784882e",
+   "id": "1dc5bd4e",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -154,7 +154,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2c1d8d32",
+   "id": "262257db",
    "metadata": {},
    "source": [
     "Initialize Graph"
@@ -163,7 +163,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "7b6a55d5",
+   "id": "ba0a04aa",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -246,7 +246,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1a930ef9",
+   "id": "58b049db",
    "metadata": {},
    "source": [
     "Testing"
@@ -255,7 +255,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "81841e22",
+   "id": "e5e34f06",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -265,7 +265,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "37df20c4",
+   "id": "3cce1430",
    "metadata": {},
    "source": [
     "Inits"
@@ -274,7 +274,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "f994388d",
+   "id": "200e2a4f",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -290,7 +290,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b9d607b7",
+   "id": "dd5994ab",
    "metadata": {},
    "source": [
     "Train"
@@ -299,7 +299,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "4a56294f",
+   "id": "6d5cba5a",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -338,7 +338,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "440d4db9",
+   "id": "5edd9ffd",
    "metadata": {},
    "source": [
     "## Your turn! 🚀\n",
diff --git a/_sources/deep-learning/image-classification.ipynb b/_sources/deep-learning/image-classification.ipynb
index 7e30cfe8d9..cc0ea0731f 100644
--- a/_sources/deep-learning/image-classification.ipynb
+++ b/_sources/deep-learning/image-classification.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "e20f56bf",
+   "id": "42afbff1",
    "metadata": {},
    "source": [
     "# Image classification\n",
@@ -71,7 +71,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "a57eb7e0",
+   "id": "38424410",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -105,7 +105,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bc7cd33c",
+   "id": "02f96ed4",
    "metadata": {},
    "source": [
     "This function is useful for constructing convolutional neural network architectures, as it provides a simple way to combine convolution and batch normalization layers.\n",
@@ -125,7 +125,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "54de2cce",
+   "id": "f556891e",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -263,7 +263,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "53989c52",
+   "id": "7fab6f6d",
    "metadata": {},
    "source": [
     "This code defines a RepVGGBlock layer in TensorFlow, which is a convolutional block used in the RepVGG model. The RepVGG model is a neural network architecture that achieves high accuracy while having a simple structure. The RepVGGBlock layer is designed to be more efficient and easier to train compared to other convolutional layers.\n",
@@ -389,7 +389,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "597b1cdb",
+   "id": "38bd35bc",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -429,7 +429,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e58ae3e6",
+   "id": "d54303d0",
    "metadata": {},
    "source": [
     "This implementation uses the conv_bn_relu function to perform a convolution operation followed by batch normalization and ReLU activation. \n",
@@ -458,7 +458,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "587eb8b3",
+   "id": "0f233f6d",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -512,7 +512,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "25fa22a1",
+   "id": "20e7ce01",
    "metadata": {},
    "source": [
     "This implementation includes functions for building the building blocks of DenseNet: conv_block, dense_block, and transition_block. These building blocks are then used to construct the densenet function, which takes an input tensor and returns the output logits for a classification task.\n",
@@ -543,7 +543,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "d2f4f5f4",
+   "id": "76bb210c",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -593,7 +593,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e7cfe6a8",
+   "id": "0245a2ac",
    "metadata": {},
    "source": [
     "This implementation defines two functions: depthwise_separable_conv and mobilenet. The former implements the depthwise separable convolution operation used by the MobileNet architecture, while the latter constructs the entire MobileNet model.\n",
@@ -610,7 +610,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "a0306154",
+   "id": "8f566b98",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -675,7 +675,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0c00bca9",
+   "id": "7e1148b4",
    "metadata": {},
    "source": [
     "The above code defines a Vision Transformer (ViT) model in TensorFlow, which is a state-of-the-art architecture for image classification tasks that combines the transformer architecture with a patch-based approach for image processing.\n",
diff --git a/_sources/deep-learning/image-segmentation.ipynb b/_sources/deep-learning/image-segmentation.ipynb
index 6923511a1b..b869fae7bf 100644
--- a/_sources/deep-learning/image-segmentation.ipynb
+++ b/_sources/deep-learning/image-segmentation.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "1a1741e6",
+   "id": "3cd0e058",
    "metadata": {},
    "source": [
     "# Image segmentation\n",
@@ -30,7 +30,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "e86e9db3",
+   "id": "6d63ba27",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -44,7 +44,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "87b9c60c",
+   "id": "bed31ba2",
    "metadata": {},
    "source": [
     "### Prepare the Oxford-IIIT Pets dataset"
@@ -53,7 +53,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "5b8e3d63",
+   "id": "84eeb22b",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -62,7 +62,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cb015d8c",
+   "id": "ffdf886a",
    "metadata": {},
    "source": [
     "In addition, the image color values are normalized to the range [0, 1]. Finally, as mentioned above the pixels in the segmentation mask are labeled either {1, 2, 3}. For the sake of convenience, subtract 1 from the segmentation mask, resulting in labels that are : {0, 1, 2}."
@@ -71,7 +71,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "0af77da9",
+   "id": "a9f5e6be",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -95,7 +95,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7aebc91c",
+   "id": "74b13141",
    "metadata": {},
    "source": [
     "The dataset already contains the required training and test splits, so continue to use the same splits:"
@@ -104,7 +104,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "8f0e5a20",
+   "id": "f04688ad",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -118,7 +118,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "29a4709b",
+   "id": "546337f1",
    "metadata": {},
    "source": [
     "The following class performs a simple augmentation by randomly-flipping an image. Go to the Image augmentation tutorial to learn more."
@@ -127,7 +127,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "5a06f944",
+   "id": "bb5d07eb",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -146,7 +146,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f075b5da",
+   "id": "82950042",
    "metadata": {},
    "source": [
     "Build the input pipeline, applying the augmentation after batching the inputs:"
@@ -155,7 +155,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "35b7888b",
+   "id": "1137457a",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -173,7 +173,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d68dd470",
+   "id": "22e40bec",
    "metadata": {},
    "source": [
     "Visualize an image example and its corresponding mask from the dataset:"
@@ -182,7 +182,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "55eb1846",
+   "id": "a14ee529",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -205,7 +205,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5b95b7a5",
+   "id": "3593d897",
    "metadata": {},
    "source": [
     ":::{figure-md} 01_dataset_example_mask\n",
@@ -228,7 +228,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "de534bc1",
+   "id": "28d9030b",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -252,7 +252,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "95ed91ba",
+   "id": "2807f194",
    "metadata": {},
    "source": [
     "The decoder/upsampler is simply a series of upsample blocks implemented in TensorFlow examples:"
@@ -261,7 +261,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "c12f1bcd",
+   "id": "836047c9",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -276,7 +276,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "dc182336",
+   "id": "1b137944",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -306,7 +306,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "99792008",
+   "id": "38c26893",
    "metadata": {},
    "source": [
     "Note that the number of filters on the last layer is set to the number of . This will be one output channel per class.output_channels.\n",
@@ -319,7 +319,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "feb26bed",
+   "id": "a80913f3",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -333,7 +333,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "03ccd01d",
+   "id": "6176cdfc",
    "metadata": {},
    "source": [
     "Try out the model to check what it predicts before training:"
@@ -342,7 +342,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "eba41299",
+   "id": "897aac1e",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -363,7 +363,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "346d625e",
+   "id": "ae6e4ddf",
    "metadata": {},
    "source": [
     "Perhaps now we can try to show the predictions."
@@ -372,7 +372,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "ddcb7775",
+   "id": "3f6c1985",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -382,7 +382,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "18990430",
+   "id": "4c0a59ae",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -418,7 +418,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "70bf91f0",
+   "id": "0e0f8c9b",
    "metadata": {},
    "source": [
     "### Make predictions\n",
@@ -429,7 +429,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "840c0845",
+   "id": "e61644f2",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -438,7 +438,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8831fe3b",
+   "id": "3ce6f3a5",
    "metadata": {},
    "source": [
     ":::{figure-md} 02_display_prediction\n",
@@ -457,7 +457,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "08070181",
+   "id": "41d849fe",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -681,7 +681,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "55001442",
+   "id": "11ea35f8",
    "metadata": {},
    "source": [
     "### FCN\n",
@@ -704,7 +704,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "7251b432",
+   "id": "fe519380",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -839,7 +839,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "88bf5d8b",
+   "id": "3e97ff26",
    "metadata": {},
    "source": [
     "### SegNet\n",
@@ -864,7 +864,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "88d6abfb",
+   "id": "5cd2bf20",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -956,7 +956,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "338f2ab9",
+   "id": "c7e389d9",
    "metadata": {},
    "source": [
     "### DeepLab V3\n",
@@ -985,7 +985,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "684177c1",
+   "id": "768dca12",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1179,7 +1179,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a9730fc5",
+   "id": "30352371",
    "metadata": {},
    "source": [
     "## Your turn! 🚀\n",
diff --git a/_sources/deep-learning/object-detection.ipynb b/_sources/deep-learning/object-detection.ipynb
index 01645f8c7d..1ecc611428 100644
--- a/_sources/deep-learning/object-detection.ipynb
+++ b/_sources/deep-learning/object-detection.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "92db163a",
+   "id": "3d3a7ae2",
    "metadata": {},
    "source": [
     "# Object detection\n",
@@ -63,7 +63,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "88ae13d8",
+   "id": "d2fc4c29",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -95,7 +95,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8b222411",
+   "id": "ff403a68",
    "metadata": {},
    "source": [
     "```{seealso}\n",
@@ -126,7 +126,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "88de2ac1",
+   "id": "36bf4494",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -222,7 +222,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d4b212eb",
+   "id": "b9e763a6",
    "metadata": {},
    "source": [
     "- Mask"
@@ -231,7 +231,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "877a2b5a",
+   "id": "eb5c1f8b",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -266,7 +266,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8d03e6f9",
+   "id": "107125ba",
    "metadata": {},
    "source": [
     "### FCOS\n",
@@ -300,7 +300,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "28b8a367",
+   "id": "1207ef55",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -349,7 +349,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4fd1001d",
+   "id": "a1a15a7d",
    "metadata": {},
    "source": [
     "### DETR\n",
@@ -374,7 +374,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "19eb1023",
+   "id": "f93aca8d",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -414,7 +414,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a3cca477",
+   "id": "a288fca9",
    "metadata": {},
    "source": [
     "```{seealso}\n",
diff --git a/_sources/deep-learning/rnn.ipynb b/_sources/deep-learning/rnn.ipynb
index 3491e9e03a..c9e292d9a9 100644
--- a/_sources/deep-learning/rnn.ipynb
+++ b/_sources/deep-learning/rnn.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "5eb6c74a",
+   "id": "89c18cf6",
    "metadata": {},
    "source": [
     "# Recurrent Neural Networks\n",
@@ -89,7 +89,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "d18507cd",
+   "id": "88e794e4",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -289,7 +289,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "050a89f3",
+   "id": "cdc58246",
    "metadata": {},
    "source": [
     "## Your turn! 🚀\n",
diff --git a/_sources/deep-learning/time-series.ipynb b/_sources/deep-learning/time-series.ipynb
index c775316205..d97f9a67ee 100644
--- a/_sources/deep-learning/time-series.ipynb
+++ b/_sources/deep-learning/time-series.ipynb
@@ -2,7 +2,59 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "c1854fb4",
+   "id": "09ba2590",
+   "metadata": {
+    "tags": [
+     "hide-cell"
+    ]
+   },
+   "source": [
+    "---\n",
+    "jupytext:\n",
+    "  cell_metadata_filter: -all\n",
+    "  formats: md:myst\n",
+    "  text_representation:\n",
+    "    extension: .md\n",
+    "    format_name: myst\n",
+    "    format_version: 0.13\n",
+    "    jupytext_version: 1.11.5\n",
+    "kernelspec:\n",
+    "  display_name: Python 3\n",
+    "  language: python\n",
+    "  name: python3\n",
+    "  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "b9cb4380",
+   "metadata": {
+    "tags": [
+     "hide-input"
+    ]
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING: Ignoring invalid distribution -ytest (c:\\users\\victor\\anaconda3\\envs\\open-machine-learning-jupyter-book\\lib\\site-packages)\n",
+      "WARNING: Ignoring invalid distribution -ytest (c:\\users\\victor\\anaconda3\\envs\\open-machine-learning-jupyter-book\\lib\\site-packages)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Install the necessary dependencies\n",
+    "\n",
+    "import os\n",
+    "import sys \n",
+    "!{sys.executable} -m pip install --quiet pandas scikit-learn numpy matplotlib jupyterlab_myst ipython requests\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f48a31c0",
    "metadata": {},
    "source": [
     "# Time series\n",
@@ -22,22 +74,97 @@
     "\n",
     "The basic object of forecasting is the time series, which is a set of observations recorded over time. In forecasting applications, the observations are typically recorded with a regular frequency, like daily or monthly.\n",
     "\n",
-    "```{seealso}\n",
+    ":::{seealso}\n",
     "kaggle kernels output ryanholbrook/linear-regression-with-time-series -p /path/to/dest\n",
     "https://www.kaggle.com/competitions/store-sales-time-series-forecasting\n",
-    "```"
+    ":::"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "ae73d6bb",
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 43,
+   "id": "8bd77a31",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Hardcover</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Date</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2000-04-01</th>\n",
+       "      <td>139</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2000-04-02</th>\n",
+       "      <td>128</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2000-04-03</th>\n",
+       "      <td>172</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2000-04-04</th>\n",
+       "      <td>139</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2000-04-05</th>\n",
+       "      <td>191</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            Hardcover\n",
+       "Date                 \n",
+       "2000-04-01        139\n",
+       "2000-04-02        128\n",
+       "2000-04-03        172\n",
+       "2000-04-04        139\n",
+       "2000-04-05        191"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "import pandas as pd\n",
     "df = pd.read_csv(\n",
-    "    \"../assets/data/book_sales.csv\",\n",
+    "    \"https://static-1300131294.cos.ap-shanghai.myqcloud.com/data/book_sales.csv\",\n",
     "    index_col='Date',\n",
     "    parse_dates=['Date'],\n",
     ").drop('Paperback', axis=1)\n",
@@ -46,7 +173,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b5a2a5f3",
+   "id": "5014d222",
    "metadata": {},
    "source": [
     "This series records the number of hardcover book sales at a retail store over 30 days. Notice that we have a single column of observations $Hardcover$ with a time index $Date$.\n",
@@ -70,10 +197,92 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "21720ee6",
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 44,
+   "id": "61b47976",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Hardcover</th>\n",
+       "      <th>Time</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Date</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2000-04-01</th>\n",
+       "      <td>139</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2000-04-02</th>\n",
+       "      <td>128</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2000-04-03</th>\n",
+       "      <td>172</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2000-04-04</th>\n",
+       "      <td>139</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2000-04-05</th>\n",
+       "      <td>191</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            Hardcover  Time\n",
+       "Date                       \n",
+       "2000-04-01        139     0\n",
+       "2000-04-02        128     1\n",
+       "2000-04-03        172     2\n",
+       "2000-04-04        139     3\n",
+       "2000-04-05        191     4"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "import numpy as np\n",
     "df['Time'] = np.arange(len(df.index))\n",
@@ -82,7 +291,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2952bb2e",
+   "id": "d3e0a3ff",
    "metadata": {},
    "source": [
     "Linear regression with the time dummy produces the model:\n",
@@ -94,10 +303,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "ccaf6b35",
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 45,
+   "id": "82b99f97",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1100x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 390,
+       "width": 1089
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "import matplotlib.pyplot as plt\n",
     "import seaborn as sns\n",
@@ -127,7 +359,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f6874edc",
+   "id": "873c0cbe",
    "metadata": {},
    "source": [
     "Time-step features let you model time dependence. A series is time dependent if its values can be predicted from the time they occured. In the Hardcover Sales series, we can predict that sales later in the month are generally higher than sales earlier in the month.\n",
@@ -139,10 +371,92 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "4a1bd81f",
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 46,
+   "id": "a761ec74",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Hardcover</th>\n",
+       "      <th>Lag_1</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Date</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2000-04-01</th>\n",
+       "      <td>139</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2000-04-02</th>\n",
+       "      <td>128</td>\n",
+       "      <td>139.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2000-04-03</th>\n",
+       "      <td>172</td>\n",
+       "      <td>128.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2000-04-04</th>\n",
+       "      <td>139</td>\n",
+       "      <td>172.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2000-04-05</th>\n",
+       "      <td>191</td>\n",
+       "      <td>139.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            Hardcover  Lag_1\n",
+       "Date                        \n",
+       "2000-04-01        139    NaN\n",
+       "2000-04-02        128  139.0\n",
+       "2000-04-03        172  128.0\n",
+       "2000-04-04        139  172.0\n",
+       "2000-04-05        191  139.0"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "df['Lag_1'] = df['Hardcover'].shift(1)\n",
     "df = df.reindex(columns=['Hardcover', 'Lag_1'])\n",
@@ -151,7 +465,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0f623d5c",
+   "id": "fdee1749",
    "metadata": {},
    "source": [
     "Linear regression with a lag feature produces the model: $target = weight * lag + bias$. So lag features let us fit curves to lag plots where each observation in a series is plotted against the previous observation."
@@ -159,10 +473,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "0023042b",
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 47,
+   "id": "78ed4fd7",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1100x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 390,
+       "width": 370
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "fig, ax = plt.subplots()\n",
     "ax = sns.regplot(x='Lag_1', y='Hardcover', data=df, ci=None, scatter_kws=dict(color='0.25'))\n",
@@ -172,7 +509,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3a287e97",
+   "id": "640403f4",
    "metadata": {},
    "source": [
     "You can see from the lag plot that sales on one day $(Hardcover)$ are correlated with sales from the previous day $(Lag_1)$. When you see a relationship like this, you know a lag feature will be useful.\n",
@@ -188,10 +525,85 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "e955b248",
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 48,
+   "id": "72ffaa79",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>NumVehicles</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Day</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2003-11-01</th>\n",
+       "      <td>103536</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2003-11-02</th>\n",
+       "      <td>92051</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2003-11-03</th>\n",
+       "      <td>100795</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2003-11-04</th>\n",
+       "      <td>102352</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2003-11-05</th>\n",
+       "      <td>106569</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            NumVehicles\n",
+       "Day                    \n",
+       "2003-11-01       103536\n",
+       "2003-11-02        92051\n",
+       "2003-11-03       100795\n",
+       "2003-11-04       102352\n",
+       "2003-11-05       106569"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "from pathlib import Path\n",
     "from warnings import simplefilter\n",
@@ -199,6 +611,8 @@
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
     "import pandas as pd\n",
+    "import requests\n",
+    "from io import StringIO\n",
     "\n",
     "simplefilter(\"ignore\")  # ignore warnings to clean up output cells\n",
     "\n",
@@ -224,9 +638,12 @@
     "\n",
     "\n",
     "# Load Tunnel Traffic dataset\n",
-    "data_dir = Path(\"../assets/data\")\n",
-    "tunnel = pd.read_csv(data_dir / \"tunnel.csv\", parse_dates=[\"Day\"])\n",
+    "cloud_link = \"https://static-1300131294.cos.ap-shanghai.myqcloud.com/data/tunnel.csv\"\n",
+    "response = requests.get(cloud_link)\n",
+    "response.raise_for_status()\n",
     "\n",
+    "data = StringIO(response.text)\n",
+    "tunnel = pd.read_csv(data, parse_dates=[\"Day\"])\n",
     "# Create a time series in Pandas by setting the index to a date\n",
     "# column. We parsed \"Day\" as a date type by using `parse_dates` when\n",
     "# loading the data.\n",
@@ -245,7 +662,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "91af82a7",
+   "id": "5ea24216",
    "metadata": {},
    "source": [
     "### Time-step feature\n",
@@ -255,10 +672,92 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "2550d3f5",
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 49,
+   "id": "43082ff4",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>NumVehicles</th>\n",
+       "      <th>Time</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Day</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2003-11-01</th>\n",
+       "      <td>103536</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2003-11-02</th>\n",
+       "      <td>92051</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2003-11-03</th>\n",
+       "      <td>100795</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2003-11-04</th>\n",
+       "      <td>102352</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2003-11-05</th>\n",
+       "      <td>106569</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            NumVehicles  Time\n",
+       "Day                          \n",
+       "2003-11-01       103536     0\n",
+       "2003-11-02        92051     1\n",
+       "2003-11-03       100795     2\n",
+       "2003-11-04       102352     3\n",
+       "2003-11-05       106569     4"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "df = tunnel.copy()\n",
     "\n",
@@ -269,7 +768,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f7d3d386",
+   "id": "0d1f8170",
    "metadata": {},
    "source": [
     "The procedure for fitting a linear regression model follows the standard steps for scikit-learn."
@@ -277,9 +776,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "61d4cfbc",
-   "metadata": {},
+   "execution_count": 50,
+   "id": "0c31e5d3",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
    "outputs": [],
    "source": [
     "from sklearn.linear_model import LinearRegression\n",
@@ -299,7 +805,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2748ba33",
+   "id": "911e5428",
    "metadata": {},
    "source": [
     "The model actually created is (approximately): $Vehicles = 22.5 * Time + 98176$. Plotting the fitted values over time shows us how fitting linear regression to the time dummy creates the trend line defined by this equation."
@@ -307,10 +813,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "2d39b235",
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 51,
+   "id": "b128083d",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1100x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 390,
+       "width": 1089
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "ax = y.plot(**plot_params)\n",
     "ax = y_pred.plot(ax=ax, linewidth=3)\n",
@@ -319,7 +848,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "35d55cf8",
+   "id": "40e71af9",
    "metadata": {},
    "source": [
     "### Lag feature\n",
@@ -329,10 +858,99 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "dd0f3fbf",
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 52,
+   "id": "c82a0012",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>NumVehicles</th>\n",
+       "      <th>Time</th>\n",
+       "      <th>Lag_1</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Day</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2003-11-01</th>\n",
+       "      <td>103536</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2003-11-02</th>\n",
+       "      <td>92051</td>\n",
+       "      <td>1</td>\n",
+       "      <td>103536.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2003-11-03</th>\n",
+       "      <td>100795</td>\n",
+       "      <td>2</td>\n",
+       "      <td>92051.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2003-11-04</th>\n",
+       "      <td>102352</td>\n",
+       "      <td>3</td>\n",
+       "      <td>100795.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2003-11-05</th>\n",
+       "      <td>106569</td>\n",
+       "      <td>4</td>\n",
+       "      <td>102352.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            NumVehicles  Time     Lag_1\n",
+       "Day                                    \n",
+       "2003-11-01       103536     0       NaN\n",
+       "2003-11-02        92051     1  103536.0\n",
+       "2003-11-03       100795     2   92051.0\n",
+       "2003-11-04       102352     3  100795.0\n",
+       "2003-11-05       106569     4  102352.0"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "df['Lag_1'] = df['NumVehicles'].shift(1)\n",
     "df.head()"
@@ -340,7 +958,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fd526244",
+   "id": "ef536691",
    "metadata": {},
    "source": [
     "When creating lag features, we need to decide what to do with the missing values produced. Filling them in is one option, maybe with 0.0 or \"backfilling\" with the first known value. Instead, we'll just drop the missing values, making sure to also drop values in the target from corresponding dates."
@@ -348,9 +966,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "f28a71d8",
-   "metadata": {},
+   "execution_count": 53,
+   "id": "bda086ba",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
    "outputs": [],
    "source": [
     "from sklearn.linear_model import LinearRegression\n",
@@ -368,7 +993,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "29539c55",
+   "id": "870be824",
    "metadata": {},
    "source": [
     "The lag plot shows us how well we were able to fit the relationship between the number of vehicles one day and the number the previous day."
@@ -376,10 +1001,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "f9a500d0",
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 54,
+   "id": "ced75d86",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1100x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 389,
+       "width": 405
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "fig, ax = plt.subplots()\n",
     "ax.plot(X['Lag_1'], y, '.', color='0.25')\n",
@@ -392,7 +1040,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e7feb7a6",
+   "id": "da88e80b",
    "metadata": {},
    "source": [
     "What does this prediction from a lag feature mean about how well we can predict the series across time? The following time plot shows us how our forecasts now respond to the behavior of the series in the recent past."
@@ -400,10 +1048,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "6b2c53bf",
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 55,
+   "id": "8529ba77",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1100x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 390,
+       "width": 1089
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "ax = y.plot(**plot_params)\n",
     "ax = y_pred.plot()"
@@ -411,7 +1082,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "23ce035d",
+   "id": "2d925f22",
    "metadata": {},
    "source": [
     "The best time series models will usually include some combination of time-step features and lag features. Over the next few lessons, we'll learn how to engineer features modeling the most common patterns in time series using the features from this lesson as a starting point.\n",
@@ -430,9 +1101,9 @@
     "The forecast horizon is the time for which you are making a forecast. We often describe a forecast by the number of time steps in its horizon: a \"1-step\" forecast or \"5-step\" forecast, say. The forecast horizon describes the target.\n",
     "\n",
     ":::{figure-md} 01_def\n",
-    "<img src=\"../../images/deep-learning/time-series/01_def.png\" width=\"90%\" class=\"bg-white mb-1\">\n",
+    "<img src=\"https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/time-series/01_def.png\" width=\"90%\" class=\"bg-white mb-1\">\n",
     "\n",
-    "A three-step forecast horizon with a two-step lead time, using four lag features. The figure represents what would be a single row of training data -- data for a single prediction, in other words. \n",
+    "A three-step forecast horizon with a two-step lead time, using four lag features. The figure represents what would be a single row of training data -- data for a single prediction, in other words.\n",
     ":::\n",
     "\n",
     "The time between the origin and the horizon is the lead time (or sometimes latency) of the forecast. A forecast's lead time is described by the number of steps from origin to horizon: a \"1-step ahead\" or \"3-step ahead\" forecast, say. In practice, it may be necessary for a forecast to begin multiple steps ahead of the origin because of delays in data acquisition or processing.\n",
@@ -448,10 +1119,181 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "d8fc8354",
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 56,
+   "id": "0d06432e",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style type=\"text/css\">\n",
+       "#T_7d221_row0_col0, #T_7d221_row0_col1, #T_7d221_row0_col2, #T_7d221_row1_col0, #T_7d221_row1_col1, #T_7d221_row1_col2, #T_7d221_row2_col0, #T_7d221_row2_col1, #T_7d221_row2_col2, #T_7d221_row3_col0, #T_7d221_row3_col1, #T_7d221_row3_col2, #T_7d221_row4_col0, #T_7d221_row4_col1, #T_7d221_row4_col2, #T_7d221_row5_col0, #T_7d221_row5_col1, #T_7d221_row5_col2, #T_7d221_row6_col0, #T_7d221_row6_col1, #T_7d221_row6_col2, #T_7d221_row7_col0, #T_7d221_row7_col1, #T_7d221_row7_col2, #T_7d221_row8_col0, #T_7d221_row8_col1, #T_7d221_row8_col2, #T_7d221_row9_col0, #T_7d221_row9_col1, #T_7d221_row9_col2 {\n",
+       "  background-color: LavenderBlush;\n",
+       "}\n",
+       "#T_7d221_row0_col3, #T_7d221_row0_col4, #T_7d221_row0_col5, #T_7d221_row0_col6, #T_7d221_row0_col7, #T_7d221_row1_col3, #T_7d221_row1_col4, #T_7d221_row1_col5, #T_7d221_row1_col6, #T_7d221_row1_col7, #T_7d221_row2_col3, #T_7d221_row2_col4, #T_7d221_row2_col5, #T_7d221_row2_col6, #T_7d221_row2_col7, #T_7d221_row3_col3, #T_7d221_row3_col4, #T_7d221_row3_col5, #T_7d221_row3_col6, #T_7d221_row3_col7, #T_7d221_row4_col3, #T_7d221_row4_col4, #T_7d221_row4_col5, #T_7d221_row4_col6, #T_7d221_row4_col7, #T_7d221_row5_col3, #T_7d221_row5_col4, #T_7d221_row5_col5, #T_7d221_row5_col6, #T_7d221_row5_col7, #T_7d221_row6_col3, #T_7d221_row6_col4, #T_7d221_row6_col5, #T_7d221_row6_col6, #T_7d221_row6_col7, #T_7d221_row7_col3, #T_7d221_row7_col4, #T_7d221_row7_col5, #T_7d221_row7_col6, #T_7d221_row7_col7, #T_7d221_row8_col3, #T_7d221_row8_col4, #T_7d221_row8_col5, #T_7d221_row8_col6, #T_7d221_row8_col7, #T_7d221_row9_col3, #T_7d221_row9_col4, #T_7d221_row9_col5, #T_7d221_row9_col6, #T_7d221_row9_col7 {\n",
+       "  background-color: Lavender;\n",
+       "}\n",
+       "</style>\n",
+       "<table id=\"T_7d221\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th class=\"blank level0\" >&nbsp;</th>\n",
+       "      <th id=\"T_7d221_level0_col0\" class=\"col_heading level0 col0\" colspan=\"3\">Targets</th>\n",
+       "      <th id=\"T_7d221_level0_col3\" class=\"col_heading level0 col3\" colspan=\"5\">Features</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th class=\"blank level1\" >&nbsp;</th>\n",
+       "      <th id=\"T_7d221_level1_col0\" class=\"col_heading level1 col0\" >y_step_3</th>\n",
+       "      <th id=\"T_7d221_level1_col1\" class=\"col_heading level1 col1\" >y_step_2</th>\n",
+       "      <th id=\"T_7d221_level1_col2\" class=\"col_heading level1 col2\" >y_step_1</th>\n",
+       "      <th id=\"T_7d221_level1_col3\" class=\"col_heading level1 col3\" >y_lag_2</th>\n",
+       "      <th id=\"T_7d221_level1_col4\" class=\"col_heading level1 col4\" >y_lag_3</th>\n",
+       "      <th id=\"T_7d221_level1_col5\" class=\"col_heading level1 col5\" >y_lag_4</th>\n",
+       "      <th id=\"T_7d221_level1_col6\" class=\"col_heading level1 col6\" >y_lag_5</th>\n",
+       "      <th id=\"T_7d221_level1_col7\" class=\"col_heading level1 col7\" >y_lag_6</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th class=\"index_name level0\" >Year</th>\n",
+       "      <th class=\"blank col0\" >&nbsp;</th>\n",
+       "      <th class=\"blank col1\" >&nbsp;</th>\n",
+       "      <th class=\"blank col2\" >&nbsp;</th>\n",
+       "      <th class=\"blank col3\" >&nbsp;</th>\n",
+       "      <th class=\"blank col4\" >&nbsp;</th>\n",
+       "      <th class=\"blank col5\" >&nbsp;</th>\n",
+       "      <th class=\"blank col6\" >&nbsp;</th>\n",
+       "      <th class=\"blank col7\" >&nbsp;</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th id=\"T_7d221_level0_row0\" class=\"row_heading level0 row0\" >2010</th>\n",
+       "      <td id=\"T_7d221_row0_col0\" class=\"data row0 col0\" >2</td>\n",
+       "      <td id=\"T_7d221_row0_col1\" class=\"data row0 col1\" >1</td>\n",
+       "      <td id=\"T_7d221_row0_col2\" class=\"data row0 col2\" >0</td>\n",
+       "      <td id=\"T_7d221_row0_col3\" class=\"data row0 col3\" >None</td>\n",
+       "      <td id=\"T_7d221_row0_col4\" class=\"data row0 col4\" >None</td>\n",
+       "      <td id=\"T_7d221_row0_col5\" class=\"data row0 col5\" >None</td>\n",
+       "      <td id=\"T_7d221_row0_col6\" class=\"data row0 col6\" >None</td>\n",
+       "      <td id=\"T_7d221_row0_col7\" class=\"data row0 col7\" >None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_7d221_level0_row1\" class=\"row_heading level0 row1\" >2011</th>\n",
+       "      <td id=\"T_7d221_row1_col0\" class=\"data row1 col0\" >3</td>\n",
+       "      <td id=\"T_7d221_row1_col1\" class=\"data row1 col1\" >2</td>\n",
+       "      <td id=\"T_7d221_row1_col2\" class=\"data row1 col2\" >1</td>\n",
+       "      <td id=\"T_7d221_row1_col3\" class=\"data row1 col3\" >None</td>\n",
+       "      <td id=\"T_7d221_row1_col4\" class=\"data row1 col4\" >None</td>\n",
+       "      <td id=\"T_7d221_row1_col5\" class=\"data row1 col5\" >None</td>\n",
+       "      <td id=\"T_7d221_row1_col6\" class=\"data row1 col6\" >None</td>\n",
+       "      <td id=\"T_7d221_row1_col7\" class=\"data row1 col7\" >None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_7d221_level0_row2\" class=\"row_heading level0 row2\" >2012</th>\n",
+       "      <td id=\"T_7d221_row2_col0\" class=\"data row2 col0\" >4</td>\n",
+       "      <td id=\"T_7d221_row2_col1\" class=\"data row2 col1\" >3</td>\n",
+       "      <td id=\"T_7d221_row2_col2\" class=\"data row2 col2\" >2</td>\n",
+       "      <td id=\"T_7d221_row2_col3\" class=\"data row2 col3\" >0</td>\n",
+       "      <td id=\"T_7d221_row2_col4\" class=\"data row2 col4\" >None</td>\n",
+       "      <td id=\"T_7d221_row2_col5\" class=\"data row2 col5\" >None</td>\n",
+       "      <td id=\"T_7d221_row2_col6\" class=\"data row2 col6\" >None</td>\n",
+       "      <td id=\"T_7d221_row2_col7\" class=\"data row2 col7\" >None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_7d221_level0_row3\" class=\"row_heading level0 row3\" >2013</th>\n",
+       "      <td id=\"T_7d221_row3_col0\" class=\"data row3 col0\" >5</td>\n",
+       "      <td id=\"T_7d221_row3_col1\" class=\"data row3 col1\" >4</td>\n",
+       "      <td id=\"T_7d221_row3_col2\" class=\"data row3 col2\" >3</td>\n",
+       "      <td id=\"T_7d221_row3_col3\" class=\"data row3 col3\" >1</td>\n",
+       "      <td id=\"T_7d221_row3_col4\" class=\"data row3 col4\" >0</td>\n",
+       "      <td id=\"T_7d221_row3_col5\" class=\"data row3 col5\" >None</td>\n",
+       "      <td id=\"T_7d221_row3_col6\" class=\"data row3 col6\" >None</td>\n",
+       "      <td id=\"T_7d221_row3_col7\" class=\"data row3 col7\" >None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_7d221_level0_row4\" class=\"row_heading level0 row4\" >2014</th>\n",
+       "      <td id=\"T_7d221_row4_col0\" class=\"data row4 col0\" >6</td>\n",
+       "      <td id=\"T_7d221_row4_col1\" class=\"data row4 col1\" >5</td>\n",
+       "      <td id=\"T_7d221_row4_col2\" class=\"data row4 col2\" >4</td>\n",
+       "      <td id=\"T_7d221_row4_col3\" class=\"data row4 col3\" >2</td>\n",
+       "      <td id=\"T_7d221_row4_col4\" class=\"data row4 col4\" >1</td>\n",
+       "      <td id=\"T_7d221_row4_col5\" class=\"data row4 col5\" >0</td>\n",
+       "      <td id=\"T_7d221_row4_col6\" class=\"data row4 col6\" >None</td>\n",
+       "      <td id=\"T_7d221_row4_col7\" class=\"data row4 col7\" >None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_7d221_level0_row5\" class=\"row_heading level0 row5\" >2015</th>\n",
+       "      <td id=\"T_7d221_row5_col0\" class=\"data row5 col0\" >7</td>\n",
+       "      <td id=\"T_7d221_row5_col1\" class=\"data row5 col1\" >6</td>\n",
+       "      <td id=\"T_7d221_row5_col2\" class=\"data row5 col2\" >5</td>\n",
+       "      <td id=\"T_7d221_row5_col3\" class=\"data row5 col3\" >3</td>\n",
+       "      <td id=\"T_7d221_row5_col4\" class=\"data row5 col4\" >2</td>\n",
+       "      <td id=\"T_7d221_row5_col5\" class=\"data row5 col5\" >1</td>\n",
+       "      <td id=\"T_7d221_row5_col6\" class=\"data row5 col6\" >0</td>\n",
+       "      <td id=\"T_7d221_row5_col7\" class=\"data row5 col7\" >None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_7d221_level0_row6\" class=\"row_heading level0 row6\" >2016</th>\n",
+       "      <td id=\"T_7d221_row6_col0\" class=\"data row6 col0\" >8</td>\n",
+       "      <td id=\"T_7d221_row6_col1\" class=\"data row6 col1\" >7</td>\n",
+       "      <td id=\"T_7d221_row6_col2\" class=\"data row6 col2\" >6</td>\n",
+       "      <td id=\"T_7d221_row6_col3\" class=\"data row6 col3\" >4</td>\n",
+       "      <td id=\"T_7d221_row6_col4\" class=\"data row6 col4\" >3</td>\n",
+       "      <td id=\"T_7d221_row6_col5\" class=\"data row6 col5\" >2</td>\n",
+       "      <td id=\"T_7d221_row6_col6\" class=\"data row6 col6\" >1</td>\n",
+       "      <td id=\"T_7d221_row6_col7\" class=\"data row6 col7\" >0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_7d221_level0_row7\" class=\"row_heading level0 row7\" >2017</th>\n",
+       "      <td id=\"T_7d221_row7_col0\" class=\"data row7 col0\" >9</td>\n",
+       "      <td id=\"T_7d221_row7_col1\" class=\"data row7 col1\" >8</td>\n",
+       "      <td id=\"T_7d221_row7_col2\" class=\"data row7 col2\" >7</td>\n",
+       "      <td id=\"T_7d221_row7_col3\" class=\"data row7 col3\" >5</td>\n",
+       "      <td id=\"T_7d221_row7_col4\" class=\"data row7 col4\" >4</td>\n",
+       "      <td id=\"T_7d221_row7_col5\" class=\"data row7 col5\" >3</td>\n",
+       "      <td id=\"T_7d221_row7_col6\" class=\"data row7 col6\" >2</td>\n",
+       "      <td id=\"T_7d221_row7_col7\" class=\"data row7 col7\" >1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_7d221_level0_row8\" class=\"row_heading level0 row8\" >2018</th>\n",
+       "      <td id=\"T_7d221_row8_col0\" class=\"data row8 col0\" >10</td>\n",
+       "      <td id=\"T_7d221_row8_col1\" class=\"data row8 col1\" >9</td>\n",
+       "      <td id=\"T_7d221_row8_col2\" class=\"data row8 col2\" >8</td>\n",
+       "      <td id=\"T_7d221_row8_col3\" class=\"data row8 col3\" >6</td>\n",
+       "      <td id=\"T_7d221_row8_col4\" class=\"data row8 col4\" >5</td>\n",
+       "      <td id=\"T_7d221_row8_col5\" class=\"data row8 col5\" >4</td>\n",
+       "      <td id=\"T_7d221_row8_col6\" class=\"data row8 col6\" >3</td>\n",
+       "      <td id=\"T_7d221_row8_col7\" class=\"data row8 col7\" >2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_7d221_level0_row9\" class=\"row_heading level0 row9\" >2019</th>\n",
+       "      <td id=\"T_7d221_row9_col0\" class=\"data row9 col0\" >11</td>\n",
+       "      <td id=\"T_7d221_row9_col1\" class=\"data row9 col1\" >10</td>\n",
+       "      <td id=\"T_7d221_row9_col2\" class=\"data row9 col2\" >9</td>\n",
+       "      <td id=\"T_7d221_row9_col3\" class=\"data row9 col3\" >7</td>\n",
+       "      <td id=\"T_7d221_row9_col4\" class=\"data row9 col4\" >6</td>\n",
+       "      <td id=\"T_7d221_row9_col5\" class=\"data row9 col5\" >5</td>\n",
+       "      <td id=\"T_7d221_row9_col6\" class=\"data row9 col6\" >4</td>\n",
+       "      <td id=\"T_7d221_row9_col7\" class=\"data row9 col7\" >3</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n"
+      ],
+      "text/plain": [
+       "<pandas.io.formats.style.Styler at 0x2344e176820>"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "import numpy as np\n",
     "import pandas as pd\n",
@@ -469,7 +1311,7 @@
     "    'y_lag_3': ts.shift(3),\n",
     "    'y_lag_4': ts.shift(4),\n",
     "    'y_lag_5': ts.shift(5),\n",
-    "    'y_lag_6': ts.shift(6),    \n",
+    "    'y_lag_6': ts.shift(6),\n",
     "})\n",
     "\n",
     "# Multistep targets\n",
@@ -487,7 +1329,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5a242e0a",
+   "id": "ca5c0f92",
    "metadata": {},
    "source": [
     "The above illustrates how a dataset would be prepared similar to the Defining a Forecast figure: a three-step forecasting task with a two-step lead time using five lag features. The original time series is y_step_1. The missing values we could either fill-in or drop.\n",
@@ -501,7 +1343,7 @@
     "Use a model that produces multiple outputs naturally. Linear regression and neural networks can both produce multiple outputs. This strategy is simple and efficient, but not possible for every algorithm you might want to use. XGBoost can't do this, for instance.\n",
     "\n",
     ":::{figure-md} 02_multioutput\n",
-    "<img src=\"../../images/deep-learning/time-series/02_multioutput.png\" width=\"90%\" class=\"bg-white mb-1\">\n",
+    "<img src=\"https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/time-series/02_multioutput.png\" width=\"90%\" class=\"bg-white mb-1\">\n",
     "\n",
     "Multioutput Model\n",
     ":::\n",
@@ -511,7 +1353,7 @@
     "Train a separate model for each step in the horizon: one model forecasts 1-step ahead, another 2-steps ahead, and so on. Forecasting 1-step ahead is a different problem than 2-steps ahead (and so on), so it can help to have a different model make forecasts for each step. The downside is that training lots of models can be computationally expensive.\n",
     "\n",
     ":::{figure-md} 03_direct\n",
-    "<img src=\"../../images/deep-learning/time-series/03_direct.png\" width=\"90%\" class=\"bg-white mb-1\">\n",
+    "<img src=\"https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/time-series/03_direct.png\" width=\"90%\" class=\"bg-white mb-1\">\n",
     "\n",
     "Direct Strategy\n",
     ":::\n",
@@ -521,7 +1363,7 @@
     "Train a single one-step model and use its forecasts to update the lag features for the next step. With the recursive method, we feed a model's 1-step forecast back in to that same model to use as a lag feature for the next forecasting step. We only need to train one model, but since errors will propagate from step to step, forecasts can be inaccurate for long horizons.\n",
     "\n",
     ":::{figure-md} 04_recursive\n",
-    "<img src=\"../../images/deep-learning/time-series/04_recursive.png\" width=\"90%\" class=\"bg-white mb-1\">\n",
+    "<img src=\"https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/time-series/04_recursive.png\" width=\"90%\" class=\"bg-white mb-1\">\n",
     "\n",
     "Recursive Strategy\n",
     ":::\n",
@@ -531,7 +1373,7 @@
     "A combination of the direct and recursive strategies: train a model for each step and use forecasts from previous steps as new lag features. Step by step, each model gets an additional lag input. Since each model always has an up-to-date set of lag features, the DirRec strategy can capture serial dependence better than Direct, but it can also suffer from error propagation like Recursive.\n",
     "\n",
     ":::{figure-md} 05_dirrec\n",
-    "<img src=\"../../images/deep-learning/time-series/05_dirrec.png\" width=\"90%\" class=\"bg-white mb-1\">\n",
+    "<img src=\"https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/time-series/05_dirrec.png\" width=\"90%\" class=\"bg-white mb-1\">\n",
     "\n",
     "DirRec Strategy\n",
     ":::\n",
@@ -547,9 +1389,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "8f529dc6",
-   "metadata": {},
+   "execution_count": 57,
+   "id": "84e0f675",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
    "outputs": [],
    "source": [
     "from pathlib import Path\n",
@@ -600,8 +1449,7 @@
     "    return ax\n",
     "\n",
     "\n",
-    "data_dir = Path(\"../assets/data\")\n",
-    "flu_trends = pd.read_csv(data_dir / \"flu-trends.csv\")\n",
+    "flu_trends = pd.read_csv(\"https://static-1300131294.cos.ap-shanghai.myqcloud.com/data/flu-trends.csv\")\n",
     "flu_trends.set_index(\n",
     "    pd.PeriodIndex(flu_trends.Week, freq=\"W\"),\n",
     "    inplace=True,\n",
@@ -611,7 +1459,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0baff6cd",
+   "id": "39686e25",
    "metadata": {},
    "source": [
     "First we'll prepare our target series (weekly office visits for the flu) for multistep forecasting. Once this is done, training and prediction will be very straightfoward."
@@ -619,9 +1467,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "c6c9120f",
-   "metadata": {},
+   "execution_count": 58,
+   "id": "15456e55",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
    "outputs": [],
    "source": [
     "def make_lags(ts, lags, lead_time=1):\n",
@@ -655,7 +1510,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "69dd4c29",
+   "id": "2d861071",
    "metadata": {},
    "source": [
     "#### Multioutput model\n",
@@ -665,9 +1520,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "1a7a96fb",
-   "metadata": {},
+   "execution_count": 59,
+   "id": "3195c222",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
    "outputs": [],
    "source": [
     "# Create splits\n",
@@ -682,7 +1544,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3f6021eb",
+   "id": "ce708191",
    "metadata": {},
    "source": [
     "Remember that a multistep model will produce a complete forecast for each instance used as input. There are 269 weeks in the training set and 90 weeks in the test set, and we now have an 8-step forecast for each of these weeks."
@@ -690,10 +1552,41 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "734f96dc",
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 60,
+   "id": "7ea55ebf",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Train RMSE: 389.12\n",
+      "Test RMSE: 582.33\n"
+     ]
+    },
+    {
+     "data": {
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+      "text/plain": [
+       "<Figure size 1100x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 590,
+       "width": 1089
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "train_rmse = mean_squared_error(y_train, y_fit, squared=False)\n",
     "test_rmse = mean_squared_error(y_test, y_pred, squared=False)\n",
@@ -711,7 +1604,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d66572cd",
+   "id": "9f9b5977",
    "metadata": {},
    "source": [
     "#### Direct strategy\n",
@@ -721,9 +1614,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "118b2487",
-   "metadata": {},
+   "execution_count": 61,
+   "id": "787ac3fa",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
    "outputs": [],
    "source": [
     "from sklearn.multioutput import MultiOutputRegressor\n",
@@ -737,7 +1637,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "080d1a93",
+   "id": "5a7119a5",
    "metadata": {},
    "source": [
     "XGBoost here is clearly overfitting on the training set. But on the test set it seems it was able to capture some of the dynamics of the flu season better than the linear regression model. It would likely do even better with some hyperparameter tuning."
@@ -745,10 +1645,41 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "ea98afb0",
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 62,
+   "id": "6461dd01",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Train RMSE: 1.22\n",
+      "Test RMSE: 526.45\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1100x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 590,
+       "width": 1089
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "train_rmse = mean_squared_error(y_train, y_fit, squared=False)\n",
     "test_rmse = mean_squared_error(y_test, y_pred, squared=False)\n",
@@ -766,7 +1697,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e1408815",
+   "id": "8ccc637e",
    "metadata": {},
    "source": [
     "To use the DirRec strategy, you would only need to replace $MultiOutputRegressor$ with another scikit-learn wrapper, $RegressorChain$. The Recursive strategy we would need to code ourselves.\n",
@@ -777,76 +1708,29 @@
     "\n",
     "## Acknowledgments\n",
     "\n",
-    "Thanks to [kaggle](https://www.kaggle.com/) for creating the open-source course [Time Series](https://www.kaggle.com/learn/time-series). It inspires the majority of the content in this chapter.\n",
-    "\n",
-    "---\n",
-    "\n",
-    "```{bibliography}\n",
-    ":filter: docname in docnames\n",
-    "```"
+    "Thanks to [kaggle](https://www.kaggle.com/) for creating the open-source course [Time Series](https://www.kaggle.com/learn/time-series). It inspires the majority of the content in this chapter.\n"
    ]
   }
  ],
  "metadata": {
-  "jupytext": {
-   "cell_metadata_filter": "-all",
-   "formats": "md:myst",
-   "text_representation": {
-    "extension": ".md",
-    "format_name": "myst",
-    "format_version": 0.13,
-    "jupytext_version": "1.11.5"
-   }
-  },
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "open-machine-learning-jupyter-book",
    "language": "python",
    "name": "python3"
   },
-  "source_map": [
-   14,
-   38,
-   46,
-   66,
-   70,
-   78,
-   103,
-   111,
-   115,
-   119,
-   124,
-   136,
-   185,
-   191,
-   197,
-   201,
-   215,
-   219,
-   223,
-   229,
-   232,
-   236,
-   248,
-   252,
-   260,
-   264,
-   267,
-   300,
-   331,
-   387,
-   443,
-   447,
-   475,
-   481,
-   490,
-   494,
-   507,
-   513,
-   521,
-   525,
-   538
-  ]
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.16"
+  }
  },
  "nbformat": 4,
  "nbformat_minor": 5
-}
\ No newline at end of file
+}
diff --git a/_sources/deep-learning/time-series.md b/_sources/deep-learning/time-series.md
deleted file mode 100644
index b47351e7cf..0000000000
--- a/_sources/deep-learning/time-series.md
+++ /dev/null
@@ -1,557 +0,0 @@
----
-jupytext:
-  cell_metadata_filter: -all
-  formats: md:myst
-  text_representation:
-    extension: .md
-    format_name: myst
-    format_version: 0.13
-    jupytext_version: 1.11.5
-kernelspec:
-  display_name: Python 3
-  language: python
-  name: python3
----
-
-# Time series
-
-## Overiew
-
-Time series forecasting is a broad field with a long history and forcasting is perhaps the most common application in the real world. Businesses forecast product demand, governments forecast economic and population growth, meteorologists forecast the weather. The understanding of things to come is a pressing need across science, government, and industry (not to mention our personal lives!), and practitioners in these fields are increasingly applying machine learning to address this need.
-
-After finishing this course, you'll know how to:
-
-- engineer features to model the major time series components (trends, seasons, and cycles),
-- visualize time series with many kinds of time series plots,
-- create forecasting hybrids that combine the strengths of complementary models, and
-- adapt machine learning methods to a variety of forecasting tasks.
-
-## What is a Time Series?
-
-The basic object of forecasting is the time series, which is a set of observations recorded over time. In forecasting applications, the observations are typically recorded with a regular frequency, like daily or monthly.
-
-```{seealso}
-kaggle kernels output ryanholbrook/linear-regression-with-time-series -p /path/to/dest
-https://www.kaggle.com/competitions/store-sales-time-series-forecasting
-```
-
-```{code-cell}
-import pandas as pd
-df = pd.read_csv(
-    "../assets/data/book_sales.csv",
-    index_col='Date',
-    parse_dates=['Date'],
-).drop('Paperback', axis=1)
-df.head()
-```
-
-This series records the number of hardcover book sales at a retail store over 30 days. Notice that we have a single column of observations $Hardcover$ with a time index $Date$.
-
-## Linear Regression with Time Series
-
-For the first part of this course, we'll use the linear regression algorithm to construct forecasting models. Linear regression is widely used in practice and adapts naturally to even complex forecasting tasks.
-
-The linear regression algorithm learns how to make a weighted sum from its input features. For two features, we would have:
-
-$target = weight_1 * feature_1 + weight_2 * feature_2 + bias$
-
-During training, the regression algorithm learns values for the parameters $weight_1$, $weight_2$, and $bias$ that best fit the $target$. (This algorithm is often called ordinary least squares since it chooses values that minimize the squared error between the target and the predictions.) The weights are also called regression coefficients and the bias is also called the intercept because it tells you where the graph of this function crosses the y-axis.
-
-### Time-step features
-
-There are two kinds of features unique to time series: time-step features and lag features.
-
-Time-step features are features we can derive directly from the time index. The most basic time-step feature is the time dummy, which counts off time steps in the series from beginning to end.
-
-```{code-cell}
-import numpy as np
-df['Time'] = np.arange(len(df.index))
-df.head()
-```
-
-Linear regression with the time dummy produces the model:
-
-$target = weight * time + bias$
-
-The time dummy then lets us fit curves to time series in a time plot, where Time forms the x-axis.
-
-```{code-cell}
-import matplotlib.pyplot as plt
-import seaborn as sns
-plt.style.use("seaborn-whitegrid")
-plt.rc(
-    "figure",
-    autolayout=True,
-    figsize=(11, 4),
-    titlesize=18,
-    titleweight='bold',
-)
-plt.rc(
-    "axes",
-    labelweight="bold",
-    labelsize="large",
-    titleweight="bold",
-    titlesize=16,
-    titlepad=10,
-)
-%config InlineBackend.figure_format = 'retina'
-
-fig, ax = plt.subplots()
-ax.plot('Time', 'Hardcover', data=df, color='0.75')
-ax = sns.regplot(x='Time', y='Hardcover', data=df, ci=None, scatter_kws=dict(color='0.25'))
-ax.set_title('Time Plot of Hardcover Sales');
-```
-
-Time-step features let you model time dependence. A series is time dependent if its values can be predicted from the time they occured. In the Hardcover Sales series, we can predict that sales later in the month are generally higher than sales earlier in the month.
-
-### Lag features
-
-To make a lag feature we shift the observations of the target series so that they appear to have occured later in time. Here we've created a 1-step lag feature, though shifting by multiple steps is possible too.
-
-```{code-cell}
-df['Lag_1'] = df['Hardcover'].shift(1)
-df = df.reindex(columns=['Hardcover', 'Lag_1'])
-df.head()
-```
-
-Linear regression with a lag feature produces the model: $target = weight * lag + bias$. So lag features let us fit curves to lag plots where each observation in a series is plotted against the previous observation.
-
-```{code-cell}
-fig, ax = plt.subplots()
-ax = sns.regplot(x='Lag_1', y='Hardcover', data=df, ci=None, scatter_kws=dict(color='0.25'))
-ax.set_aspect('equal')
-ax.set_title('Lag Plot of Hardcover Sales');
-```
-
-You can see from the lag plot that sales on one day $(Hardcover)$ are correlated with sales from the previous day $(Lag_1)$. When you see a relationship like this, you know a lag feature will be useful.
-
-More generally, lag features let you model serial dependence. A time series has serial dependence when an observation can be predicted from previous observations. In Hardcover Sales, we can predict that high sales on one day usually mean high sales the next day.
-
-Adapting machine learning algorithms to time series problems is largely about feature engineering with the time index and lags. For most of the course, we use linear regression for its simplicity, but these features will be useful whichever algorithm you choose for your forecasting task.
-
-## Example - Tunnel Traffic
-
-Tunnel Traffic is a time series describing the number of vehicles traveling through the Baregg Tunnel in Switzerland each day from November 2003 to November 2005. In this example, we'll get some practice applying linear regression to time-step features and lag features.
-
-```{code-cell}
-from pathlib import Path
-from warnings import simplefilter
-
-import matplotlib.pyplot as plt
-import numpy as np
-import pandas as pd
-
-simplefilter("ignore")  # ignore warnings to clean up output cells
-
-# Set Matplotlib defaults
-plt.style.use("seaborn-whitegrid")
-plt.rc("figure", autolayout=True, figsize=(11, 4))
-plt.rc(
-    "axes",
-    labelweight="bold",
-    labelsize="large",
-    titleweight="bold",
-    titlesize=14,
-    titlepad=10,
-)
-plot_params = dict(
-    color="0.75",
-    style=".-",
-    markeredgecolor="0.25",
-    markerfacecolor="0.25",
-    legend=False,
-)
-%config InlineBackend.figure_format = 'retina'
-
-
-# Load Tunnel Traffic dataset
-data_dir = Path("../assets/data")
-tunnel = pd.read_csv(data_dir / "tunnel.csv", parse_dates=["Day"])
-
-# Create a time series in Pandas by setting the index to a date
-# column. We parsed "Day" as a date type by using `parse_dates` when
-# loading the data.
-tunnel = tunnel.set_index("Day")
-
-# By default, Pandas creates a `DatetimeIndex` with dtype `Timestamp`
-# (equivalent to `np.datetime64`, representing a time series as a
-# sequence of measurements taken at single moments. A `PeriodIndex`,
-# on the other hand, represents a time series as a sequence of
-# quantities accumulated over periods of time. Periods are often
-# easier to work with, so that's what we'll use in this course.
-tunnel = tunnel.to_period()
-
-tunnel.head()
-```
-
-### Time-step feature
-
-Provided the time series doesn't have any missing dates, we can create a time dummy by counting out the length of the series.
-
-```{code-cell}
-df = tunnel.copy()
-
-df['Time'] = np.arange(len(tunnel.index))
-
-df.head()
-```
-
-The procedure for fitting a linear regression model follows the standard steps for scikit-learn.
-
-```{code-cell}
-from sklearn.linear_model import LinearRegression
-
-# Training data
-X = df.loc[:, ['Time']]  # features
-y = df.loc[:, 'NumVehicles']  # target
-
-# Train the model
-model = LinearRegression()
-model.fit(X, y)
-
-# Store the fitted values as a time series with the same time index as
-# the training data
-y_pred = pd.Series(model.predict(X), index=X.index)
-```
-
-The model actually created is (approximately): $Vehicles = 22.5 * Time + 98176$. Plotting the fitted values over time shows us how fitting linear regression to the time dummy creates the trend line defined by this equation.
-
-```{code-cell}
-ax = y.plot(**plot_params)
-ax = y_pred.plot(ax=ax, linewidth=3)
-ax.set_title('Time Plot of Tunnel Traffic');
-```
-
-### Lag feature
-
-Pandas provides us a simple method to lag a series, the $shift$ method.
-
-```{code-cell}
-df['Lag_1'] = df['NumVehicles'].shift(1)
-df.head()
-```
-
-When creating lag features, we need to decide what to do with the missing values produced. Filling them in is one option, maybe with 0.0 or "backfilling" with the first known value. Instead, we'll just drop the missing values, making sure to also drop values in the target from corresponding dates.
-
-```{code-cell}
-from sklearn.linear_model import LinearRegression
-
-X = df.loc[:, ['Lag_1']]
-X.dropna(inplace=True)  # drop missing values in the feature set
-y = df.loc[:, 'NumVehicles']  # create the target
-y, X = y.align(X, join='inner')  # drop corresponding values in target
-
-model = LinearRegression()
-model.fit(X, y)
-
-y_pred = pd.Series(model.predict(X), index=X.index)
-```
-
-The lag plot shows us how well we were able to fit the relationship between the number of vehicles one day and the number the previous day.
-
-```{code-cell}
-fig, ax = plt.subplots()
-ax.plot(X['Lag_1'], y, '.', color='0.25')
-ax.plot(X['Lag_1'], y_pred)
-ax.set_aspect('equal')
-ax.set_ylabel('NumVehicles')
-ax.set_xlabel('Lag_1')
-ax.set_title('Lag Plot of Tunnel Traffic');
-```
-
-What does this prediction from a lag feature mean about how well we can predict the series across time? The following time plot shows us how our forecasts now respond to the behavior of the series in the recent past.
-
-```{code-cell}
-ax = y.plot(**plot_params)
-ax = y_pred.plot()
-```
-
-The best time series models will usually include some combination of time-step features and lag features. Over the next few lessons, we'll learn how to engineer features modeling the most common patterns in time series using the features from this lesson as a starting point.
-
-## Forecasting With Deep Learning
-
-### Defining the Forecasting Task
-
-There are two things to establish before designing a forecasting model:
-
-- what information is available at the time a forecast is made (features),
-- the time period during which you require forecasted values (target).
-
-The forecast origin is time at which you are making a forecast. Practically, you might consider the forecast origin to be the last time for which you have training data for the time being predicted. Everything up to he origin can be used to create features.
-
-The forecast horizon is the time for which you are making a forecast. We often describe a forecast by the number of time steps in its horizon: a "1-step" forecast or "5-step" forecast, say. The forecast horizon describes the target.
-
-:::{figure-md} 01_def
-<img src="../../images/deep-learning/time-series/01_def.png" width="90%" class="bg-white mb-1">
-
-A three-step forecast horizon with a two-step lead time, using four lag features. The figure represents what would be a single row of training data -- data for a single prediction, in other words. 
-:::
-
-The time between the origin and the horizon is the lead time (or sometimes latency) of the forecast. A forecast's lead time is described by the number of steps from origin to horizon: a "1-step ahead" or "3-step ahead" forecast, say. In practice, it may be necessary for a forecast to begin multiple steps ahead of the origin because of delays in data acquisition or processing.
-
-### Preparing Data for Forecasting
-
-In order to forecast time series with ML algorithms, we need to transform the series into a dataframe we can use with those algorithms. (Unless, of course, you are only using deterministic features like trend and seasonality.)
-
-We saw the first half of this process in Lesson 4 when we created a feature set out of lags. The second half is preparing the target. How we do this depends on the forecasting task.
-
-Each row in a dataframe represents a single forecast. The time index of the row is the first time in the forecast horizon, but we arrange values for the entire horizon in the same row. For multistep forecasts, this means we are requiring a model to produce multiple outputs, one for each step.
-
-```{code-cell}
-import numpy as np
-import pandas as pd
-
-N = 20
-ts = pd.Series(
-    np.arange(N),
-    index=pd.period_range(start='2010', freq='A', periods=N, name='Year'),
-    dtype=pd.Int8Dtype,
-)
-
-# Lag features
-X = pd.DataFrame({
-    'y_lag_2': ts.shift(2),
-    'y_lag_3': ts.shift(3),
-    'y_lag_4': ts.shift(4),
-    'y_lag_5': ts.shift(5),
-    'y_lag_6': ts.shift(6),    
-})
-
-# Multistep targets
-y = pd.DataFrame({
-    'y_step_3': ts.shift(-2),
-    'y_step_2': ts.shift(-1),
-    'y_step_1': ts,
-})
-
-data = pd.concat({'Targets': y, 'Features': X}, axis=1)
-
-data.head(10).style.set_properties(['Targets'], **{'background-color': 'LavenderBlush'}) \
-                   .set_properties(['Features'], **{'background-color': 'Lavender'})
-```
-
-The above illustrates how a dataset would be prepared similar to the Defining a Forecast figure: a three-step forecasting task with a two-step lead time using five lag features. The original time series is y_step_1. The missing values we could either fill-in or drop.
-
-### Multistep Forecasting Strategies
-
-There are a number of strategies for producing the multiple target steps required for a forecast. We'll outline four common strategies, each with strengths and weaknesses.
-
-#### Multioutput model
-
-Use a model that produces multiple outputs naturally. Linear regression and neural networks can both produce multiple outputs. This strategy is simple and efficient, but not possible for every algorithm you might want to use. XGBoost can't do this, for instance.
-
-:::{figure-md} 02_multioutput
-<img src="../../images/deep-learning/time-series/02_multioutput.png" width="90%" class="bg-white mb-1">
-
-Multioutput Model
-:::
-
-#### Direct strategy
-
-Train a separate model for each step in the horizon: one model forecasts 1-step ahead, another 2-steps ahead, and so on. Forecasting 1-step ahead is a different problem than 2-steps ahead (and so on), so it can help to have a different model make forecasts for each step. The downside is that training lots of models can be computationally expensive.
-
-:::{figure-md} 03_direct
-<img src="../../images/deep-learning/time-series/03_direct.png" width="90%" class="bg-white mb-1">
-
-Direct Strategy
-:::
-
-#### Recursive strategy
-
-Train a single one-step model and use its forecasts to update the lag features for the next step. With the recursive method, we feed a model's 1-step forecast back in to that same model to use as a lag feature for the next forecasting step. We only need to train one model, but since errors will propagate from step to step, forecasts can be inaccurate for long horizons.
-
-:::{figure-md} 04_recursive
-<img src="../../images/deep-learning/time-series/04_recursive.png" width="90%" class="bg-white mb-1">
-
-Recursive Strategy
-:::
-
-#### DirRec strategy
-
-A combination of the direct and recursive strategies: train a model for each step and use forecasts from previous steps as new lag features. Step by step, each model gets an additional lag input. Since each model always has an up-to-date set of lag features, the DirRec strategy can capture serial dependence better than Direct, but it can also suffer from error propagation like Recursive.
-
-:::{figure-md} 05_dirrec
-<img src="../../images/deep-learning/time-series/05_dirrec.png" width="90%" class="bg-white mb-1">
-
-DirRec Strategy
-:::
-
-### Example - Flu Trends
-
-In this example we'll apply the MultiOutput and Direct strategies to the Flu Trends data from Lesson 4, this time making true forecasts for multiple weeks beyond the training period.
-
-We'll define our forecasting task to have an 8-week horizon with a 1-week lead time. In other words, we'll be forecasting eight weeks of flu cases starting with the following week.
-
-The hidden cell sets up the example and defines a helper function $plot_multistep$.
-
-```{code-cell}
-from pathlib import Path
-from warnings import simplefilter
-
-import matplotlib.pyplot as plt
-import numpy as np
-import pandas as pd
-import seaborn as sns
-from sklearn.linear_model import LinearRegression
-from sklearn.metrics import mean_squared_error
-from sklearn.model_selection import train_test_split
-from xgboost import XGBRegressor
-
-simplefilter("ignore")
-
-# Set Matplotlib defaults
-plt.style.use("seaborn-whitegrid")
-plt.rc("figure", autolayout=True, figsize=(11, 4))
-plt.rc(
-    "axes",
-    labelweight="bold",
-    labelsize="large",
-    titleweight="bold",
-    titlesize=16,
-    titlepad=10,
-)
-plot_params = dict(
-    color="0.75",
-    style=".-",
-    markeredgecolor="0.25",
-    markerfacecolor="0.25",
-)
-%config InlineBackend.figure_format = 'retina'
-
-
-def plot_multistep(y, every=1, ax=None, palette_kwargs=None):
-    palette_kwargs_ = dict(palette='husl', n_colors=16, desat=None)
-    if palette_kwargs is not None:
-        palette_kwargs_.update(palette_kwargs)
-    palette = sns.color_palette(**palette_kwargs_)
-    if ax is None:
-        fig, ax = plt.subplots()
-    ax.set_prop_cycle(plt.cycler('color', palette))
-    for date, preds in y[::every].iterrows():
-        preds.index = pd.period_range(start=date, periods=len(preds))
-        preds.plot(ax=ax)
-    return ax
-
-
-data_dir = Path("../assets/data")
-flu_trends = pd.read_csv(data_dir / "flu-trends.csv")
-flu_trends.set_index(
-    pd.PeriodIndex(flu_trends.Week, freq="W"),
-    inplace=True,
-)
-flu_trends.drop("Week", axis=1, inplace=True)
-```
-
-First we'll prepare our target series (weekly office visits for the flu) for multistep forecasting. Once this is done, training and prediction will be very straightfoward.
-
-```{code-cell}
-def make_lags(ts, lags, lead_time=1):
-    return pd.concat(
-        {
-            f'y_lag_{i}': ts.shift(i)
-            for i in range(lead_time, lags + lead_time)
-        },
-        axis=1)
-
-
-# Four weeks of lag features
-y = flu_trends.FluVisits.copy()
-X = make_lags(y, lags=4).fillna(0.0)
-
-
-def make_multistep_target(ts, steps):
-    return pd.concat(
-        {f'y_step_{i + 1}': ts.shift(-i)
-         for i in range(steps)},
-        axis=1)
-
-
-# Eight-week forecast
-y = make_multistep_target(y, steps=8).dropna()
-
-# Shifting has created indexes that don't match. Only keep times for
-# which we have both targets and features.
-y, X = y.align(X, join='inner', axis=0)
-```
-
-#### Multioutput model
-
-We'll use linear regression as a MultiOutput strategy. Once we have our data prepared for multiple outputs, training and prediction is the same as always.
-
-```{code-cell}
-# Create splits
-X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, shuffle=False)
-
-model = LinearRegression()
-model.fit(X_train, y_train)
-
-y_fit = pd.DataFrame(model.predict(X_train), index=X_train.index, columns=y.columns)
-y_pred = pd.DataFrame(model.predict(X_test), index=X_test.index, columns=y.columns)
-```
-
-Remember that a multistep model will produce a complete forecast for each instance used as input. There are 269 weeks in the training set and 90 weeks in the test set, and we now have an 8-step forecast for each of these weeks.
-
-```{code-cell}
-train_rmse = mean_squared_error(y_train, y_fit, squared=False)
-test_rmse = mean_squared_error(y_test, y_pred, squared=False)
-print((f"Train RMSE: {train_rmse:.2f}\n" f"Test RMSE: {test_rmse:.2f}"))
-
-palette = dict(palette='husl', n_colors=64)
-fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(11, 6))
-ax1 = flu_trends.FluVisits[y_fit.index].plot(**plot_params, ax=ax1)
-ax1 = plot_multistep(y_fit, ax=ax1, palette_kwargs=palette)
-_ = ax1.legend(['FluVisits (train)', 'Forecast'])
-ax2 = flu_trends.FluVisits[y_pred.index].plot(**plot_params, ax=ax2)
-ax2 = plot_multistep(y_pred, ax=ax2, palette_kwargs=palette)
-_ = ax2.legend(['FluVisits (test)', 'Forecast'])
-```
-
-#### Direct strategy
-
-XGBoost can't produce multiple outputs for regression tasks. But by applying the Direct reduction strategy, we can still use it to produce multi-step forecasts. This is as easy as wrapping it with scikit-learn's $MultiOutputRegressor$.
-
-```{code-cell}
-from sklearn.multioutput import MultiOutputRegressor
-
-model = MultiOutputRegressor(XGBRegressor())
-model.fit(X_train, y_train)
-
-y_fit = pd.DataFrame(model.predict(X_train), index=X_train.index, columns=y.columns)
-y_pred = pd.DataFrame(model.predict(X_test), index=X_test.index, columns=y.columns)
-```
-
-XGBoost here is clearly overfitting on the training set. But on the test set it seems it was able to capture some of the dynamics of the flu season better than the linear regression model. It would likely do even better with some hyperparameter tuning.
-
-```{code-cell}
-train_rmse = mean_squared_error(y_train, y_fit, squared=False)
-test_rmse = mean_squared_error(y_test, y_pred, squared=False)
-print((f"Train RMSE: {train_rmse:.2f}\n" f"Test RMSE: {test_rmse:.2f}"))
-
-palette = dict(palette='husl', n_colors=64)
-fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(11, 6))
-ax1 = flu_trends.FluVisits[y_fit.index].plot(**plot_params, ax=ax1)
-ax1 = plot_multistep(y_fit, ax=ax1, palette_kwargs=palette)
-_ = ax1.legend(['FluVisits (train)', 'Forecast'])
-ax2 = flu_trends.FluVisits[y_pred.index].plot(**plot_params, ax=ax2)
-ax2 = plot_multistep(y_pred, ax=ax2, palette_kwargs=palette)
-_ = ax2.legend(['FluVisits (test)', 'Forecast'])
-```
-
-To use the DirRec strategy, you would only need to replace $MultiOutputRegressor$ with another scikit-learn wrapper, $RegressorChain$. The Recursive strategy we would need to code ourselves.
-
-## Your turn! 🚀
-
-TBD.
-
-## Acknowledgments
-
-Thanks to [kaggle](https://www.kaggle.com/) for creating the open-source course [Time Series](https://www.kaggle.com/learn/time-series). It inspires the majority of the content in this chapter.
-
----
-
-```{bibliography}
-:filter: docname in docnames
-```
-
-
-
diff --git a/_sources/machine-learning-productionization/data-engineering.ipynb b/_sources/machine-learning-productionization/data-engineering.ipynb
index f73299cb9b..4e6960e1fe 100644
--- a/_sources/machine-learning-productionization/data-engineering.ipynb
+++ b/_sources/machine-learning-productionization/data-engineering.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "23bcbf5b",
+   "id": "00f564aa",
    "metadata": {},
    "source": [
     "# Data engineering\n",
diff --git a/_sources/machine-learning-productionization/model-deployment.ipynb b/_sources/machine-learning-productionization/model-deployment.ipynb
index d9f8d71e7b..4bafba9e76 100644
--- a/_sources/machine-learning-productionization/model-deployment.ipynb
+++ b/_sources/machine-learning-productionization/model-deployment.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "b3f45026",
+   "id": "305a6076",
    "metadata": {},
    "source": [
     "# Model deployment\n",
diff --git a/_sources/machine-learning-productionization/model-training-and-evaluation.ipynb b/_sources/machine-learning-productionization/model-training-and-evaluation.ipynb
index 9a52fba795..c5e5966556 100644
--- a/_sources/machine-learning-productionization/model-training-and-evaluation.ipynb
+++ b/_sources/machine-learning-productionization/model-training-and-evaluation.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "b61452bd",
+   "id": "0b9ac086",
    "metadata": {},
    "source": [
     "# Model training & evaluation\n",
diff --git a/_sources/machine-learning-productionization/overview.ipynb b/_sources/machine-learning-productionization/overview.ipynb
index 93575d2840..c58acce29b 100644
--- a/_sources/machine-learning-productionization/overview.ipynb
+++ b/_sources/machine-learning-productionization/overview.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "08e2681b",
+   "id": "6a251ac6",
    "metadata": {},
    "source": [
     "# Overview\n",
@@ -83,7 +83,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "11d51dbf",
+   "id": "10354f57",
    "metadata": {
     "tags": [
      "output_scroll"
@@ -299,7 +299,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e919e432",
+   "id": "213d5139",
    "metadata": {},
    "source": [
     "The above data is publicly available, and able to be automatically and easily sourced by a web crawler. There are also other ingestion approaches, including synthetic data simulation and manual collecting.\n",
@@ -314,7 +314,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "de885765",
+   "id": "202d2dc1",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -343,7 +343,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1896cc65",
+   "id": "dd654789",
    "metadata": {},
    "source": [
     "## Model training & evaluation\n",
diff --git a/_sources/machine-learning-productionization/problem-framing.ipynb b/_sources/machine-learning-productionization/problem-framing.ipynb
index a33d536d4a..0e6979dd5f 100644
--- a/_sources/machine-learning-productionization/problem-framing.ipynb
+++ b/_sources/machine-learning-productionization/problem-framing.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "8a27d14b",
+   "id": "64b086af",
    "metadata": {},
    "source": [
     "# Problem framing\n",
diff --git a/_sources/ml-advanced/ensemble-learning/bagging.ipynb b/_sources/ml-advanced/ensemble-learning/bagging.ipynb
index a8677d5f4c..52e0fa15a6 100644
--- a/_sources/ml-advanced/ensemble-learning/bagging.ipynb
+++ b/_sources/ml-advanced/ensemble-learning/bagging.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "10ef57a2",
+   "id": "268a7358",
    "metadata": {},
    "source": [
     "# Bagging\n",
@@ -58,7 +58,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "280a8721",
+   "id": "a18e03a8",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -75,7 +75,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "3a589c63",
+   "id": "a136b217",
    "metadata": {
     "tags": [
      "output_scroll"
@@ -303,7 +303,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "b993fd84",
+   "id": "6a274cc2",
    "metadata": {},
    "outputs": [
     {
@@ -339,7 +339,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "241b33ef",
+   "id": "17fc3901",
    "metadata": {},
    "source": [
     "Looks like loyal customers make fewer calls to customer service than those who eventually leave. Now, it might be a good idea to estimate the average number of customer service calls in each group. Since our dataset is small, we would not get a good estimate by simply calculating the mean of the original sample. We will be better off applying the bootstrap method. Let's generate 1000 new bootstrap samples from our original population and produce an interval estimate of the mean."
@@ -348,7 +348,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "1e8e20b3",
+   "id": "58f3dca1",
    "metadata": {},
    "outputs": [
     {
@@ -404,7 +404,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b3769743",
+   "id": "437c066e",
    "metadata": {},
    "source": [
     "For the interpretation of confidence intervals, you can address this [concise note](https://www.graphpad.com/guides/prism/7/statistics/stat_more_about_confidence_interval.htm?toc=0&printWindow) or any course on statistics. It's not correct to say that a confidence interval contains 95% of values. Note that the interval for the loyal customers is narrower, which is reasonable since they make fewer calls (0, 1 or 2) in comparison with the churned clients who call until they are fed up and decide to switch providers. \n",
diff --git a/_sources/ml-advanced/ensemble-learning/feature-importance.ipynb b/_sources/ml-advanced/ensemble-learning/feature-importance.ipynb
index 7e199e1cf6..3b757a15b8 100644
--- a/_sources/ml-advanced/ensemble-learning/feature-importance.ipynb
+++ b/_sources/ml-advanced/ensemble-learning/feature-importance.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "7e5e1e79",
+   "id": "aaadf12f",
    "metadata": {},
    "source": [
     "# Feature importance\n",
@@ -159,7 +159,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "0e774629",
+   "id": "bc78b684",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -174,7 +174,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "7cc0556b",
+   "id": "d48cad59",
    "metadata": {
     "tags": [
      "output_scroll"
@@ -269,7 +269,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9656b30a",
+   "id": "7e78b554",
    "metadata": {},
    "source": [
     "Since our aim is just to demonstrate the sequence of steps in calculating feature importances we'll transform the `target` variable as for classifying Iris Virginica One-To-All."
@@ -278,7 +278,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "ff4971bd",
+   "id": "2e899ca7",
    "metadata": {},
    "outputs": [
     {
@@ -311,7 +311,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e2a1ce1f",
+   "id": "c178c36e",
    "metadata": {},
    "source": [
     "Creating Random Forest. For reproducibility, we set `random_state=17`. For the sake of simplicity we set the number of trees to 3 and limit the depth of trees in ensemble to be not greater than 3."
@@ -320,7 +320,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "7c6f9956",
+   "id": "3c4dd4eb",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -332,7 +332,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e4d82f88",
+   "id": "88dd33f1",
    "metadata": {},
    "source": [
     "After fitting list of all the trees are stored in `estimators_` property."
@@ -341,7 +341,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "1eedde3e",
+   "id": "eb2f3699",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -350,7 +350,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "117bcb52",
+   "id": "0640c055",
    "metadata": {},
    "source": [
     "Visualizing trees"
@@ -359,7 +359,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "a4c95033",
+   "id": "355d619d",
    "metadata": {},
    "outputs": [
     {
@@ -393,7 +393,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "0da26b37",
+   "id": "e78d2e00",
    "metadata": {},
    "outputs": [
     {
@@ -425,7 +425,7 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "d08ee865",
+   "id": "e6d62089",
    "metadata": {},
    "outputs": [
     {
@@ -456,7 +456,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "07e7b9c8",
+   "id": "f03a85ab",
    "metadata": {},
    "source": [
     "Let's start from the first tree and `Sepal length (cm)` feature. This feature is located in two nodes: the root (#0) and the rightmost node (#4). The reduction in impurity for these nodes are:\n",
@@ -505,7 +505,7 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "0bb5e55b",
+   "id": "e1a911ca",
    "metadata": {},
    "outputs": [
     {
@@ -525,7 +525,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0305bb06",
+   "id": "5e5abb87",
    "metadata": {},
    "source": [
     "Voila!\n",
@@ -538,7 +538,7 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "88e17dd3",
+   "id": "aa82f8ce",
    "metadata": {},
    "outputs": [
     {
@@ -623,7 +623,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "13d2e190",
+   "id": "73ebd5f3",
    "metadata": {},
    "source": [
     "The picture above shows that, more often than not, customers pay great attention to staff and the price-quality ratio. This couple of factors affects the resulting overall rating the most. The difference between these two features and other features is not very large, so we can conclude that exclusion of any of these features will lead to a reduction of model's accuracy. However, based on our analysis, we can recommend hostel owners to focus primarily on staff training and price-to-quality ratio.\n",
diff --git a/_sources/ml-advanced/ensemble-learning/getting-started-with-ensemble-learning.ipynb b/_sources/ml-advanced/ensemble-learning/getting-started-with-ensemble-learning.ipynb
index 01c9feac30..42bcc81731 100644
--- a/_sources/ml-advanced/ensemble-learning/getting-started-with-ensemble-learning.ipynb
+++ b/_sources/ml-advanced/ensemble-learning/getting-started-with-ensemble-learning.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "4b85cee0",
+   "id": "884bd6c5",
    "metadata": {},
    "source": [
     "# Getting started with ensemble learning\n",
diff --git a/_sources/ml-advanced/ensemble-learning/random-forest.ipynb b/_sources/ml-advanced/ensemble-learning/random-forest.ipynb
index cc23253821..db0c9376b5 100644
--- a/_sources/ml-advanced/ensemble-learning/random-forest.ipynb
+++ b/_sources/ml-advanced/ensemble-learning/random-forest.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "5a13da99",
+   "id": "1cb5e30e",
    "metadata": {},
    "source": [
     "# Random forest\n",
@@ -64,7 +64,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "12f9b6f4",
+   "id": "c826a795",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -93,7 +93,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "ce851b50",
+   "id": "b165f7ee",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -155,7 +155,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "531f9515",
+   "id": "ef9fb746",
    "metadata": {},
    "source": [
     "As we can see from our graphs and the MSE values above, a random forest of 10 trees achieves a better result than a single decision tree and is comparable to bagging with 10 trees. The main difference between random forests and bagging is that, in a random forest, the best feature for a split is selected from a random subset of the available features while, in bagging, all features are considered for the next best split.\n",
@@ -166,7 +166,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "2eb1a2cf",
+   "id": "5a7272e6",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -215,7 +215,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "27fc04ba",
+   "id": "f038ce71",
    "metadata": {},
    "source": [
     "The figures above show that the decision boundary of the decision tree is quite jagged and has a lot of acute angles that suggest overfitting and a weak ability to generalize. We would have trouble making reliable predictions on new test data. In contrast, the bagging algorithm has a rather smooth boundary and has no obvious signs of overfitting.\n",
@@ -268,7 +268,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "2dcc9394",
+   "id": "743a0cf0",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -304,7 +304,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "97c63bef",
+   "id": "4dae6ba3",
    "metadata": {},
    "source": [
     "We have accuracy close to 92%. Now, let's try to improve this result, and take a look at the behavior of the learning curves when we change the basic parameters.\n",
@@ -315,7 +315,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "dd668ae2",
+   "id": "4b320e47",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -350,7 +350,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "cebd6f73",
+   "id": "1f2840d0",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -371,7 +371,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cfde37dc",
+   "id": "63a59ad5",
    "metadata": {},
    "source": [
     "As you can see, when a certain number of trees is reached, our accuracy on the test set is very close to the asymptote. You can decide by yourself which value would be the optimal number of trees for your problem.\n",
@@ -384,7 +384,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "47026e30",
+   "id": "bce72a94",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -427,7 +427,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "516a7e2c",
+   "id": "7f8523f5",
    "metadata": {},
    "source": [
     "Parameter `max_depth` copes well with the regularization of our model and it does not overfit as badly as before. The model accuracy has increased slightly.\n",
@@ -438,7 +438,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "255cd13b",
+   "id": "4f82bb83",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -471,7 +471,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "7e5aba53",
+   "id": "c69c8870",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -490,7 +490,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "aa8c4d86",
+   "id": "c714c228",
    "metadata": {},
    "source": [
     "In this case, we do not see an improvement in accuracy on the validation set, but we significantly reduce the overfitting down to 2% while keeping the accuracy at about 92%.\n",
@@ -501,7 +501,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "35be1cd9",
+   "id": "437b9940",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -545,7 +545,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1ac9c65e",
+   "id": "2aacd060",
    "metadata": {},
    "source": [
     "In our case, the optimal number of features is equal to 10. This is the value at which the best result is achieved.\n",
@@ -556,7 +556,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "e7373cd9",
+   "id": "d44ee72c",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -572,7 +572,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "316eafac",
+   "id": "260d7d60",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -581,7 +581,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "de12096d",
+   "id": "ad15df40",
    "metadata": {},
    "source": [
     "## Variance and Decorrelation\n",
diff --git a/_sources/ml-advanced/gradient-boosting/gradient-boosting-example.ipynb b/_sources/ml-advanced/gradient-boosting/gradient-boosting-example.ipynb
index e171a0e2fe..be888361e2 100644
--- a/_sources/ml-advanced/gradient-boosting/gradient-boosting-example.ipynb
+++ b/_sources/ml-advanced/gradient-boosting/gradient-boosting-example.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "6bedd148",
+   "id": "8bfa60db",
    "metadata": {},
    "source": [
     "# Gradient boosting example\n",
@@ -38,7 +38,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "b733bfd3",
+   "id": "2dd0d7c5",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -56,7 +56,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6bfa2548",
+   "id": "9c06cc69",
    "metadata": {},
    "source": [
     "We will work with a modified version of our familiar titanic dataset. This is a ready-to-train version: doesn't not contains missings and categorical data has been encoded."
@@ -65,7 +65,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "7e416622",
+   "id": "6b30e305",
    "metadata": {
     "tags": [
      "output_scroll"
@@ -282,7 +282,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "4c4d9e5b",
+   "id": "67fe538a",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -298,7 +298,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "fcab4137",
+   "id": "b20b9214",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -310,7 +310,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "490097fa",
+   "id": "ef96aca2",
    "metadata": {},
    "source": [
     "Let's fit a base learner for the data."
@@ -319,7 +319,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "b82353b9",
+   "id": "aaa7e16d",
    "metadata": {},
    "outputs": [
     {
@@ -342,7 +342,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f326bc83",
+   "id": "b8dcf3f0",
    "metadata": {},
    "source": [
     "Now we will construct a gradient booster class implementing the previous algorithm. Let's code it first and then talk a little bit about the details."
@@ -351,7 +351,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "ecff0b1b",
+   "id": "7dbfcb40",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -401,7 +401,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a590dff0",
+   "id": "3e376a38",
    "metadata": {},
    "source": [
     "We intenionally picked a classification problem over a regression because has some additional technical details worth understanding.\n",
@@ -416,7 +416,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "07e368a7",
+   "id": "1e3c11de",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -427,7 +427,7 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "4347cbc9",
+   "id": "7e79cef8",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -437,7 +437,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0c0a2026",
+   "id": "37592968",
    "metadata": {},
    "source": [
     "Let's implement the booster and see how it performs"
@@ -446,7 +446,7 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "fde79987",
+   "id": "3d1e041c",
    "metadata": {},
    "outputs": [
     {
@@ -470,7 +470,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ce667c8c",
+   "id": "dcdb4efa",
    "metadata": {},
    "source": [
     "Nice! We got decent increment in auc and a good amount of f1. To finish this section we'll plot the classic loss vs epochs plot to verify that the loss is decresing with every addition of a new tree."
@@ -479,7 +479,7 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "dd056832",
+   "id": "74309809",
    "metadata": {},
    "outputs": [
     {
@@ -505,7 +505,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7fd6bc00",
+   "id": "fb07d64d",
    "metadata": {},
    "source": [
     "## Your turn! 🚀\n",
diff --git a/_sources/ml-advanced/gradient-boosting/gradient-boosting.ipynb b/_sources/ml-advanced/gradient-boosting/gradient-boosting.ipynb
index cb1150f504..90a0c2d16f 100644
--- a/_sources/ml-advanced/gradient-boosting/gradient-boosting.ipynb
+++ b/_sources/ml-advanced/gradient-boosting/gradient-boosting.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "dbd7f48c",
+   "id": "20b3b5cc",
    "metadata": {},
    "source": [
     "# Gradient Boosting\n",
diff --git a/_sources/ml-advanced/gradient-boosting/introduction-to-gradient-boosting.ipynb b/_sources/ml-advanced/gradient-boosting/introduction-to-gradient-boosting.ipynb
index 6c376e4fd8..5106e6324e 100644
--- a/_sources/ml-advanced/gradient-boosting/introduction-to-gradient-boosting.ipynb
+++ b/_sources/ml-advanced/gradient-boosting/introduction-to-gradient-boosting.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "08e13efc",
+   "id": "8c90612b",
    "metadata": {},
    "source": [
     "# Introduction to Gradient Boosting\n",
diff --git a/_sources/ml-advanced/gradient-boosting/xgboost-k-fold-cv-feature-importance.ipynb b/_sources/ml-advanced/gradient-boosting/xgboost-k-fold-cv-feature-importance.ipynb
index 0806c6d82d..b5ae508f01 100644
--- a/_sources/ml-advanced/gradient-boosting/xgboost-k-fold-cv-feature-importance.ipynb
+++ b/_sources/ml-advanced/gradient-boosting/xgboost-k-fold-cv-feature-importance.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "c7951b8d",
+   "id": "6657aed4",
    "metadata": {},
    "source": [
     "# XGBoost + k-fold CV + Feature Importance\n",
@@ -221,7 +221,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "94f2baeb",
+   "id": "b046c305",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -232,7 +232,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "260be9f0",
+   "id": "698181e1",
    "metadata": {},
    "source": [
     "### Read dataset"
@@ -241,7 +241,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "ec7d7b65",
+   "id": "59c66244",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -252,7 +252,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ba06b5a9",
+   "id": "60a28431",
    "metadata": {},
    "source": [
     "### EDA\n",
@@ -265,7 +265,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "29fa7d55",
+   "id": "3c987754",
    "metadata": {},
    "outputs": [
     {
@@ -285,7 +285,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "33bb5576",
+   "id": "51154e32",
    "metadata": {},
    "source": [
     "We can see that there are 440 instances and 8 attributes in the dataset.\n",
@@ -296,7 +296,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "a80d53a0",
+   "id": "4d45ff0d",
    "metadata": {
     "tags": [
      "output_scroll"
@@ -414,7 +414,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "168314c8",
+   "id": "6f79cab7",
    "metadata": {},
    "source": [
     "- We can see that `Channel` variable contains values as 1 and 2. \n",
@@ -429,7 +429,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "1f4ae913",
+   "id": "a54ba8ed",
    "metadata": {},
    "outputs": [
     {
@@ -460,7 +460,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fcafc415",
+   "id": "4d7e1a38",
    "metadata": {},
    "source": [
     "#### Summary statistics of dataset"
@@ -469,7 +469,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "33fefa76",
+   "id": "f5210d9c",
    "metadata": {
     "tags": [
      "output_scroll"
@@ -633,7 +633,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "708782f0",
+   "id": "3ea0954f",
    "metadata": {},
    "source": [
     "#### Check for missing values"
@@ -642,7 +642,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "5e2d582c",
+   "id": "c2b67a2d",
    "metadata": {},
    "outputs": [
     {
@@ -670,7 +670,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cb697541",
+   "id": "d6b55dda",
    "metadata": {},
    "source": [
     "We can see that there are no missing values in the dataset.\n",
@@ -681,7 +681,7 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "079ef06a",
+   "id": "615b7bc8",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -692,7 +692,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "024c4695",
+   "id": "32c8f5f3",
    "metadata": {},
    "source": [
     "- Now, let's take a look at feature vector(X) and target variable(y)."
@@ -701,7 +701,7 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "577623e5",
+   "id": "4431d24a",
    "metadata": {
     "tags": [
      "output_scroll"
@@ -814,7 +814,7 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "30df3206",
+   "id": "a483c616",
    "metadata": {},
    "outputs": [
     {
@@ -839,7 +839,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8392b866",
+   "id": "4fc08685",
    "metadata": {},
    "source": [
     "- We can see that the y label contain values as 1 and 2. \n",
@@ -852,7 +852,7 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "id": "c1c939de",
+   "id": "6035c407",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -866,7 +866,7 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "id": "4702cba3",
+   "id": "bb6d0f53",
    "metadata": {},
    "outputs": [
     {
@@ -893,7 +893,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5ce707de",
+   "id": "e208b628",
    "metadata": {},
    "source": [
     "- Now, we will convert the dataset into an optimized data structure called **Dmatrix** that XGBoost supports and gives it acclaimed performance and efficiency gains. \n",
@@ -904,7 +904,7 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "id": "f1a91567",
+   "id": "5f97db11",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -918,7 +918,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "af8678c2",
+   "id": "c736bcfc",
    "metadata": {},
    "source": [
     "### Split data into separate training and test set"
@@ -927,7 +927,7 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "id": "4361609a",
+   "id": "d455869d",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -939,7 +939,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a93eff72",
+   "id": "ddd2e889",
    "metadata": {},
    "source": [
     "### Train the XGBoost Classifier\n",
@@ -956,7 +956,7 @@
   {
    "cell_type": "code",
    "execution_count": 15,
-   "id": "b60add82",
+   "id": "86587eec",
    "metadata": {},
    "outputs": [
     {
@@ -1025,7 +1025,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "62edb03e",
+   "id": "a31b1a15",
    "metadata": {},
    "source": [
     "we can view the parameters of the xgb trained model as follows:"
@@ -1034,7 +1034,7 @@
   {
    "cell_type": "code",
    "execution_count": 16,
-   "id": "eb1543f7",
+   "id": "c73a12bd",
    "metadata": {},
    "outputs": [
     {
@@ -1060,7 +1060,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fe816689",
+   "id": "3015d151",
    "metadata": {},
    "source": [
     "### Make predictions with XGBoost classifier"
@@ -1069,7 +1069,7 @@
   {
    "cell_type": "code",
    "execution_count": 17,
-   "id": "d2a444e7",
+   "id": "4a7860bf",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1080,7 +1080,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "61875a9b",
+   "id": "16f84476",
    "metadata": {},
    "source": [
     "### Check accuracy score"
@@ -1089,7 +1089,7 @@
   {
    "cell_type": "code",
    "execution_count": 18,
-   "id": "1a48b9d2",
+   "id": "e0e4a614",
    "metadata": {},
    "outputs": [
     {
@@ -1110,7 +1110,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "11df076d",
+   "id": "b5f2a906",
    "metadata": {},
    "source": [
     "We can see that XGBoost obtain accuracy score of 88.64%.\n",
@@ -1145,7 +1145,7 @@
   {
    "cell_type": "code",
    "execution_count": 19,
-   "id": "24fafff6",
+   "id": "9b09d991",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1160,7 +1160,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bd6b5e94",
+   "id": "50e25d7c",
    "metadata": {},
    "source": [
     "- **xgb_cv** contains train and test auc metrics for each boosting round. \n",
@@ -1171,7 +1171,7 @@
   {
    "cell_type": "code",
    "execution_count": 20,
-   "id": "392698de",
+   "id": "7ce88597",
    "metadata": {
     "tags": [
      "output_scroll"
@@ -1265,7 +1265,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6db8b585",
+   "id": "0f62c94b",
    "metadata": {},
    "source": [
     "## Feature importance with XGBoost\n",
@@ -1282,7 +1282,7 @@
   {
    "cell_type": "code",
    "execution_count": 21,
-   "id": "d9ccc9ff",
+   "id": "543e6775",
    "metadata": {},
    "outputs": [
     {
@@ -1317,7 +1317,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "69e21d09",
+   "id": "15897f0e",
    "metadata": {},
    "source": [
     "- We can see that the feature `Delicassesn` has been given the highest importance score among all the features. \n",
diff --git a/_sources/ml-advanced/gradient-boosting/xgboost.ipynb b/_sources/ml-advanced/gradient-boosting/xgboost.ipynb
index f3c9232487..0a40b770a9 100644
--- a/_sources/ml-advanced/gradient-boosting/xgboost.ipynb
+++ b/_sources/ml-advanced/gradient-boosting/xgboost.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "6ff2d4c3",
+   "id": "f1b5d970",
    "metadata": {},
    "source": [
     "# XGBoost\n",
@@ -40,7 +40,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "7b378bfa",
+   "id": "cb610abf",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -63,7 +63,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "92af61f8",
+   "id": "0e042778",
    "metadata": {},
    "source": [
     "We build and fit a model just as we would in scikit-learn."
@@ -72,7 +72,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "35dff470",
+   "id": "bc3f49b2",
    "metadata": {},
    "outputs": [
     {
@@ -126,7 +126,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a370e9c5",
+   "id": "8aff5cf8",
    "metadata": {},
    "source": [
     "We similarly evaluate a model and make predictions as we would do in scikit-learn."
@@ -135,14 +135,14 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "c8910edf",
+   "id": "72bc906b",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Mean Absolute Error : 17770.93957619863\n"
+      "Mean Absolute Error : 19357.02450770548\n"
      ]
     }
    ],
@@ -156,7 +156,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c8c94a86",
+   "id": "1ada172e",
    "metadata": {},
    "source": [
     "## Model Tuning\n",
@@ -179,7 +179,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "0c28d3ab",
+   "id": "871369e4",
    "metadata": {},
    "outputs": [
     {
@@ -231,7 +231,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b99c59b3",
+   "id": "307dffd9",
    "metadata": {},
    "source": [
     "When using `early_stopping_rounds`, you need to set aside some of your data for checking the number of rounds to use.  If you later want to fit a model with all of your data, set `n_estimators` to whatever value you found to be optimal when run with early stopping.\n",
@@ -251,7 +251,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "d74871fa",
+   "id": "ff8e58d2",
    "metadata": {},
    "outputs": [
     {
@@ -303,7 +303,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "10f61512",
+   "id": "cb60d360",
    "metadata": {},
    "source": [
     "### `n_jobs`\n",
diff --git a/_sources/ml-advanced/kernel-method.ipynb b/_sources/ml-advanced/kernel-method.ipynb
index cf1bfb25fe..e64a2dd608 100644
--- a/_sources/ml-advanced/kernel-method.ipynb
+++ b/_sources/ml-advanced/kernel-method.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "b293bf64",
+   "id": "7394849d",
    "metadata": {},
    "source": [
     "# Kernel method\n",
@@ -21,7 +21,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "e0c06221",
+   "id": "a543813a",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -38,7 +38,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "fc789e8b",
+   "id": "be60f8d3",
    "metadata": {},
    "outputs": [
     {
@@ -65,7 +65,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2360286b",
+   "id": "4b3e880d",
    "metadata": {},
    "source": [
     "A linear discriminant classifier may draw straight lines to separate two sets of data, but there may be multiple split lines that perfectly distinguish between the two categories. \n",
@@ -76,7 +76,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "9dd10274",
+   "id": "eeb41a90",
    "metadata": {},
    "outputs": [
     {
@@ -107,7 +107,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "acc25d71",
+   "id": "18a96bb8",
    "metadata": {},
    "source": [
     "All three separators are good if we want to distinguish between these samples. But they have some significant differences. In the following, I will explain these separators with an example to show the difference between them. Now, suppose you want a new data point, which is marked with an \"X\" in this graph. So, which separator would you choose? This is a very interesting question! Because you can choose to give this new data point a different label. It's up to you!\n",
@@ -123,7 +123,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "be3a9c23",
+   "id": "92599c2e",
    "metadata": {},
    "outputs": [
     {
@@ -156,7 +156,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1a5ec23e",
+   "id": "b02827ff",
    "metadata": {},
    "source": [
     "In SVM, the best models are those that maximise this margin, and SVM is an optimal model that achieves the maximum margin and can effectively use the boundary information in the dataset to predict the distribution of new data points, thereby improving prediction accuracy. Because of its excellent fitting ability, SVM can also be used to handle large amounts of unbalanced data.\n",
@@ -189,7 +189,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "7d46eda0",
+   "id": "81027cc6",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -200,7 +200,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3b456d93",
+   "id": "4767e516",
    "metadata": {},
    "source": [
     "Let's create a quick and easy function to draw the SVM decision boundary:"
@@ -209,7 +209,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "be3dbeb3",
+   "id": "5ae2f807",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -244,12 +244,12 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "2d15aeb4",
+   "id": "74edf4e2",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -269,7 +269,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b4231b9a",
+   "id": "52db047e",
    "metadata": {},
    "source": [
     "This is a maximum expansion of the space between the two points. Note that some of the practice points just reach the boundary and are marked with black circles. Those points are important in the matching process and are called, support vectors, which is the name of this algorithm. In Skeeter-learning, the identification of these points is stored in the support_vectors_ property of the classifier at:"
@@ -278,7 +278,7 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "c0a79042",
+   "id": "d9c6d220",
    "metadata": {},
    "outputs": [
     {
@@ -300,7 +300,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "69775e2f",
+   "id": "47db9b1e",
    "metadata": {},
    "source": [
     "One of the main points of this classification method is that just the space in which the support vector is located has a large effect on the matching result; on the correct side, no point away from the boundary can correct the match. This is due to the fact that during the fitting process those points do not affect the loss function used in the model. Theoretically, this is due to the fact that during the fitting process, those points do not affect the loss function used in the fitting process, and therefore, if they do not cross the boundary, then their location and number are irrelevant.\n",
@@ -311,12 +311,12 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "bacc462a",
+   "id": "1286b729",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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       "text/plain": [
        "<Figure size 1600x600 with 2 Axes>"
       ]
@@ -353,7 +353,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3de39409",
+   "id": "bb4d5dc3",
    "metadata": {},
    "source": [
     " One of the strengths of the SVM model is that it is insensitive to the exact behaviour of distant points, as can be seen from the comparison between the two figures, and increases the number of training points without affecting the classification results of the model.\n",
@@ -364,13 +364,13 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "5af3dc72",
+   "id": "7cc02337",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "1cf50a7e3fda45e683a7ed36129bdadd",
+       "model_id": "fa4b9e11a211451f910376019ccbafa8",
        "version_major": 2,
        "version_minor": 0
       },
@@ -389,7 +389,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0e22fbf1",
+   "id": "344baad6",
    "metadata": {},
    "source": [
     "## Beyond linear boundaries: Kernel SVM\n",
@@ -413,12 +413,12 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "id": "1beacb5e",
+   "id": "7469c8b7",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -443,7 +443,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cfdc16ed",
+   "id": "f8194f14",
    "metadata": {},
    "source": [
     "It is possible to project the data into higher dimensions, so that a linear partition line is sufficient. As an example, we can use a simple projection to compute a radial basis function with the centre on the middle block as follows:"
@@ -452,7 +452,7 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "id": "b58eb30f",
+   "id": "fb5d4f9e",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -461,7 +461,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a6ff7957",
+   "id": "bd1684df",
    "metadata": {},
    "source": [
     "This additional dimension of data can be visualised in a 3D diagram.:"
@@ -470,13 +470,13 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "id": "70ddfe4a",
+   "id": "7df99896",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "4396863816b74d4a96478dc1a1cda72b",
+       "model_id": "40b3631e052d4f45a2405421746e81f4",
        "version_major": 2,
        "version_minor": 0
       },
@@ -505,7 +505,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9043ba45",
+   "id": "a6f16574",
    "metadata": {},
    "source": [
     "Next we describe how to select and adjust the radial basis function centres.\n",
@@ -516,7 +516,7 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "id": "76b68df3",
+   "id": "fcb3e534",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -527,12 +527,12 @@
   {
    "cell_type": "code",
    "execution_count": 15,
-   "id": "1e377aba",
+   "id": "28d32446",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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+       "grid_auto_flow": null,
+       "grid_auto_rows": null,
+       "grid_column": null,
+       "grid_gap": null,
+       "grid_row": null,
+       "grid_template_areas": null,
+       "grid_template_columns": null,
+       "grid_template_rows": null,
+       "height": null,
+       "justify_content": null,
+       "justify_items": null,
+       "left": null,
+       "margin": null,
+       "max_height": null,
+       "max_width": null,
+       "min_height": null,
+       "min_width": null,
+       "object_fit": null,
+       "object_position": null,
+       "order": null,
+       "overflow": null,
+       "overflow_x": null,
+       "overflow_y": null,
+       "padding": null,
+       "right": null,
+       "top": null,
+       "visibility": null,
+       "width": null
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-     "4f1b4cbdc5c143ccadbae13801b81d2b": {
+     "7e321030c98a4e6ea5a9d5bba8fc94fd": {
       "model_module": "@jupyter-widgets/base",
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       "model_name": "LayoutModel",
@@ -904,32 +954,23 @@
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-     "517294c2a73c4071853c887c7991fd94": {
+     "9699938620b041b39e2e7a0699ed8b60": {
       "model_module": "@jupyter-widgets/controls",
       "model_module_version": "1.5.0",
-      "model_name": "DropdownModel",
+      "model_name": "SliderStyleModel",
       "state": {
-       "_dom_classes": [],
        "_model_module": "@jupyter-widgets/controls",
        "_model_module_version": "1.5.0",
-       "_model_name": "DropdownModel",
-       "_options_labels": [
-        "10",
-        "200"
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+       "_model_name": "SliderStyleModel",
        "_view_count": null,
-       "_view_module": "@jupyter-widgets/controls",
-       "_view_module_version": "1.5.0",
-       "_view_name": "DropdownView",
-       "description": "N",
-       "description_tooltip": null,
-       "disabled": false,
-       "index": 0,
-       "layout": "IPY_MODEL_4f1b4cbdc5c143ccadbae13801b81d2b",
-       "style": "IPY_MODEL_afa055db038f446eb2a92f05d75a9040"
+       "_view_module": "@jupyter-widgets/base",
+       "_view_module_version": "1.2.0",
+       "_view_name": "StyleView",
+       "description_width": "",
+       "handle_color": null
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      },
-     "6fc1130ed0e94536bd4d830852e25390": {
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       "model_module_version": "1.2.0",
       "model_name": "LayoutModel",
@@ -981,7 +1022,7 @@
        "width": null
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-     "801d8ad033064bf787e41e91d3640f28": {
+     "b1c33466ad7341b0988201215511e61e": {
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       "model_module_version": "1.2.0",
       "model_name": "LayoutModel",
@@ -1033,7 +1074,22 @@
        "width": null
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      },
-     "99f5916181d94cf785422b3b7c90103d": {
+     "ccdd032dd754402385b0cf1dd57a6410": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "1.5.0",
+      "model_name": "DescriptionStyleModel",
+      "state": {
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "1.5.0",
+       "_model_name": "DescriptionStyleModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/base",
+       "_view_module_version": "1.2.0",
+       "_view_name": "StyleView",
+       "description_width": ""
+      }
+     },
+     "de335f9e901a4400b87ea08b6a0434ec": {
       "model_module": "@jupyter-widgets/base",
       "model_module_version": "1.2.0",
       "model_name": "LayoutModel",
@@ -1085,32 +1141,7 @@
        "width": null
       }
      },
-     "a296b3b385464869b0bc237ea58a23d9": {
-      "model_module": "@jupyter-widgets/controls",
-      "model_module_version": "1.5.0",
-      "model_name": "DropdownModel",
-      "state": {
-       "_dom_classes": [],
-       "_model_module": "@jupyter-widgets/controls",
-       "_model_module_version": "1.5.0",
-       "_model_name": "DropdownModel",
-       "_options_labels": [
-        "-90",
-        "90"
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-       "_view_count": null,
-       "_view_module": "@jupyter-widgets/controls",
-       "_view_module_version": "1.5.0",
-       "_view_name": "DropdownView",
-       "description": "elev",
-       "description_tooltip": null,
-       "disabled": false,
-       "index": 0,
-       "layout": "IPY_MODEL_6fc1130ed0e94536bd4d830852e25390",
-       "style": "IPY_MODEL_fb678518733b4e349920ca8f3e41ad0e"
-      }
-     },
-     "ac52743ada7448c98d8e7f41e7652da2": {
+     "e6e37a37ba5d40abafef620f2cbf37cf": {
       "model_module": "@jupyter-widgets/output",
       "model_module_version": "1.0.0",
       "model_name": "OutputModel",
@@ -1123,12 +1154,12 @@
        "_view_module": "@jupyter-widgets/output",
        "_view_module_version": "1.0.0",
        "_view_name": "OutputView",
-       "layout": "IPY_MODEL_99f5916181d94cf785422b3b7c90103d",
+       "layout": "IPY_MODEL_b1c33466ad7341b0988201215511e61e",
        "msg_id": "",
        "outputs": [
         {
          "data": {
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",
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DuSxgtQL5PKoBLL75rZj73/8MvbbO7CqJqARks4uIxWJQlAiGh4eg6zqAwt0+brcbmqYZO9NWV9eYWCmVAwYWWr18Ht73HoLjZ4ch6Es7Ri5fcwbgOPwT1PSomPnJz6FXec2rk4hKws9+9ggGBvqMr1taWiFJYfj9QTidTvMKo7LEwEKr5vzxD+F85OGTPi7k87AqCtyf/2cs3MEeUkRbyfT0MchyBLt374HHU1h/EgyGMDsbhyiGIYoSvN5qk6ukcsbAQqvm+vL/gW6xQtDyJ/0eQcvD/bWvYOGjHwPs9g2sjog22sJCEtGoCkWJYHJyEgDgdruxd+95AIBQSIQoSlyXQuuCgYVWR9dhf+43pwwryyzTx2Dt70N+qacHEW0euVwOfX0xyLKMoaEBaFrh8rDVakVnpw+NjU3G9y6vTyFaDwwstDq6Dmhn0Ol0aW0LEW0u+Xwejz/+qLFnSnPzNohiGIFAEG632+TqaDNjYKHVsViQDwRh7YlCWFrpfzK6y4X80q6URFS+ZmbiUBQZ8Xgcb3rT1QAAp9OJHTt2wW63QxQlo88PUbExsNCqpd77AVT+7d+c8nt0qxXpG28BuOkTUVlKpVLo6YlCUSIYHx8zxuPxOGprC+Hk4osvNas82sIYWGjV0jfeAvdXv1w4y5J/7VoW3WqFXlWFhf/rIxtfHBGdlbGxURw58jz6+/uQX5rfFosF7e2dkCTuPEvmY2Ch1ausxMz3f4zqQzfC/vzvodtsQC4HYWnzOK2lFbMPfA9ap8/sSonoNHRdRz6fh81W+DOQTCYRi/UAABoaGiGKEkIhERUVHjPLJDIwsNAZ0ZubMXP4cdifeRrOB78D6+QEHA11mL/qGqQO/Alg468UUSlLJBJLW+RHEAyKuOCCfQAAn68L5513PoLBEOrrG0yukui1+NeFzpwgILv/YmT3XwybzQJHrQfZeBLIncFdRES0YTKZDGKxKGQ5gtHREWO8tzdmBBabzYZ9+/abVSLRaTGwEBFtYk888RgURTZuQxYEAW1t7RDFMPx+v8nVEa0eAwsR0Sah6zri8WnU1tYZu8tqmo5cLofa2jpIUhihEBfQUnliYCEiKnPz8/NQVQWKEsH09DFcd90NaGpqBgDs3Xsedu7chcbGJm6RT2WNgYWIqAxls4uIxWJQVRlDQ4PQlzZ0tNlsmJ4+ZgSW5b1TiModAwsRUZmZnj6Ghx76HrLZrDHW0tIKUZQQCITgdDpNrI6oOBhYiIhK3PT0MczNzcHn6wIA1NTUwuFwoKKiAqIYhihK8HqrzS2SqMgYWIiISlAqtWCsS5mcnITH48E73/kXsFgssFgseNvbrkdlZSXXpdCWwcBCRFQistks+vv7oCgRDA4OQFvqkG6xWNDY2IRMJg23uwIAUFVVZWapRBuOgYWIqET89rfP4MiRF4yvm5qaIYphBIMhuN1u8wojKgEMLEREJpiZiUNVZXR2dqG5eRsAIBAQ0dPTY+yXwjt8iF7BwEJEtEHS6TSiURWKEsH4+BgAYH4+aQSWpqYmvPOd7+K6FKITYGAhIioiXdfR19cLRYmgv78P+XweQGGL/I4On3Hnz/IYEZ0YAwsRUZE9/fR/IpGYBQA0NDRCFCWEQiIqKjwmV0ZUPhhYiE4mmYTzZz+FZXICmrcaiwffBL2hweyqqIQlEgmoqozBwX68+923AiicNdm9ew/m5uYgSWHU1/N3iGgtGFiIXi2fR8U//k+4/88XYEkmoVssEDQNus2G9NtvRPJ/fg56JW8ppYLFxUXEYlHIcgQjI8MAAItFQG9vLxoaWgEA55yzx8wSiTYFBhai4+k6qm77IJwPfQ/CUm8WYWkvDCGXg+t734bt5Zcw88OfAh6ezt/K4vE4nnvuWfT19SKXywEonE1pa2vH9u1hdHZ2YmEhZ3KVRJsHAwvRcRyHH4br+9896eNCPg/bH/+Aii/+CxY++rENrIzMpus6crkc7Ha78XU0qgIAamvrIElhBIMiqqqqYLNZ4HQ6GViI1hEDC9Fx3F/+InSrFcLSnRwnImga3Pd/CQt//VHAxim02SWT81CUwhb5dXV1uPLKqwAAdXV1uOii16OtrR2NjU28w4eoyPhuS7RM12H/9VOnDCvLLJMTsPb1Ih8MbUBhtNGy2UX09hZuRR4aGoS+dHkwmZxHLpeDbSmonnvu68wsk6gsZLNZ9PXFoCgybrrpOlitrjU9z4YHlmQyiauvvhrj4+N48MEHcc4552x0CUQnt4qwYlhcLF4dZJrf/vZZHDnyPLLZrDHW0tIKUZTg9weNsEJEJ6frOkZGhqEoMmKxKBaX3i+z2SxcrjIJLF/4wheMjZOISoogIN/VDWtfr7Hg9mR0uwNaR8cGFUbFND09jaqqStjtDgCAzWZHNptFdXU1RDEMUZTg9VabXCVReYjH41CUCBRFxvz8nDHu9VZDFEW4XGvvibWhgaWnpwf/9m//ho997GP41Kc+tZEvTbQq6Xe/D55P/fdTfo9utSJ93Q3Qq7wbVBWtt1RqAdGoClmOYHJyAgcOXAlJCgMAwuEwtm3bhm3bWrguhWgVUqmU0XJiYmLcGHc6nQgEggiFJLS0tEIQBFgsljW/zoYGls985jO46aab0N3dvZEvS7Rq6VsOwf2V+2AZGjzhWhbdYoXuciH117ebUB2djVwuZ2yRPzg4AG3pdnWLxWLsQgsAbncF3O4Ks8okKgu5XA79/X1QlAgGBvpXzKeOjk6IYhhdXd3regl1wwLL4cOHoSgK7rnnHrz00kvr8pxW69qTGq2P5WOwaY5FbQ3m/uMwqm54G6yRo8YdQ8bmcXW1mP/WgxAkqeRWrG+6Y7GOMpkMHnjg60in08ZYS8s241Zkt3vtp6lPhMeidPBYrB9d1zE2NgZZjqCnR10xn7Zta4YkbUcwGEJFxckD/9mctNyQ99xUKoW7774bt99+OyorK9fteb3e9X2TobXbVMeiVgL++CLwyCMQvvlNYHgYQm0t8Pa3w3L99fCuccHYRtlUx2KN4vE4RkZGsHPnzqURDzo6WjE7O4vt27dj586dqKurK3odPBalg8di7WZmZvDyyy/j6NGjmJmZAVAIHo2NdcZ8qq+vL3odGxJY7r33XtTX1+O6665b1+dNJFLI57V1fU46M1arBV6ve3Mei/1vKPzveKk8kEqaU89pbOpjsQrpdNpYlzI2NgqLxYKamkajweAllxyA2+021qXE48U7jlv9WJQSHou1yWQyxnwaHR0xxu12OwKBIERRQltbu7EmZbXzqbraveZ1LEUPLMPDw7j//vvx+c9/HnNzhRXDCwsLxv8nk0l41rjFeT6vIZfjL2Ap4LEoHVvpWOTzeQwM9ENRIujv7zPuQBQEAa2t7Ugm03A4Cp+sHQ4X8nkdwKnvAFvf+rbOsSh1PBanl8/nMTjYD0VR0N+/suVEe3sHQiEJfr/fuKNO02CsXVmt09yAeUpFDyxDQ0PIZrP4wAc+8JrHDh06hD179uC73z35VuhERCcjy0fxxBOPG1/X1zdAFCWEQiI8nvW7/Ey0Wem6jsnJCSiKjGhURSq1YDxWV1cPUQwjFBLXdTnHWhU9sGzfvh3f+MY3VowdPXoUn/3sZ3HXXXdx4zgiWpVEIgFVlVFdXYPg0g7Dfn8Qv//97+D3ByCKEhoaGk2ukqg8zM3NQVVlKIqMeHzaGK+oqEAoJEIUw6ivbyipW/uLHli8Xi/27dt3wsd27tx53KI4IqKVFhcXEYtFIcsRjIwMAwCam7cZgcXlcuGWWw6V1JsqUakqzKceKEphPi23nLDZbOjq6oYohtHR0XlWe6UUU6ndmUlEhMHBAcjyUfT2xlZcR29tbYMohqHruhFSGFaITk7TNAwNDUJVZcRiMeRyr7ScaG1tgySF0d0dgNPpNLHK1TElsOzbtw+yLJvx0kRUBl588Qj6+/sAALW1tUvX0SVUVVWZWxhRmTh2bAqyHEE0qiCZfOUOnpqaGmM+eb3ltVs3z7AQkWmSyXmoqgJVVfCmN73ZeAPduXPXUu8RCY2NTTyLQrQKCwtJqKoCRZExNTVpjLtcLgQCIUjSdjQ1le98YmAhog2VzS6it7ewRf7Q0KBxHV1VZbzudRcAAHy+bvh8bOFBdDrZbBZ9fTEoiozBwQFjPlmtVvh8XRDFMDo7fbBarSZXevYYWIhoQyST83j22WcQi0WRzb5yHb2lpRWiKMHvD5pYHVH50HUdIyPDUBQZsVgUi4uLxmPbtrVAFCUEAiG4SnxX7jPFwEJERbO4uAiHo7DJlMPhMMLK8uUeUZRQXV1jbpFEZSIej0NRIlAUGfPzc8Z4VZUXkiQhFJJQU1NrYoXFxcBCROsqlVowtvTO5/O44YZ3QBAE2O0OXHLJG1BdXY1t21rK9jo60UZKpVKIRlUoSgQTE+PGuNPpRCAQRCgkoaWldUvMJwYWIjprp2o1PzeXgNdbDQAIh7ebWSZRWVieT6oqo7+/b8V86ujohCiG4fN1wW63m1zpxmJgIaKzcvToS/j1r59CJpMxxhobmyBJYQSDIbjdJ281T0QFuq5jfHwMihJBT08U6XTaeKyxsdHYIn8rzycGFiI6I7OzM7DZbEavnooKDzKZDCorK439Herq6kyukqg8JBKzUBQZihLB7OysMV5ZWYlQqLDOq66u3sQKSwcDCxGdVjqdRk+PCkWRMTY2inPPfR0uuuj1AICOjk786Z++Da2tbVviOjrR2cpkMsZ8Gh0dMcbtdvtSX6wwWlvbSnaLfLMwsBDRCeXzeQwM9ENRZPT39yKfzwMobIW/sPBKR1eLxYK2tnazyiQqC/l8HkNDA5Dlwnw6vuVEe3sHRDGM7u5u2O0OkystXQwsRPQauq7je9/7FuLxuDFWX98AUZQQConG5SAiOjld1zE5OWHs5pxKvRL06+rqjXUplZWcT6vBwEJEmJubQ19fDLt27YYgCEuNBtuRyWSM6+gNDY1ml0lUFubn56GqMmQ5gnh82hh3uysQConGfOIl1DPDwEK0RZ2o1XxDQyNaWloBAPv27ccll1zG6+hEq5DNLiIWi0FRIhgeHjK2yLfZbOjq6oYohtHR0cn5dBYYWIi2kOVW84oSQW9v74pW869eh1IO7eaJzKRpGoaHh6AoEcRisRXzqaWlFZIUht8f5FxaJwwsRFvI2NgofvKTHxlf19bWIhSSyrLVPJFZpqePQZYjUFUZyWTSGK+pqVm6hBrmfCoCBhaiTSqZnIeqKgCAvXvPA1D41NfY2ITm5m0QxXBZt5on2kgLC0moqgJFkTE1NWmMu1wuBAIhSFIYTU3NnE9FxMBCtIlks1koioyjR48areZdLjfOOWcPrFYrBEHAddfdwDdVolXIZrPo7++FLMsYGhowtsi3Wq3w+boQCkno7PTBZuOf0o3A/8pEm8DY2Chk+WWMjAwgkUhC0woL/pZbzS8vAATAsEJ0CrquY2RkGKqqoKdHxeLiovHY8pnJQCAIt9ttYpVbEwMLUZnSdd0IH729MUQiR+F2O+D1ViMYLNw6WV1dY26RRGUiHo9DVWUoioy5uYQxXlXlhSgWbu2vqak1sUJiYCEqI6nUAqJRFbIcwYUXXoTOTh8AQJLCyOezuPDC8+B2VyOf10/zTESUSqUQjapQVRnj42PGuMPhQCAQQigksuVECWFgISpxy63mFSWCgYF+4zq6okSMwFJXV483vvEAams9iMeTABhYiE4kl8sttZwozKfllhMWiwXt7Z2QJAk+XzfsdrvJldKrMbAQlahcLoennvoVenqiyGQyxnhjYxMkKYxgMGRidUTlQ9d1jI6O4ujRo+jpUZFOp43HGhsbIYqF+VRR4TGxSjodBhaiEpJOp+FyuQAU7kQYGxtDJpM5rtV8GHV1dSZXSVQeEolZ9PSoGByMYXR0wliM7vF4EApJkKQw6urqTa6SVouBhchk6XQasVgUiiLj2LEpHDr0HtjtdgiCgIsuej2sVitbzROtUiaTQSwWhSxHMDo6AotFgNvtgM1mR3e3H6IY5nwqUwwsRCbI5/MYHOw3Ws0vX0cXBAHj42Nob+8AAPh8XSZWSVQe8vk8hoYGjPmUy+UAFOZTe3sHLrjgXDQ2tkIQ+CevnPHoEW2wgYF+PProz5FOp4yxurp6SFKh1bzHw1bzRKej6zqmpiahKDJUVUEqtWA8VltbtzSfJNTUeI3F6LmcZmLFdLYYWIiKbG5uDtls1lh7UlNTg3Q6hYqK5VbzYdTXN/DWSaJVmJ+fX9oiP4Lp6WPGuNu9PJ8kNDQ0cj5tQgwsREWwuLiIWKwHihLByMgwfL4uXH31NQAAr7cab3vb29HU1Mzr6ESrkM0uIhaLQVEiGB4eMnZuttls6OrqhihKaG/vhNVqNblSKiYGFqJ1omkahoYGoSgR9Pb2rmg1n8/noWmaEVC2bWsxq0yisqBpGoaHh6CqMnp6elbMp5aWVkhSGH5/EE6n08QqaSMxsBCtk8OHf4L+/j7j69raWoRCEkIhia3miVZpevoYZDkCVZWRTCaN8ZqamqVb+yV4vdUmVkhmYWAhWoNksnAdPRzeYeyb0tHhw/j4OILBEEQxjKamJl5HJ1qFhYUkVFWBqsqYnJw0xl0uFwKBECQpjKamZs6nLY6BhWiVstks+vpiUBQZg4MD0HUdDocDO3bsAgCEw9uxY8dOXkcnWoVsNov+/l7IsoyhoQGj5YTVakVnpw+iGEZnpw82G/9MUQF/E2jryOchJGahO11ARcWqfmS51byiyIjFoitazW/b1oKK456HvUeITq2wRf4IFEVGT4+6Yj41N2+DKIYRCAThdrtNrJJKFQMLbXqWkWG477sXrm9+DZaltvHZ885H6v0fQuZtbwdOcadOKrWA//iPfzfuSvB6q41W89XVNRtRPlHZm5mJQ1FkKIqMuaU5CABVVV5jPtXU1JpYIZUDBhba1GwvHkH1n78VwvwchKXdZAHA9sLv4f3L9yH9s8OY+8KXAKvVaDU/OzuDSy65DABQUeFBMBiC3W6HKIaxbVsLr6MTrUIqlUJPTxSKEsH4+Jgx7nA4EAiEIIoSWlpaOZ9o1RhYaPNKJlF949teE1YAQFi+Xv7v38dIczOO7L8YAwP90DQNgiBgz55zUVVVBQA4ePBNG146UTnK5/MYGOiHokTQ399ntJywWCxob++EJEnw+bp5+ZTWhIGFNi3XQ9+DMDWFE31+m7RY8ILdDsVuR+r738VCUzNgtRqt5h0Ox4bXS1SOdF3HxMQ4ZDmCnh4V6XTaeGx5PgWDIVRUeEyskjYDBhbatJwPfgcQBGBp/YkOGOFl3GLBH5ZCSdXCAnZarfDfeDNbzROtUiIxu9THR8bMzIwx7vF4jP1S6usbzCuQNh0GFtq0LFOTyOg6FLsdL9tsCOZyOD9b2C0zlMthOJtFOJtFRz6P+bp6ZBhWiE4pk8kgFotCliMYHR0xxm02O/x+P0QxjLa2dracoKIoemB54okn8KUvfQnRaBTz8/Nobm7GwYMHcdtttxlrBIjWUz6fx+BgP/7T7UZ/VRWWV69kBcEILE4Abzru1LW+1JiQiFbSNA2DgwNQlAj6+nqRy+UAAIIgoK2tHaIYht/vh93Oy6hUXEUPLDMzM9i9ezduvfVW1NTUQFVV3HPPPVBVFffff3+xX562mF//+ilEIkeRTqdgC4Tg6I2hIZ/HzlwO4Wz2hD+jeauxeOkbN7ZQohKm6zqmpiaXLvkoSKUWjMdqa+sgSWEEgyI/dNKGKnpgufbaa1d8vW/fPjgcDnziE5/A+Pg4mpubi10CbWILC8kVi/kSiQTS6RQqKioQuuEm7PvlY2hKLsCi5U/487ogIPX+DwFL2+sTbWXz84WWE4oSwfT0MWPc7a5AKCQiFBLR2MiWE2QOU9aw1NTUAChszUx0phYXFxGL9UBVZQwPD+HGG29BbW1h06lzzz0P4fB2dHR0wmKxwPatB4Hrr4W+sLDi1mbdYgE0DYtXX4OFj37MrH8VItNls4uIxWJQVRlDQ4PGJok2mw0+XzckSUJ7eydbTpDpNiyw5PN55HI5RKNRfP7zn8eBAwfQ3t6+US9PZW651byiRBCLxVa0mh8dHTYCS1PTyjN2ufPOR/yxp+D+P5+H+9++CWGhcGo7H96B1Af+Eukbbwb4RkxbjKZpSy0nIojFelZ8eGxpaYUoSggEQnA6nSZWSbSSoC/H6SK77LLLMD4+DgC49NJL8c///M8r+rCsRSKRQj6vrUd5tEZWqwVer7uox2Jqago//vEPX9NqXpK2L7Wa967uiRYXIRw7Bric0GtqC7c8byIbcSxodUr1WBw7dgyKEoGiyJifnzfGq6trIIoSJCmM6upqEytcf6V6LLaq6mr3mu8i27DAEolElrY+j+Lee+9Fe3s7vvrVr/I0I71GMpnE7OwsWltbAQC5XA5f/OIXIQgCJEnCjh070NLCLfKJVmNhYQFHjx7Fyy+/jImJCWPc6XQa86m1lVvkU+nbsMByvEgkgmuvvRb/9E//hKuuumrNz8PEbL71+vSSzWbR19cLWY5gcHAAVVVe3HLLrcab6NTUJGpr6xhwT4GfJEuH2cdieT4pimy0nAAKW+T7fF2QpDB8vi7YbJt/Ky6zjwWtdDZnWEz5bZUkCXa7HQMDA2f1PPm8hlyOv4ClYC3HQtf1pevoMmKx6IpW8w6HE/PzC0ab+Zqaeug6eLxXgfOidGzksdB1HaOjI1AUGT096or51NTUbGyRvzyngK01nzgvSsPZnCIxJbAcOXIE2WyWi263uKef/k/84Q8vGF9XVXkhSRJCIbaaJ1qtmZk4VFWGLMuYm0sY45WVVZCkMEIhyViUTlTOih5YbrvtNuzatQuSJMHlciESieArX/kKJEnCwYMHi/3yVCIK65dUtLW1Gf16urq6EYm8zFbzRGconU4jGlWhKBGMj48Z4w6HA35/EKIoobW1jfOJNpWiB5bdu3fj4Ycfxn333Qdd19HW1obrr78e733ve9kRd5PL5XIrWs1rmobdu/fi4osvBQC0trbh0KH3sNU80Srk8/kV8ym/tK+QIAjo6OiEKEro6vJzPtGmVfTA8oEPfAAf+MAHiv0yVCJ0Xcf4+BgUJYKenuhrWs3X17/SYFAQBL65Ep2CruuYmBiHosiIRlWk0ynjsYaGRoiihGAwBI+n0sQqiTbG5l8iThtK13U88sjDWFjaoG251bwkhY1LQUR0aolEAqoqQ1EimJmZMcY9Hg+CQRGSFEZ9fYN5BRKZgIGF1iyTyUCWe3Ds2BguueQAgMJtkzt27MTc3BxEMYzW1ja2midahUwmg1gsCkWRMTIybIzbbHb4/X6IYhhtbe2cT7RlMbDQGcnn8xgaGoAsy+jv74Wm5eF2O9DdLaKlpXDX1wUXXGRylUTlQdM0DA4OQFEi6OvrRS6XA1C4XNra2gZRDMPvD3C9HxEYWGiVEolZvPjiH17Tar6xsQHnn3/uirUpRHRyuq5jamoSqipDVRXj8ikA1NbWQZLCCAZFVFVVmVglUelhYKGT0nXduC0ylUobe6Yst5oXRQnbtjWjrq4S8XiSmzIRncL8/DxUVYGiRDA9fcwYd7ncxnxqbGzirchEJ8HAQisst5pXlAi8Xi/e8IbC2pSmpibs3r0X7e0daG/vMLbI55sr0clls4tQ1R4oSgRDQ4NY7oRis9ng83VDkiS0t3ey5QTRKjCwEDRNw/Dw0FKr+RhyuUKreafTiUsueQOsVisEQTD2TyGikyusSxnCM8/04Q9/eAmZzCtb5Le0tEIUJQQCITidThOrJCo/DCxb3JEjz+PIkeeRTCaNsZqaGohiGKGQyE9+RKs0PX0MiiJDVWUsLCThdjuQzWZRXV0NUQxDFCV4vdVml0lUthhYtpiFhSScTpcRRBYXM0gmk3C5XAgEQpCkMJqamnmph2gVUqkFY13K5OSkMe52u7B79zloa+tGQwPXpRCtBwaWLSCbzaK/vxeyLGNoaABXXnkV/P4AACAc3on6+kZ0dvq2RKt5orOVy+XQ19cLRYlgcHAAmlZYbG6xWODzdSEUkhAI+NHYWM3F6ETriH+hNild1zEyMgxVVV7Tan5iYtwILFVVVbx9kug0dF3H6OgIVFVGT08UmUzGeKypqRmiGEYwGILb7QYA2Gzc3I1ovTGwbELpdBoPPvidFa3mq6q8kCQJoZCEmhq2midajdnZGShKBIqiIJGYNcYrK6sgihJEMYzaWs4noo3AwLIJpFIpTE5OoLPTBwBwuVxwuVzIZNIIBEIQRQktLa28jk60Cul0Gj09KhRFxtjYqDFut9uN+dTa2sb5RLQKmUwGPT0q+vp68aY3vfmsnouBpUzlcjmj1fzAQD8EQcChQ+8xbpU8ePBP4PFUshsy0Srk83ljPvX39yGfzwMo7DPU0dEJUZTQ1eXnfCJahXw+j8HBfiiKgv7+V1pODA72o7HxnDU/LwNLGdF1HePjY1AUGT09KtLptPFYY2Mjksl5I7Dwsg/Rqem6jomJCShKBNGoinQ6ZTxWX98AUZQQConweCpNrJKofCQSCfzhDy8gGlVXtHCpq6uHKBbuQD0bDCxl5KWX/ognn/yl8bXH40EoJEGSwqirYy8fotVIJBKIRgu3IsfjcWO8oqICoZAEUZTQ0NBoYoVE5UPTNKODeC6XxYsvHgFQaOEiiiJEMYz6+oZ1uYTKwFKillvNV1ZWoaOjEwDQ3d2NZ599Gt3dhVbzra1tbDVPtAqLi4uIxaKQ5QhGRoaNcZvNju7ubohiGO3tHZxPRKtQmE+FlhMejwdXXPEnAApnUvbuPQ+trW3o6Ohc9/nEwFJC8vk8hoYGIMuycd2vo6PTCCweTyXe9a73cr8UolXQNA1DQ4NQlAh6e2PGdXRBENDa2gZRlOD3B+FwOEyulKj0nayFi81mx2WXZY31Xfv3X1y0GviXrwRMTk4sbemtrLjuV1tbh/b2jhVdkxlWiE5tamoSihKBqipYWDh+PtUutZyQuPcQ0Rn4wx9ewAsv/P4kLVykDVuMzr9+JeDpp//TOE3tdlcYreYbGhp56yTRKiST80tb5Ms4dmzKGHe53AiFRIRCEpqauEU+0WosLCRhtzuMIJLP540WLsFg4e+TGS1cGFg2UDa7iFgsBlWVccUVV8LtrgAAbN++ExUVFRBFtponWq1sdhG9vYUt8oeGBqHrOgDAarXC5+uGJEno6PBxPhGtQjabRV9fDIoiY3BwAFdccSVCIQkAIIqFDUc7O82dTwwsRaZpGkZGhpeu+/Ugmy1c91NVBbt37wWApR0zJROrJCoPyy0nZDmCWCxqzCcAaGlpRSgkIhAIweVymVglUXlYnk+KIiMWi65o4TI5OWEEFo+nEt3d5t/ez8BSJKnUAl544Xmoqrziut9yq/muLr+J1RGVl+np6aV1KTLm5+eNca+32gj81dU15hVIVGay2UV85zvfWtHCpTCfxJJt4cLAso6Ovx8dEPDii0eQz+fhcrkQCIQgSWFTrvsRlaNUagHRqApZjmBycsIYdzqdCASCEMUwtm1r4XwiWoVUKoWJiTH4fN0AALvdgcrKSiwuZspmPjGwnKVsNov+/l7IsoxsdhF/9mfXAQDcbjcuuGAfqqtr0Nnp4909RKuQy+XQ11dYlzI4OABN0wAAFosFnZ0+iGIYPl8X5xPRKuRyOfT39xktXADg0KH3GF3FDxw4CLe7omxaTnDWr8Fyq/nlLfKPv+43Nzdn3DJ57rmvM6tEorKh6zrGxkahKBH09ESRyWSMx5qamiGKEoLBkLFInYhO7pUWLoX5dKIWLsuBxeutNqvMNWFgOUOqKuPZZ59Zcd2vqsq71HeE+zsQrdbs7AwUJQJFUZBIzBrjlZVVS7f2h1FXV2dihUTlR5aP4vHHHzW+rqysNFpOlHsLFwaW00ilUhAEwbjrQBAEzM0l4HA44PcH2Wqe6Ayk02n09KhQFBljY6PGuN1uh98fhCSFOZ+IVimTyaCnR0VFRYVxI4fP1w2n04muru5N18KFgeUEcrmc0Wp+YKAf559/Ic4773wAhV+GK698E3y+7rK57kdkpnw+vzSfCi0n8vk8gEL47+johChK6Orycz4RrcKJWri0tLQagcXtduNd73rvptx/iIFlSaHV/DhkOYKeHnXFdb/JyUnjn+12O4JB0YwSicpGYT5NQFEiiEZVpNMp47H6+oalS6giPB7z93YgKgeTkxOQ5cJ8Or6FS11dPXy+7hUtXDZjWAEYWAAU3ly///3vrAgmHo/HuO5XX99gYnVE5WNubg6qKkNRIojH48Z4RUWFMZ8aGhpNrJCoPP3mN88Yd/q43RXGfilbqYXLlgwsmUwGg4MDCAZDAAqnpuvq6hGPz8Dv90MUw2hra9801/2IiqnQaj4KWY4YPbGAQhfX7u7CdfT29g7OJ6JVWG7hoigRvPGNVxg3coTDO+BwOCCKYXR0dG7J+bRlAoumaRgcHICiRNDXV7juV1NTY3za27dvPy699A2w29lqnuh0NE3D0NAgFCWC3t5eo9U8ALS1tUMUJfj9QTgcnE9Ep6NpGoaHh5ZauMSM+RSNKsb2GIFAEIFA0MwyTbepA4uu60ut5mVEo69uNV+3Yp0Kr6UTnV5hPkWgqq+eT7VGq3ne2k+0OqlUCi+88PvXtHCpqamBKIYRCIRMrK70bOrAMjIyjB/96AfG1253BYLBEERRQmMjW80TrUYyOQ9VVaAoMo4dmzLGXS43QqHCdfSmJs4notXI5/PGolir1Yo//vFF5HJZtnBZhU0TWJav++m6jnB4OwBg27YWVFZWobl5GyRJQnt756ZdPU20nrLZRfT29kJVC63mdV0HUHiD9fm6IUkSOjrMbTVPVC6Ob+GSSi3g7W+/EQDgcDiwb99FqKryorOT8+l0yjqwHH8dPRbrQTabRWVlJSQpDEEQYLVacfPNt/KXgGgVllvNy3IEsVgU2ewr61K2bWuBKEoIBELGJopEdHLL80lRZMRi0RUtXGZm4kY35N2795pUYfkp28CSSqXwzW9+DYnEnDFWXV0NUQwjl8sZm1AxrBCd2vT09NKtyDLm51+ZT4VW84Vbkaura8wrkKjMRKMKnnnm169p4SJJhRYuy2GFzkzZBpZMJoP5+Xk4nc6ldSlhNDdv43U/olVIpRYQjaqQ5QgmJyeMcafTWTat5olKRSpV2BhxuamgzWYzWrgEAoV1ky0trZxPZ6lsA4vdbsdVV70ZbW2dbDVPtAqvbjWvaRoAwGKxoLPTh1BIQldXN+cT0Sq8uoXL3r3n4cILLwIAdHT4cOWVV8Hn62LLiXVU9Hemn/70p/jRj36El156CYlEAj6fD7feeiuuu+66s0qbHo8HgUAQuZy2jtUSbS7Ht5qPRlVkMhnjscbGJkhSGMFgCG53hYlVEpWH4+dTT090xdYY09PHjH+2Wq3GxqS0fooeWL72ta+hra0Nd9xxB2pra/H000/jE5/4BMbGxnDbbbcV++WJtqTZ2RkoigxVlTE7O2uMV1ZWGvul1NXVmVghUXnRdR0PPfQ9TEyMG2OVlZVGy4m6unoTq9saih5Y7r333hVvjPv378fMzAy++tWv4r/8l/+yJbcXJiqGdDqNP/yhB7/97fMYHn5li3y73Q6/PwhJKrSa53V0otPLZDLo7+9DKCRCEAQIgoCGhgbE49Pw+wMQxcJ84t+wjVP0wHKiT3Hbt2/Hd7/7XSwsLKCykjvMEq1VPp/H4GA/ZFnG4GAfHA4rUqlFCIKAjo5OiGJhXQpbThCdXj6fx9DQAGRZRn9/oYWL1+vFtm0tAIALLtiH17/+Es4nk5iyuu53v/sdmpubzzqsWK1MtmZbPgY8FhtH13Wj1byqKsYdChaLgIaGZnR1FXqOsN2EeTgvSsfpjsVyCxdZjkBRFKRSr7ScaGioh6blYbMVftbrZduJs3U2J3g3PLA899xzePjhh/Gxj33srJ/L63WvQ0W0Hngsii+RSODo0aN4+eWXMT09bYw3NNQiHA5jx44daGpqMrFCejXOi9JxsmMxPDyM//iPh4yv6+trVswnXkItHRsaWMbGxnD77bdj3759OHTo0Fk/XyKRQj7Pu4TMZLVa4PW6eSyKZHFxEbFYFLIcwfDwsLFFvs1mW7qOLhmt5pc/QfJYmI/zonQcfyzS6TR6enqQy+Wwa9c5AAC3uxpOZwUaGhohSWF0dLzSwmVmZuFUT01rUF3tXvO6nw0LLIlEAu9///tRU1ODe+65Z10WKuXzGm9rLhE8Fuvn+JYTvb29Rqt5AGhtbYMkheH3B+FwOJa+H8aeKgCPRSnhsTCfpmno6+vDb37ze0SjPcjlsnC7KxAKhY1gcv31Nxt/k3QdPGZFtPSZa002JLCk02l88IMfxNzcHL7zne+w/TzRCRw7NgVZjiAaVU7Yaj4UkuD1ek2skKh8TE9PQ5aPoqdHgaZlkUotQtN01NTUIBSSoGmvdE3mnT7loeiBJZfL4SMf+QhisRgeeOABNDc3F/slicrGwkISqqpAUWRMTU0a4y6X22g5wevoRGcuEnkJR468AItFQG2tF4GAhGBQQlNTM+dTmSp6YLnrrrvw+OOP44477sD8/DxeeOEF47EdO3YYp7WJtopsNou+vhgURcbg4ICxLsVqtcLn64IohtlqnmiVstks+vt7Icsy9uzZi/b2DgCAKIaRSCSwfft27N27E3NzGV7qKXNFDyxPPfUUAODuu+9+zWOPPvoo2tvbi10CkelO1Wq+uXkbJCmMQCAEl8tlYpVE5UHXdYyOjkBRZPT0qMZ8crtdRmBpaGjEVVe9BTabZak/VuYUz0jloOiB5bHHHiv2SxCVrHg8DkWJQFFkzM/PGeNebzVEUWSreaIzkMvl8PvfPwdFkTE3lzDGq6q8EMXCFvm0ebEtK9E6S6VSiEZVKEpkRd8Rp9OJQCAIUQxj27YWXkcnWoVcLmd0ELdarYjFopibS8DhcCAQCEEUJbS0tHI+bQEMLETrIJfLob+/z2g1v3ybscViWdoiP4yurm7jjZeITi6Xy2FgoB+KEsHY2BhuueUQ7HY7BEHA+efvA6DD5+uG3W43u1TaQHz3JFqj41vNR6MqMplXrpE3NjZBksIIBkNwuytMrJKoPOi6jomJcchyBD09KtLptPHYyMgwfL4uAEAwGDKpQjIbAwvRGZqdnYGiyFBVGbOzs8b4K63mwyds+klEJzY8PIRf/epxzMzMGGMej2dpPkmor28wrzgqGQwsRKuQTqcRi0WhKDJGR0eMcbvdDr8/CFGU2GqeaJUymQwymYyxEaLH48HMzAxsNjv8fj9EMYy2tnbOJ1qBgYXoJPL5PAYH+yHLMgYG+pDL5QAAgiCgvb0DohhGd3c3W80TrUI+n8fQ0AAURUZfXy86O31405veDACoqanF1Ve/BW1t7ZxPdFIMLETH0XUdk5MTS5d8FKTTKeOxurr6pS3yRVRWVppYJVF50HUdU1OTxnxKpV5pJphIJKBpmnEWpavLb1aZVCYYWIgAzM3NQVVlKEoE8XjcGK+oqEAoJEIUw6ivb+Ctk0Rn4Be/eATRqGp8XWg6KCIUEtHYyJYTdGYYWGjLWlxcRCzWA0WJYGRk2Ngi32azoaurG6JYaDXP6+hEp5fNLiIWi6GrqxtOpxMAsG1bC/r6euHzdUOSJLS3d7LlBK0ZAwttKZqmYWhoEKoqIxaLIZfLGo+1trZBksLo7g4Yb7hEdHKapi21nIggFutBNpvF5ZdfgXB4BwBAkrZDFMOcT7QuGFhoSzh2bAqyHEE0qiCZTBrjNTU1S+tSJOOOBSI6tenpY5DlCFRVXjGfqqurIQivnJFkc1taTwwstGktLCShqgoURcbU1KQx7nK5EAiEIEnb0dTE6+hEZ2JhIYnvfvdbxiXUwnwqtJxobt7G+URFw8BCm0o2m0VfXwyKImNwcMB4U7VarfD5uiCKYXR2+ngdnWgVstks+vt7EY9P44ILLgIAVFR40NHhg9VqMeYTW07QRuBvGZU9XdeXrqPLiMWiRqt5oLDoTxQlBAIhuFwuE6skKg+6rmN0dASKIqOnR8Xi4iIEQcD27buM2/nf/OZreCaFNhwDC5WteDwOVZUhyxHMz88Z41VVXkiShFBIQk1NrYkVEpWPRGIWsnwUsixjbi5hjFdWVkGSwrBYXgkoDCtkBgYWKiupVArRqApFiWBiYtwYZ6t5orMzPDyE5577LYDCfDq+5QTnE5UCBhYqece3mu/v74OmaQAAi8WCjo5OiGIYPl8XW80TrUI+nzfmU1tbB3btOgcA4PcH0dfXi2AwhK4uP+cTlRwGFipJuq5jfHwMihJBT090Rav5xsZGY4t8t7vCxCqJyoOu65iYGIcsR9DToxrzaW5uzggsTqcTV199jZllEp0SAwuVlERiFopS2CJ/dnbWGK+srDRazdfV1ZtYIVF5ef753yMSeQkzMzPGmMfjMeYTUblgYCHTZTIZ9PSoUBQZo6MjxrjdboffH4AohtHa2sYt8olWIZtdXNHxeGJiDDMzM7DZ7PD7/RDFMNra2jmfqOwwsJApllvNy7KM/v5e5HI5AIW7D9ra2iGKYfj9fraaJ1oFTdMwODgARYmgr68PN9xwE6qrawAAu3fvhc/XDb8/wJ1nqawxsNCG0XUdk5MTUFXlNa3m6+rqjXUpy3s9ENHJ6bqOqalJKIqMaFTBwsIr86mvrxd79pwLAGhpaUVLS6tZZRKtGwYWKrq5uTlEowpkOYJ4fNoYX241L4oSGhoaeesk0SrF43H87Gc/xfT0MWPM5XIb86mxscnE6oiKg4GFimK51byiRDA8PGRskW+z2dDV1Q1RDKO9vYNb5BOtQja7iLm5OWPBeWVlJebn52Cz2eDzdUMUxaXt8jmfaPNiYKF1o2kahoeHllrNx5DLZY3HWlpaIUlh+P1BtponWgVN05ZaTkQQi/WgsrISN954CwRBgN1ux9VXX4P6+gbOJ9oyGFjorE1OTuI3v3kekcjRFa3ma2pqltalSPB6vSZWSFQ+pqePQVFkqKqM+fl5Y1zTNKRSC6io8AAAWlvbzCqRyBQMLLQmCwtJqKqCaFRBMjmLVGoRmqYvtZoPQZLCaGpq5roUojPwm988g9/97rfG106nE8FgCKIYRnPzNs4n2tIYWGjVllvNy7KMoaEBaJoGi0WAx+OC3x9AICCy1TzRKuVyOfT19aKhocFo0tnS0gqLxQKfrwuhkASfr4vziWgJZwKdkq7rS9fRZcRiUSwuLhqPNTdvw/bt23HBBXuRTmvI5TQTKyUqfbquY3R0BKoqo6cnikwmgz179uL1r78UANDW1o5Dh97NlhNEJ8DAQicUj8ehqjIUZWWr+aoqL0SxsKV3TU0tbDYL3G430unkKZ6NaGubnZ2BokSgKAoSieNbTlQZa1KAQkNPhhWiE2NgIUMqlUI0qkJVZYyPjxnjDocDgUAIoiihpaWV19GJzoCmafjBD75vbJRot9uN+dTa2sb5RLRKDCxbXC6XM1rNDwz0I5/PAyh80mtv74QkSfD5utlqnmgV8vk8Bgb6MTTUj7e97a0ACnNJFCXE49MQRQldXX7OJ6I1YGDZgk7Wah4AGhsbIYphBIOhFaeqiejECvNpAooSQTSqIp1OwWIRcP75e1FVVdjobf/+i3kmhegsMbBsIYnErLG/w4lazUtS2NhJk2grsVh64HZ/A1brUQB2ZLP7kU7fAl2vPenPLCwkcfToy1CUyIr5VFFRgXB4O7xeL5Y2eC7jsJKBzfY8BCEFTetAPh80uyDawhhYNrlMJoNYLApZjmB0dMQYt9nsCAQCEMUwWlvb2GqetqgcKiv/Bm73l6HrVgB5AAIcjh/D47kLc3P/LzKZd57wJxOJBH7zm2cAFOaT3+9HKCShvb0DDocNNTUexOPluhg9hYqKf4Tb/WVYLDPGaDZ7IZLJjyObvcK80mjLYmDZhPL5PIaGBqAoMvr6epHL5QAUPuW1tbVDFMPw+/2w29lqnra2ysq/gcv1FQCAIOSXRgunRXQ9A6/3v2Bmxolo9AKoqgy3uwIXX1y4Bbm5eRskKYzW1nb4/QE4HJtlPqVQU/OnsNl+C0FYuVWBzfYcqqv/HHNz9yKTudmk+mirYmDZJI5vNa+qinFHAgDU1tZBkgpb5FdWVppYJVHpsFqjcLu/fMLHdB2YnLTgpZfsOHr0TkxNfRiAAKfTiX379sNms0EQBBw4cOXGFr0BPJ7PnjCsAIAgaNB1oKrqw8hmL4am+UyokLYqBpYyNz8/D1WVIcsRxOPTxrjbXYFQSEQoJKKxsamMr6ETFYfL9XXouvW4MysFf/yjDb/7nQNTU8udj+dRWTmE7u5rEApJm7wjcgou11dOGFaWCUIh0LndX0Uy+XcbVxpteQwsZSibXUQsFoOiRDA8PAR9aWWfzWZDV1c3RFFCe3vnJn9jJTo7VuvLAPJYXARsNmB5GVc8bsHUlBUWi45AIIcdOzQ0NrZgcfEyU+vdCHb7s7BY5k77fYKQh8PxIwYW2lAMLGVC0zQMDw8tbendg1wuazzW0tIKSQrD7w+y1TzRKui6jsFBHarqgqra8OY3pxAMFs607NqVRXW1jlAoC7cb0HULksmtsW+KICyc/puM7y3XBcVUrooeWPr7+/GVr3wFR44cgaqq8Pv9+PGPf1zsl900pqePQZYjUFUZyeQrbxA1NTUIhQpb5Hu91SZWSFQ+pqenoSgRqKqCTKYCdrsdggD09dmMwFJbq6O29pUPBIKgIZvdb1bJG0rT2lb1fbpugaZ1FLkaopWKHlhUVcUTTzyBPXv2QNM04/IFndzCQhLRqApFiWByctIYd7lcCARCkKQwmpqauS6FaJUymQz+4z/+HZOTE8aY03k+9ux5FDt2pNDaeuI1G7puRS63C7nc6zaqVFPlcruRy4VhtcoQhFO9V2tIpf5io8oiArABgeXAgQM4ePAgAOCOO+7AH//4x2K/ZFnKZrPo7++FLMsYGhqAphXeQK1WKzo7fRDFMDo7fWw1T7QKuVwOx45Nobl5GwDA6XRC13VYLBZjPvl8XaisrEFV1V/hRJ+jCvuyODA/f8/GFm8qAcnk36K6+tBJv0PXrdC0NmQyf76BdRFtQGDhhmQnt9xqXlFk9PSoWFxcNB5rbt4GUQwjEAjC7XabWCVRedB1HWNjo1CUCHp6osjnNbzrXe8x9kd54xsPoLKyckU35HT6L6Drbng8d8JqHV8KKToEQUM+vx1zc19ALrfXnH8hkywu/hnm5++Gx/NxABbjLipdFwDo0LRWzMz8BwB2laaNVdYf163W8gxDMzNxyHIEiiIjkUgY4zU11RDFwhb5NTUn3xK8lCwfg3I9FpvJVj0Ws7MzkOUIZFlGIjFrjFdVVSGZTKCiogkA0NKy7YQ/n8+/A4nE9bDbfwaLpbA1fy73euTzrwMgYC0nNcv9WGSztyGROACn80twOH4CIAVN60Qm814sLt4Ii8WDcvksWu7HYrM5m5UMZR1YvN7yOfOQSqUgyzJefvlljI6OGuPV1ZWQJAnbt29He3t72a5LKadjsdltpWPxxz/+EY888ojxtdfrgSiK2LFjBzo6Os5wPl2/7vWV97G4YOl/BRZL4fZvT5n2RC3vY0FAmQeWRCKFfP7kGxyZLZfLob+/D7IcwcBAP/L5wqlVi8WCjo5OSFIYXV3dRqv5mZnV31JYKqxWC7xed8kfi61gsx+LfD6P/v4+uFwutLYW7mbxehuQyeTQ3t4BSQqju9tfEvNpsx+LcsJjUVqqq91rXipS1oEln9eQy5XWL2Ch1fw4ZDmCnh4V6XTaeKyxsRGiGEYwGEJFxSsfU0rt32EtSvFYbFWb6VgU5tMEFCWCaFRFOp1CZ6cPb3lLCwDA7fbg1lvfA5fLZfxMKf27b6ZjUe54LErD2dwoXNaBpZQkErNLfXzkFa3mPR4PQqHCupS6unrzCiQqI4lEAtGoAkWJIB6PG+MVFRWoq6uHruvG5Z7jwwoRbV4MLGchk8kgFotCliMYHR0xxpdbzYtiGG1t7bxTiugMPfbYz405ZbPZ0d3dDVEMo729g/OJaIsqemBJpVJ44oknAADDw8OYn5/H4cOHAQAXXngh6urqil3CutI0DYODA1CUCPr6epHL5QAAgiCgra0dohiG3++H3b5ZWs0TFY+maRgaGoSqynj96y81buGXpDAsFsvSfAoYtyYT0dYl6EXeenZoaAhXXHHFCR/7xje+gX379q35uePx5IZck9R1HVNTk0uXfBSkUq8s5qutrYMkhREKSaisrCx6LaXGZrOgttazYceCTq6cjkVhPhW2yF9YKMynSy55A845ZzcArLjkU47K6VhsdjwWpaWuzrPmW8yLfoalvb0dsiwX+2WKYn5+HqpauI4+PX3MGHe7KxAKiQiFRDQ2NpX1GyvRRslkMjh69CUoioxjx6aMcZfLjWAwhNbWVmOMc4qIXo1rWF4lm11ELBaDqsoYGho0eh/ZbDb4fN2QJAnt7Z2wWq0mV0pU+o4/U6JpeTz77K+haRqsVqsxnzo6fJxPRHRaDCwoXEcfGRmGokQQi/Ugm32lU2tLSytEUUIgEILT6TSxSqLyoOs6RkaGIcsRpFIpvOUtbwVQODO5d+95qKysRCAQ2lR39wjCNJzOB2G1DkDXncjn/wTAiS+FE9HabOnAMj19zLgVeX5+3hivrq6GKIYhihK83moTKyQqH9PT01BVGYoiY35+zhhPJGaNebRv336zyiuSPDyez8DtvgdAFoW3VB2C8I8AdsFiuR9A2NQKiTaLLRdYUqkFY13K5OSkMe50OhEMhiCKYTQ3b+M1dKJV6uuL4bnnfovJyQljzOl0IhAIQhTDqKrymlhdcVVW/je4XPdDEJbvXcge9+hRVFUdxMzM48jnQ2aUR7SpbInAks1m0d/fB0WJYHBwAJpWWClusVjg83VBFMPo7PTBtpYuZ0RbTC6Xg65rxq372WwWk5MTsFgs6Oz0IRSS0NXVbdJ8ysNu/yWs1kHouhuLi5dD15uK8ko22+/gdn/llLUIQhIez39HIvHdotRAtJVs2r/Quq5jdHQEiiKjp0fF4uKi8VhTU7OxRf7yvg9EdHK6rmNsbBSKEkFPTxTnnvs6nHvu6wAAXV1+XHLJZUvzqcK0Gl2ur6Oi4n/Aah0zxnTdikzmbZif/0fo+vruNO1yfQW6boMg5E76PYKQh8PxCCyWIWha+7q+PtFWs+kCy8xMHKoqQ5ZlzM0ljPHKyipjv5Ta2loTKyQqH7OzM1CUwrqURGLWGB8aGjQCi91uxznn7DGrRABARcXn4PF85jV9SgQhD6fzB7DZnsfMzC/WNbTY7b8+ZVh5pQYdNtvzWFzcqoGlENocjl8ASEPTupBO38wAR2dsUwSWdDqNaFSFqsoYGxs1xh0OB/z+IERRQmtrG9elEK2Sruv4yU9+hMHBAWPMbrfD7w9CksJGt+RSYLW+BI/nMwCAE01xQcjDau2Dx/NpzM//f+v4ymey52ZR9+csWTbbs/B63wWrdQS6vvznRkdFxf9AOv1uzM//IwC7mSVSGSnbwKLrOmKxHhw9ehT9/X3I5/MAChtOdXT4IIoiurpeaTVPRCeXz+cxNjaKtrbCp15BEOByuZbmUydEsbAupRRbTrjdX17VpRmX69+QTN4FXV+fO/9yuXNhtQ6s6ixLLrdrXV6znNhsR1BT81YsL0R+9X8nl+urEIQ5zM19GQA/TNLplW1gSSQS+OlPfwJNK3xyaWhohChKCAZD8Hi23hb5RGdK13VMTk4YLSfS6RRuvPFmo6v4hRdehP37Ly75+eRw/GKVl2bSsNl+h2z2wLq8bjr9Prhc3z/l9+i6FdnspdA0/7q8ZjnxeO4EkIUg5E/4uCDocLm+h1TqA8jl1t6ihbaOsg0suq7D4/HA7w9BksKor28wuySisjA3N7e0X0oE8XjcGK+oqMDcXMIILOWzB1Fm1d8pCKv/3tPJZl+PdPptcDp/CEE4UY8aCwA7kslPr9trlguLpQcOxxOn/T5dt8Ht/jLm5hhY6PTKNrB4PB4cOvRuaOxlRbRqo6Mj+OEPH1rRcqK7249QSEJHRycslrU1JTNTPi/CYpk86Sf5ld8bXMdXFjA39yXoehVcrm+iEFAK44UzPs2Ym/smcjlzFySbwWZ7cVXfJwg52Gy/K3I1tFmUbWCx2+2wWBaNPVWIaCVN0zA0NIhsNotAoPCHuqmpGS6XG7W1tZCkMPz+IByO0luXcibS6ffA4fjVKb9H1y3I5S4swgZuDszP/wsWFv5vuFwPwGrth667kc9ficrKtyOfzwDYiu9RZ7ImhetXaHXKNrAQ0YlNTU3h5ZdfhqrKWFhYQFWVF35/AIIgwGq14uabby37kHK8TOatyGb3wGb74wnPsui6AEBAMvmJotWgaT4sLPyt8bXNZkHh7XX9LkGVk1xuL3RdOG4H4BMrrPHh5SBaHQYWok0gmZxHLBbF4GAMg4MjxmJ0l8sNn68LuVzWuMNnM4WVAjtmZ/8d1dVvh93+O+i6FYKQX9qTRQDgQCJxP7LZS02uc+vQNB8WFw/C4XjslJfqBCGPdPp9G1gZlTMGFqJN4LnnfoNI5GW43Q5YrVZ0dXVBkiR0dPhgtVrNLq/odL0eMzOPwm5/DC7XN2G19kLXK7C4+Gak07es+y63dHrJ5P+E3X45gNRJz3yl03+BXO68jS+OyhIDC1EZ0XUdIyPDUBQZO3bsQnNzMwBAFMOYmYnjggvORVNTO2y2zXYWZTUsyGYPIps9aHYhBCCflzAz8wi83r+AzaYubRwnAMgDsCKVug3J5CdNrpLKCQMLURmIx+NQlAgURcb8/BwAwGq1GoGlpaUV1113PWprPYjHk8jltuJCTyo1+fw5iMefg93+FByORwGkoGk+pNM38KwXnTEGFqISlcvlcPToy1CUCCYmxo1xp9OJQKCwRT5R6ROQzV6CbPYSswuhMsfAQlRCdF03el5ZrVa88MLvMD8/D4vFsrRFfhhdXd2w2Th1iWhr4bsekcl0Xcf4+BgUJYKRkWHccMPNsFgsEAQB5557PjQtj1BIhNtdYXapRESmYWAhMkkiMQtZjkBVZczOzhrjQ0OD6Oz0AQB27TrHrPKIiEoKAwvRBhsbG8UzzzyN0dERY8xut8PvD0AUw2htbTOxOiKi0sTAQlRk+Xwei4uLcLvdAAr9e0ZHRyAIAtrbOyCKYXR3dxsbuxER0WsxsBAVga7rmJycgKLIUFUFPp8PBw5cCQCor2/AG95wOTo7u1BZWWlypURE5YGBhWgdzc3NQVVlKIqMeHzaGB8bG4WmacZi2h07dplYJRFR+WFgIVonv/zlY4hEXoZeaGIDm82Grq5uiGIYHR2dsFgsJldIRFS+GFiI1kDTNAwPD2HbthbY7XYAQFVVFXRdR2trGyQpjO7uAJxOp8mVEhFtDgwsRGfg2LEpyHIE0aiCZDKJK6+8CsFgCACwY8cuhEISvF6vyVUSEW0+DCxEp7GwkISqKlAUGVNTk8a4y+XG4mLG+Nrtdht3AhER0fpiYCE6hWRyHv/6r1+HphWaCVqtVvh8XRDFMDo7fbBarSZXSES0NTCwEC3RdR0jI8OYnp7GOefsBgB4PJVobGyCIAgQRQmBQAgul8vkSomIth4GFtry4vE4FCUCRZExPz8Hq9WKUEg0gslb3/pnxsJaIiIyBwMLbUmpVArRqApFiWBiYtwYdzgcCARCyOVyxhjDChGR+RhYaEtSVRlPPfUkAMBisaCjoxOiGIbP18WAQkRUghhYaFPTdR3j42NQlAhaW9sQDIoAgGAwBFVVEAqJCAZDqKjwmFwpERGdCgMLbUqJxCwURYaiRDA7OwugsFZlObBUVHhw3XU3mFkiERGdAQYW2lQikZcRiRzF6OiIMWa32+H3ByCKYRMrIyKis8HAQmVN13UIgmB8raoKRkdHIAgC2ts7IIphdHd3w253mFglERGdLQYWKju6rmNycgKqqiAaVfD2t98Ij6cSALB79x60t3ciFBJRWVlpcqVERLReNiSw9PT04DOf+Qyef/55eDweXHvttfjIRz4Ch4Ofemn15ubmEI0qkOUI4vFpY7ynJ4rdu/cCAHy+bvh83SZVSERExVL0wDI7O4t3vetd6Orqwj333IPx8XHcfffdSKfT+OQnP1nsl6dNYGYmjieffALDw0PQdR0AYLPZ0NXVDVEMo729w+QKiYio2IoeWL797W8jmUziX/7lX1BTUwMAyOfzuOuuu/DBD34Qzc3NxS6ByoymaVhYWDAu6bjdFRgbG4Wu62hpaYUkheH3B+F0Ok2ulIiINkrRA8uvfvUr7N+/3wgrAHD11VfjU5/6FJ566in8+Z//ebFLoDIxPX0MshyBqsqoqPDg7W+/EQDgdDpx4MCVaGxshNdbbXKVRERkhqIHllgshuuuu27FmNfrRWNjI2Kx2Fk9t9VqOaufp7O3fAzWeiwWFpJQlMK6lKmpSWNc1zUsLqZRUVEBAJAk8eyL3eTO9ljQ+uGxKB08FqXluJs6z1jRA0sikYDX633NeHV1tbGh11p5ve6z+nlaP2s5Fs888wx+/etfQ9M0AIDH44Lf78eOHTvQ3d0Nm403sa0F50Xp4LEoHTwW5a+s/yIkEink85rZZWxpVqsFXq/7tMdC13WMjAyjurrGWJtis7mRTKbR3LwNkhRGMBiC2114U5mbywDIbMS/wqax2mNBxcdjUTp4LEpLdbUbFsvaznYVPbB4vV7Mzc29Znx2dhbV1We3HiGf15DL8RewFJzsWMTjcaiqDEWRMTeXwPnnX4gLLtgHAGhr68SNN96Cmppa4/t5PM8e50Xp4LEoHTwWpWHpRs81KXpg8fv9r1mrMjc3h8nJSfj9/mK/PJkglUqhpycKRYlgfHzMGC/su/PKb6vNZlsRVoiIiE6m6IHlsssuwxe/+MUVa1kOHz4Mi8WCiy++uNgvTxtM0zR8+9sPIJ1OAQAsFgs6OjohimH4fF2w2+0mV0hEROWo6IHlpptuwje/+U18+MMfxgc/+EGMj4/jc5/7HG666SbuwVLmdF3H6OgoXn55HNu37wFQCCjd3X5MTU1AFAvrUioqPCZXSkRE5a7ogaW6uhpf//rX8elPfxof/vCH4fF48Pa3vx233357sV+aiiSRmIWiyFCUCObmEnC7HWhoaEFdXSMA4JJLLuMdPkREtK425K9KIBDA1772tY14KSqSTCaDWCwKWY5gdHTEGHc4HNixYwdstlcu9TCsEBHReuNfFlqViYlx/PKXjwEABEFAW1s7RDEMUQyiqakW8XiSK/CJiKhoGFhoBV3XMTU1CUWR4Xa7cd555wMA2tra0d7egfb2ToRC4nF7qXD3SCIiKj4GFgIAzM/PQ1VlyHIE8fg0AKCiogJ7954Hi8UCi8WCt771z8wtkoiItiwGli2upyeKl1/+I4aHh6Av7ehjs9ng83VDkiSTqyMiIipgYNliNE2DIAgQljpQjY6OYGhoEADQ0tIKUZQQCITgdDrNLJOIiGgFBpYtYnr6GGQ5AlWVccUVf4K2tnYAwPbtO+ByuSCKErzes2uVQEREVCwMLJvYwkIS0agKRYlgcnLSGO/pUY3AUl/fgPr6BrNKJCIiWhUGlk0onU7j0Ud/jqGhAWha4VZjq9WKzk4fRDGMzk6fyRUSERGdGQaWTUDXdSQSs6iurgEAOJ1OzM7GoWkampu3IRSSEAyG4Ha7zS2UiIhojRhYytjMTHxpi3wZi4sZHDr0HthsNgiCgDe84QAqKjyorWU3ZCIiKn8MLGUmlUqhpycKRYlgfHzMGHc4HJiePoampkJDyeU1KkRERJsBA0sZURQZv/zlo8jn8wAKnZHb2zshSRJ8vm7Y7fbTPAMREVF5YmApUbquY2JiHFarFQ0NhS7IjY1NyOfzaGxshCiGEQyGUFHhMblSIiKi4mNgKTGJRAKqKkNRIpiZmUEgEMKf/MlVAIDa2lq84x3vRE0N16UQEdHWwsBSAjKZDGKxKGQ5gtHREWPcZrPD4XBA13VjZ1qGFSIi2ooYWErAT37yI2MBrSAIaGtrhyiG4ff7Ybc7TK6OiIjIfAwsG0jXdUxNTUJVZVxwwT4jjPj9ASwuLkKSwgiFJFRWVppcKRERUWlhYNkA8/PzUFUFihLB9PQxAEB9fSMkKQwA2L17L/bsOde47ENEREQrMbAUSTabRSzWA0WJYHh4CLquAwBsNht8vm5UV7/SaNBisZhVJhERUVlgYCmSdDqNxx77ufF1S0srRFFCIBCC0+k0sTIiIqLyw8CyDqanj0GWI8hkMnjjGw8AAKqqqhAO70BVVRVEUYLXW32aZyEiIqKTYWBZo1RqwViXMjk5CaBwaefCC/cZm7ldfvkVZpZIRES0aTCwnKHh4SEcOfI8BgcHoGkaAMBqtaKz0wdRDMPh4OUeIiKi9cbAchq6rkPTNFitVgCFDsn9/X0AgKamZmOLfLfbbWKVREREmxsDy0nMzMShqjJkWcbevedh165zAACBQAjJZBKhkITaWu46S0REtBEYWI6TTqcRjapQlIix8ywAxGJRI7C4XC5ceOFFZpVIRES0JTGwoHDZ5xe/eAS9vTHk83kAhQW07e2dkCQJPl+3yRUSERFtbVsysOi6jnh8GnV19QAK/Xuy2Szy+TwaGhohihJCIdG424eIiIjMtaUCSyKRgKrKUJQIZmZmcMsth4z9US688CLs27cf9fUNJldJREREr7bpA8vi4iJisShkOYKRkWFj3GazY2pqyggsDQ2NZpVIREREp7GpA8vo6Ah+/OMfIpfLAShc+mltbYMohuH3B+BwOEyukIiIiFZj0wQWXdcxNTWJxcVFtLW1AwAaGhogCAJqa+sgSWEEgyKqqqpMrpSIiIjOVNkHlmRyHopS2CJ/evoYamvrcOONN0MQBNjtDtx44y2orKyEIAhml0pERERrVLaBZXFxET/60b9jYGAAuq4DAGw2G+rq6pHLZWG3Fy738IwKERFR+SvbwLKwsIDBwUJYaWlphShKCARCcDrZy4eIiGizKdvAUuiMfBECgZBxpw8RERFtTmUbWKqqqnDBBRcil9PMLoWIiIiKzGJ2AWvFRbRERERbR9kGFiIiIto6GFiIiIio5BU9sDz11FP46Ec/ioMHD0KSJPz93/99sV+SiIiINpmiB5Ynn3wSkUgEF1xwAbxeb7FfjoiIiDahot8l9Dd/8ze44447AADPPvtssV+OiIiINqGin2GxWLhMhoiIiM5O2e7DAgDV1W4s7cpPJlm+u5zHwnw8FqWDx6J08FiUFotl7VuSlHVg4dmb0sFjUTp4LEoHj0Xp4LEof2ccWObm5jAxMXHa7+vo6IDD4VhTUURERETHO+PAcvjwYdx5552n/b6HH34YgUBgTUURERERHe+MA8v111+P66+/vhi1EBEREZ0QL+oRERFRySv6otvh4WG8+OKLAIBUKoWBgQEcPnwYAHDVVVcV++WJiIhoExB0vbg3ej300EP4+Mc/fsLHZFku5ksTERHRJlH0wEJERER0triGhYiIiEoeAwsRERGVPAYWIiIiKnkMLERERFTyGFiIiIio5DGwEBERUckr+8Dy1FNP4aMf/SgOHjwISZLw93//92aXtCX09PTg3e9+N/bu3YuLL74Yn/vc57C4uGh2WVtSf38/PvnJT+Laa6/Fjh07cM0115hd0pb005/+FH/5l3+Jyy67DHv37sW1116LBx98ENw5whxPPPEE3vnOd+Kiiy7Crl27cMUVV+Czn/0s5ubmzC5ty0smk7jssssgSZKxsexqFH2n22J78sknEYlEcMEFF2B2dtbscraE2dlZvOtd70JXVxfuuecejI+P4+6770Y6ncYnP/lJs8vbclRVxRNPPIE9e/ZA0zT+gTTJ1772NbS1teGOO+5AbW0tnn76aXziE5/A2NgYbrvtNrPL23JmZmawe/du3HrrraipqYGqqrjnnnugqiruv/9+s8vb0r7whS8gn8+f+Q/qZS6fzxv/fPnll+t33XWXidVsDV/84hf1vXv36vF43Bj79re/rW/fvl0fGxszr7At6vg58LGPfUx/y1veYmI1W9exY8deM3bnnXfq55133opjROb5zne+o4uiyPcpE0WjUX3v3r36t771LV0URf0Pf/jDqn+27C8JWSxl/69Qdn71q19h//79qKmpMcauvvpqaJqGp556yrzCtijOgdJQV1f3mrHt27djfn4eCwsLJlREr7b8npXNZs0tZAv7zGc+g5tuugnd3d1n/LN8p6MzFovF4Pf7V4x5vV40NjYiFouZVBVR6fnd736H5uZmVFZWml3KlpXP55HJZPDSSy/h85//PA4cOID29nazy9qSDh8+DEVR8OEPf3hNP1/2a1ho4yUSCXi93teMV1dXcx0R0ZLnnnsODz/8MD72sY+ZXcqWdvnll2N8fBwAcOmll+J//a//ZXJFW1MqlcLdd9+N22+/fc0BvuQCy9zcHCYmJk77fR0dHXA4HBtQERHRmRkbG8Ptt9+Offv24dChQ2aXs6Xdd999SKVSiEajuPfee/GhD30IX/3qV2G1Ws0ubUu59957UV9fj+uuu27Nz1FygeXw4cO48847T/t9Dz/8MAKBwAZURK/m9XpPeGvg7OwsqqurTaiIqHQkEgm8//3vR01NDe655x6uMTJZOBwGAJx77rk455xzcO211+LnP/85rrrqKpMr2zqGh4dx//334/Of/7zxt2N5XdfCwgKSySQ8Hs9pn6fkAsv111+P66+/3uwy6BT8fv9r1qrMzc1hcnLyNWtbiLaSdDqND37wg5ibm8N3vvMdVFVVmV0SHUeSJNjtdgwMDJhdypYyNDSEbDaLD3zgA6957NChQ9izZw+++93vnvZ5Si6wUOm77LLL8MUvfnHFWpbDhw/DYrHg4osvNrk6InPkcjl85CMfQSwWwwMPPIDm5mazS6JXOXLkCLLZLBfdbrDt27fjG9/4xoqxo0eP4rOf/SzuuusunHPOOat6nrIPLMPDw8ZOealUCgMDAzh8+DAA8JRfkdx000345je/iQ9/+MP44Ac/iPHxcXzuc5/DTTfdxDdpE6RSKTzxxBMACvNhfn7emAMXXnjhCW+3pfV311134fHHH8cdd9yB+fl5vPDCC8ZjO3bs4Jq7DXbbbbdh165dkCQJLpcLkUgEX/nKVyBJEg4ePGh2eVuK1+vFvn37TvjYzp07sXPnzlU9j6Dr5b0t5kMPPYSPf/zjJ3xMluUNrmbr6Onpwac//Wk8//zz8Hg8uPbaa3H77bfzTdkEQ0NDuOKKK0742De+8Y2TvlHQ+jpw4ACGh4dP+Nijjz7KT/Ub7L777sPDDz+MgYEB6LqOtrY2XHnllXjve9/L28xLwLPPPotDhw7hwQcfXPUZlrIPLERERLT5cfk6ERERlTwGFiIiIip5DCxERERU8hhYiIiIqOQxsBAREVHJY2AhIiKiksfAQkRERCWPgYWIiIhKHgMLERERlTwGFiIiIip5DCxERERU8v5/Uw+Slkht6IgAAAAASUVORK5CYII=",
           "text/plain": "<Figure size 640x480 with 1 Axes>"
          },
          "metadata": {},
@@ -1137,22 +1168,7 @@
        ]
       }
      },
-     "afa055db038f446eb2a92f05d75a9040": {
-      "model_module": "@jupyter-widgets/controls",
-      "model_module_version": "1.5.0",
-      "model_name": "DescriptionStyleModel",
-      "state": {
-       "_model_module": "@jupyter-widgets/controls",
-       "_model_module_version": "1.5.0",
-       "_model_name": "DescriptionStyleModel",
-       "_view_count": null,
-       "_view_module": "@jupyter-widgets/base",
-       "_view_module_version": "1.2.0",
-       "_view_name": "StyleView",
-       "description_width": ""
-      }
-     },
-     "c2f9fbddc4cc41d2a0cfb5f31d8e82f1": {
+     "e6fcaee78b794c5186b2da37a8679b6c": {
       "model_module": "@jupyter-widgets/output",
       "model_module_version": "1.0.0",
       "model_name": "OutputModel",
@@ -1165,12 +1181,12 @@
        "_view_module": "@jupyter-widgets/output",
        "_view_module_version": "1.0.0",
        "_view_name": "OutputView",
-       "layout": "IPY_MODEL_16802d9e848a45fe823d56984c49fc96",
+       "layout": "IPY_MODEL_a1fdac994ab24d80abc1b17dbb5f38b6",
        "msg_id": "",
        "outputs": [
         {
          "data": {
-          "image/png": 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",
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",
           "text/plain": "<Figure size 640x480 with 1 Axes>"
          },
          "metadata": {},
@@ -1179,7 +1195,7 @@
        ]
       }
      },
-     "dad66c99e7784a38898e206d60883759": {
+     "ed095bc3accc486bbeb67fdc849c7eac": {
       "model_module": "@jupyter-widgets/base",
       "model_module_version": "1.2.0",
       "model_name": "LayoutModel",
@@ -1231,71 +1247,55 @@
        "width": null
       }
      },
-     "db1da06a39a44326bfe81d4eddac371b": {
-      "model_module": "@jupyter-widgets/base",
-      "model_module_version": "1.2.0",
-      "model_name": "LayoutModel",
+     "fa4b9e11a211451f910376019ccbafa8": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "1.5.0",
+      "model_name": "VBoxModel",
       "state": {
-       "_model_module": "@jupyter-widgets/base",
-       "_model_module_version": "1.2.0",
-       "_model_name": "LayoutModel",
+       "_dom_classes": [
+        "widget-interact"
+       ],
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "1.5.0",
+       "_model_name": "VBoxModel",
        "_view_count": null,
-       "_view_module": "@jupyter-widgets/base",
-       "_view_module_version": "1.2.0",
-       "_view_name": "LayoutView",
-       "align_content": null,
-       "align_items": null,
-       "align_self": null,
-       "border": null,
-       "bottom": null,
-       "display": null,
-       "flex": null,
-       "flex_flow": null,
-       "grid_area": null,
-       "grid_auto_columns": null,
-       "grid_auto_flow": null,
-       "grid_auto_rows": null,
-       "grid_column": null,
-       "grid_gap": null,
-       "grid_row": null,
-       "grid_template_areas": null,
-       "grid_template_columns": null,
-       "grid_template_rows": null,
-       "height": null,
-       "justify_content": null,
-       "justify_items": null,
-       "left": null,
-       "margin": null,
-       "max_height": null,
-       "max_width": null,
-       "min_height": null,
-       "min_width": null,
-       "object_fit": null,
-       "object_position": null,
-       "order": null,
-       "overflow": null,
-       "overflow_x": null,
-       "overflow_y": null,
-       "padding": null,
-       "right": null,
-       "top": null,
-       "visibility": null,
-       "width": null
+       "_view_module": "@jupyter-widgets/controls",
+       "_view_module_version": "1.5.0",
+       "_view_name": "VBoxView",
+       "box_style": "",
+       "children": [
+        "IPY_MODEL_1e3f9fb98022415e81bd0b1d29f32e02",
+        "IPY_MODEL_e6e37a37ba5d40abafef620f2cbf37cf"
+       ],
+       "layout": "IPY_MODEL_5bccd89d7d2d4fec802bed7854496cfb"
       }
      },
-     "fb678518733b4e349920ca8f3e41ad0e": {
+     "fe83954702344ffd95336b0bbff9fd20": {
       "model_module": "@jupyter-widgets/controls",
       "model_module_version": "1.5.0",
-      "model_name": "DescriptionStyleModel",
+      "model_name": "IntSliderModel",
       "state": {
+       "_dom_classes": [],
        "_model_module": "@jupyter-widgets/controls",
        "_model_module_version": "1.5.0",
-       "_model_name": "DescriptionStyleModel",
+       "_model_name": "IntSliderModel",
        "_view_count": null,
-       "_view_module": "@jupyter-widgets/base",
-       "_view_module_version": "1.2.0",
-       "_view_name": "StyleView",
-       "description_width": ""
+       "_view_module": "@jupyter-widgets/controls",
+       "_view_module_version": "1.5.0",
+       "_view_name": "IntSliderView",
+       "continuous_update": true,
+       "description": "azim",
+       "description_tooltip": null,
+       "disabled": false,
+       "layout": "IPY_MODEL_ed095bc3accc486bbeb67fdc849c7eac",
+       "max": 90,
+       "min": -30,
+       "orientation": "horizontal",
+       "readout": true,
+       "readout_format": "d",
+       "step": 1,
+       "style": "IPY_MODEL_9699938620b041b39e2e7a0699ed8b60",
+       "value": 30
       }
      }
     },
diff --git a/_sources/ml-fundamentals/build-a-web-app-to-use-a-machine-learning-model.ipynb b/_sources/ml-fundamentals/build-a-web-app-to-use-a-machine-learning-model.ipynb
index 8410855930..79f76143f9 100644
--- a/_sources/ml-fundamentals/build-a-web-app-to-use-a-machine-learning-model.ipynb
+++ b/_sources/ml-fundamentals/build-a-web-app-to-use-a-machine-learning-model.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "92c750d6",
+   "id": "c44762ef",
    "metadata": {},
    "source": [
     "# Build a web app to use a Machine Learning model\n",
@@ -63,7 +63,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "699949e9",
+   "id": "8c08c144",
    "metadata": {
     "tags": [
      "output_scroll"
@@ -80,7 +80,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "78020125",
+   "id": "57748739",
    "metadata": {},
    "source": [
     "Convert the ufos data to a small dataframe with fresh titles. Check the unique values in the `Country` field."
@@ -89,7 +89,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "0489d00a",
+   "id": "096e7db6",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -105,7 +105,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8a803c00",
+   "id": "fa97296f",
    "metadata": {},
    "source": [
     "Now, you can reduce the amount of data we need to deal with by dropping any null values and only importing sightings between 1-60 seconds:"
@@ -114,7 +114,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "34f6ebf8",
+   "id": "577d47b3",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -127,7 +127,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3a661797",
+   "id": "be08f84e",
    "metadata": {},
    "source": [
     "Import Scikit-learn's `LabelEncoder` library to convert the text values for countries to a number:"
@@ -136,7 +136,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "fca62dc5",
+   "id": "01ac7434",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -149,7 +149,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d5a8fb7a",
+   "id": "ff3fc5b3",
    "metadata": {},
    "source": [
     "```{note}\n",
@@ -166,7 +166,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "84fa642b",
+   "id": "9ccc173c",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -182,7 +182,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b59539c7",
+   "id": "3af53151",
    "metadata": {},
    "source": [
     "Train your model using logistic regression:"
@@ -191,7 +191,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "4805987d",
+   "id": "696f0c65",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -209,7 +209,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "20b0090b",
+   "id": "1a4a2b8c",
    "metadata": {},
    "source": [
     "The accuracy isn't bad **(around 95%)**, unsurprisingly, as `Country` and `Latitude/Longitude` correlate.\n",
@@ -224,7 +224,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "96f5a16d",
+   "id": "96c91652",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -239,7 +239,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5e04d426",
+   "id": "53c6ccbb",
    "metadata": {},
    "source": [
     "The model returns **'3'**, which is the country code for the UK. Wild! 👽\n",
diff --git a/_sources/ml-fundamentals/regression/logistic-regression.ipynb b/_sources/ml-fundamentals/regression/logistic-regression.ipynb
index 4cf5e1b63e..8a6b0056a8 100644
--- a/_sources/ml-fundamentals/regression/logistic-regression.ipynb
+++ b/_sources/ml-fundamentals/regression/logistic-regression.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "45935723",
+   "id": "221ea189",
    "metadata": {},
    "source": [
     "# Logistic regression\n",
@@ -108,7 +108,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "d02e513a",
+   "id": "e72a0a8f",
    "metadata": {
     "tags": [
      "output_scroll"
@@ -326,7 +326,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "80a60e28",
+   "id": "b616df41",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -343,7 +343,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "31d1720c",
+   "id": "a6713d74",
    "metadata": {},
    "source": [
     "You can always take a peek at your new dataframe:"
@@ -352,7 +352,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "cf29881a",
+   "id": "2c26948a",
    "metadata": {},
    "outputs": [
     {
@@ -385,7 +385,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a06f668c",
+   "id": "c5cc4607",
    "metadata": {},
    "source": [
     "### Visualization - side-by-side grid\n",
@@ -400,13 +400,13 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "c80b3c24",
+   "id": "539855a5",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<seaborn.axisgrid.PairGrid at 0x7f948b8a8df0>"
+       "<seaborn.axisgrid.PairGrid at 0x7efbe7287880>"
       ]
      },
      "execution_count": 4,
@@ -437,7 +437,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "eceef26c",
+   "id": "707c2481",
    "metadata": {},
    "source": [
     "By observing data side-by-side, you can see how the Color data relates to the other columns.\n",
@@ -458,7 +458,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "503350dc",
+   "id": "0f7c1900",
    "metadata": {},
    "outputs": [
     {
@@ -466,13 +466,7 @@
      "output_type": "stream",
      "text": [
       "/usr/share/miniconda/envs/open-machine-learning-jupyter-book/lib/python3.9/site-packages/seaborn/categorical.py:3544: UserWarning: 63.4% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n",
-      "  warnings.warn(msg, UserWarning)\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
+      "  warnings.warn(msg, UserWarning)\n",
       "/usr/share/miniconda/envs/open-machine-learning-jupyter-book/lib/python3.9/site-packages/seaborn/categorical.py:3544: UserWarning: 21.8% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n",
       "  warnings.warn(msg, UserWarning)\n"
      ]
@@ -506,7 +500,7 @@
      },
      "metadata": {
       "filenames": {
-       "image/png": "/home/runner/work/machine-learning/machine-learning/open-machine-learning-jupyter-book/_build/jupyter_execute/ml-fundamentals/regression/logistic-regression_8_4.png"
+       "image/png": "/home/runner/work/machine-learning/machine-learning/open-machine-learning-jupyter-book/_build/jupyter_execute/ml-fundamentals/regression/logistic-regression_8_3.png"
       }
      },
      "output_type": "display_data"
@@ -518,7 +512,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2110b782",
+   "id": "467c09b8",
    "metadata": {},
    "source": [
     "### Violin plot\n",
@@ -531,13 +525,13 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "8b85f672",
+   "id": "8143b2c7",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<seaborn.axisgrid.FacetGrid at 0x7f947ddb6f10>"
+       "<seaborn.axisgrid.FacetGrid at 0x7efbda001c70>"
       ]
      },
      "execution_count": 6,
@@ -566,7 +560,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c7f749fc",
+   "id": "f5c090f8",
    "metadata": {},
    "source": [
     "```{seealso}\n",
@@ -601,7 +595,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "1ba188a1",
+   "id": "a8d2257e",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -617,7 +611,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ba86270d",
+   "id": "1adf3d1b",
    "metadata": {},
    "source": [
     "2 . Now you can train your model, by calling `fit()` with your training data, and print out its result:"
@@ -626,7 +620,7 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "ff6fff3b",
+   "id": "43572010",
    "metadata": {},
    "outputs": [
     {
@@ -668,7 +662,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "728a1167",
+   "id": "d989d3ef",
    "metadata": {},
    "source": [
     "```{note}\n",
@@ -689,7 +683,7 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "260926cc",
+   "id": "e0fb82f8",
    "metadata": {},
    "outputs": [
     {
@@ -711,7 +705,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "119d17a1",
+   "id": "bb27834c",
    "metadata": {},
    "source": [
     "Take a look at your model's confusion matrix:\n",
@@ -774,7 +768,7 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "b30e9f86",
+   "id": "a636b899",
    "metadata": {},
    "outputs": [
     {
@@ -814,7 +808,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a2f14ba1",
+   "id": "457715cf",
    "metadata": {},
    "source": [
     "Using Seaborn again, plot the model's [Receiving Operating Characteristic](https://scikit-learn.org/stable/auto_examples/model_selection/plot_roc.html?highlight=roc) or ROC. ROC curves are often used to get a view of the output of a classifier in terms of its true vs. false positives. \"ROC curves typically feature true positive rate on the Y axis, and false positive rate on the X axis.\" Thus, the steepness of the curve and the space between the midpoint line and the curve matter: you want a curve that quickly heads up and over the line. In our case, there are false positives to start with, and then the line heads up and over properly:\n",
@@ -825,7 +819,7 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "id": "c86d2845",
+   "id": "4ee71722",
    "metadata": {},
    "outputs": [
     {
@@ -843,7 +837,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e9b35e1e",
+   "id": "6ea3706a",
    "metadata": {},
    "source": [
     "The result is `0.6976998904709748`. Given that the AUC ranges from 0 to 1, you want a big score, since a model that is 100% correct in its predictions will have an AUC of 1; in this case, the model is _pretty good_.\n",
diff --git a/_sources/ml-fundamentals/regression/managing-data.ipynb b/_sources/ml-fundamentals/regression/managing-data.ipynb
index 0fe5eb37bd..c6e9a17c50 100644
--- a/_sources/ml-fundamentals/regression/managing-data.ipynb
+++ b/_sources/ml-fundamentals/regression/managing-data.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "59bfbc17",
+   "id": "d8109778",
    "metadata": {},
    "source": [
     "# Managing data\n",
@@ -74,7 +74,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "5810be16",
+   "id": "8dea9c22",
    "metadata": {
     "tags": [
      "output_scroll"
@@ -90,7 +90,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5888d97e",
+   "id": "198c6cd2",
    "metadata": {},
    "source": [
     "```{note}\n",
@@ -103,7 +103,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "0a241282",
+   "id": "8c9f4a9b",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -112,7 +112,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e27c9cc6",
+   "id": "23651730",
    "metadata": {},
    "source": [
     "```{warning}\n",
@@ -125,7 +125,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "25d23b1f",
+   "id": "bf851727",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -135,7 +135,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1239430f",
+   "id": "9cbb4068",
    "metadata": {},
    "source": [
     "### Second, determine average price of pumpkin\n",
@@ -150,7 +150,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "dc3a4ad8",
+   "id": "67fa57a8",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -160,7 +160,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9baf256f",
+   "id": "a2b50d45",
    "metadata": {},
    "source": [
     "```{seealso}\n",
@@ -173,7 +173,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "c1490de6",
+   "id": "1afa60b7",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -182,7 +182,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ea3a146a",
+   "id": "a2c6bf3c",
    "metadata": {},
    "source": [
     "Printing out your dataframe will show you a clean, tidy dataset on which you can build your new regression model.\n",
@@ -203,7 +203,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "083faa89",
+   "id": "98137b04",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -212,7 +212,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5223ef68",
+   "id": "37f357df",
    "metadata": {},
    "source": [
     "If you print the data now, you can see that you are only getting the 415 or so rows of data containing pumpkins by the bushel.\n",
@@ -227,7 +227,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "54dd9ce7",
+   "id": "cf5c2116",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -238,7 +238,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "de2c060d",
+   "id": "d287f4c4",
    "metadata": {},
    "source": [
     "```{seealso}\n",
@@ -273,7 +273,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "b5c02c9c",
+   "id": "2c942536",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -282,7 +282,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ce4ec4f1",
+   "id": "eef31647",
    "metadata": {},
    "source": [
     "2 . Rerun the entire notebook to refresh.\n",
@@ -293,7 +293,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "68d738ce",
+   "id": "67db59e9",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -305,7 +305,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "11415c9c",
+   "id": "aeb300ab",
    "metadata": {},
    "source": [
     "```{note}\n",
@@ -326,7 +326,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "a2ba1f93",
+   "id": "827d31a9",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -336,7 +336,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "55d72cb0",
+   "id": "901d5048",
    "metadata": {},
    "source": [
     "```{note}\n",
diff --git a/_sources/ml-fundamentals/regression/regression-models-for-machine-learning.ipynb b/_sources/ml-fundamentals/regression/regression-models-for-machine-learning.ipynb
index c38c18a218..566cd22649 100644
--- a/_sources/ml-fundamentals/regression/regression-models-for-machine-learning.ipynb
+++ b/_sources/ml-fundamentals/regression/regression-models-for-machine-learning.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "8916cc22",
+   "id": "bcfbfdf6",
    "metadata": {},
    "source": [
     "# Regression models for Machine Learning\n",
diff --git a/_sources/ml-fundamentals/regression/tools-of-the-trade.ipynb b/_sources/ml-fundamentals/regression/tools-of-the-trade.ipynb
index 5ed2ab3cfc..089d394673 100644
--- a/_sources/ml-fundamentals/regression/tools-of-the-trade.ipynb
+++ b/_sources/ml-fundamentals/regression/tools-of-the-trade.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "36bfb534",
+   "id": "84da9ded",
    "metadata": {},
    "source": [
     "# Tools of the trade\n",
@@ -132,7 +132,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "4bd2e0eb",
+   "id": "f6897773",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -143,7 +143,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a1a63082",
+   "id": "3fc80134",
    "metadata": {},
    "source": [
     "Above you are importing `matplotlib`, `numpy` and you are importing `datasets`, `linear_model` and `model_selection` from `sklearn`. `model_selection` is used for splitting data into training and test sets.\n",
@@ -175,7 +175,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "90eeab51",
+   "id": "fa13016e",
    "metadata": {},
    "outputs": [
     {
@@ -196,7 +196,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f4462ae6",
+   "id": "11d66949",
    "metadata": {},
    "source": [
     "What you are getting back as a response, is a tuple. What you are doing is to assign the two first values of the tuple to `X` and `y` respectively. Learn more [about tuples](https://wikipedia.org/wiki/Tuple).\n",
@@ -213,7 +213,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "8053886f",
+   "id": "f9e4805e",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -222,7 +222,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e17553f3",
+   "id": "96027d6c",
    "metadata": {},
    "source": [
     "```{note}\n",
@@ -235,7 +235,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "9b4e43dd",
+   "id": "a12b9c87",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -244,7 +244,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4bdca3f2",
+   "id": "09d5baaa",
    "metadata": {},
    "source": [
     "4 . Now you are ready to train your model! Load up the linear regression model and train it with your X and y training sets using `model.fit()`:"
@@ -253,7 +253,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "c71b1b17",
+   "id": "9dc54747",
    "metadata": {},
    "outputs": [
     {
@@ -277,7 +277,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a80418b5",
+   "id": "dfdeeba6",
    "metadata": {},
    "source": [
     "```{note}\n",
@@ -290,7 +290,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "351495cd",
+   "id": "1abcf5b6",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -299,7 +299,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "12f92e18",
+   "id": "4b2a2877",
    "metadata": {},
    "source": [
     "6 . Now it's time to show the data in a plot. Matplotlib is a very useful tool for this task. Create a scatterplot of all the X and y test data, and use the prediction to draw a line in the most appropriate place, between the model's data groupings."
@@ -308,12 +308,12 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "3b00ec10",
+   "id": "1b1988c0",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -337,7 +337,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "16e66b4f",
+   "id": "c02bb081",
    "metadata": {},
    "source": [
     "```{seealso}\n",
diff --git a/_sources/prerequisites/python-programming-advanced.ipynb b/_sources/prerequisites/python-programming-advanced.ipynb
index 8a0f4ec610..0e7b39749a 100644
--- a/_sources/prerequisites/python-programming-advanced.ipynb
+++ b/_sources/prerequisites/python-programming-advanced.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "2cb48220",
+   "id": "96d59947",
    "metadata": {},
    "source": [
     "<details>\n",
@@ -39,7 +39,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "4bf66527",
+   "id": "41b78c58",
    "metadata": {
     "tags": [
      "hide-input"
@@ -54,7 +54,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f2257fb9",
+   "id": "3ce4ced2",
    "metadata": {},
    "source": [
     "## Control flow\n",
@@ -73,7 +73,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "80e32b44",
+   "id": "c9fe5e0e",
    "metadata": {},
    "outputs": [
     {
@@ -102,7 +102,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9623cd77",
+   "id": "5c0d3fc7",
    "metadata": {},
    "source": [
     "<iframe width=\"800\" height=\"500\" frameborder=\"0\" src=\"https://pythontutor.com/iframe-embed.html#code=def%20get_age_group%28age%29%3A%0A%20%20%20%20if%20age%20%3E%3D%200%20and%20age%20%3C%3D%209%3A%0A%20%20%20%20%20%20%20%20print%28%22You%20are%20a%20child!%22%29%0A%20%20%20%20elif%20age%20%3E%209%20and%20age%20%3C%3D%2018%3A%0A%20%20%20%20%20%20%20%20print%28%22You%20are%20an%20adolescent!%22%29%0A%20%20%20%20elif%20age%20%3E%2018%20and%20age%20%3C%3D%2065%3A%0A%20%20%20%20%20%20%20%20print%28%22You%20are%20an%20adult!%22%29%0A%20%20%20%20else%3A%20%23%20age%20%3E%2065%0A%20%20%20%20%20%20%20%20print%28%22Golden%20ages!%22%29%0A%0Aget_age_group%283%29%0Aget_age_group%2850%29&codeDivHeight=400&codeDivWidth=350&cumulative=false&curInstr=0&heapPrimitives=nevernest&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false\"> </iframe>\n",
@@ -121,7 +121,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "6fcdad41",
+   "id": "9f23001c",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -136,7 +136,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a1c11142",
+   "id": "1bf32781",
    "metadata": {},
    "source": [
     "If you need to modify the sequence you are iterating over while inside the loop (for example to duplicate selected items), it is recommended that you first make a copy. Iterating over a sequence does not implicitly make a copy. The slice notation makes this especially convenient:"
@@ -145,7 +145,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "3a800463",
+   "id": "145c5725",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -156,7 +156,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d334ce61",
+   "id": "18846430",
    "metadata": {},
    "source": [
     "Otherwise, with `for w in words`, the example would attempt to create an infinite list, inserting defenestrate over and over again."
@@ -165,7 +165,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "6f9e5667",
+   "id": "8ee4dc1b",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -174,7 +174,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1400fdaf",
+   "id": "9b73d61a",
    "metadata": {},
    "source": [
     "If you do need to iterate over a sequence of numbers, the built-in function `range()` comes in handy. It generates arithmetic progressions:"
@@ -183,7 +183,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "36be6a7f",
+   "id": "b53ae9c0",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -197,7 +197,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1a961c83",
+   "id": "b83980b2",
    "metadata": {},
    "source": [
     "Let's see a more complex example about how to find the prims by in an embedded `for` loops."
@@ -206,7 +206,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "32bfe97f",
+   "id": "6543ab4e",
    "metadata": {},
    "outputs": [
     {
@@ -234,7 +234,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "77d03750",
+   "id": "cea7909f",
    "metadata": {},
    "source": [
     "<iframe width=\"800\" height=\"500\" frameborder=\"0\" src=\"https://pythontutor.com/iframe-embed.html#code=%23%20find%20primes%20using%20a%20for-else%20construct%0Afor%20n%20in%20range%282,%2010%29%3A%0A%20%20%20%20x_range%20%3D%20range%282,%20n%29%0A%20%20%20%20for%20x%20in%20x_range%3A%0A%20%20%20%20%20%20%20%20if%20n%20%25%20x%20%3D%3D%200%3A%0A%20%20%20%20%20%20%20%20%20%20%20%20break%0A%20%20%20%20else%3A%0A%20%20%20%20%20%20%20%20%23%20loop%20fell%20through%20without%20finding%20a%20factor%0A%20%20%20%20%20%20%20%20print%28n%29&codeDivHeight=400&codeDivWidth=350&cumulative=false&curInstr=30&heapPrimitives=nevernest&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false\"> </iframe>\n",
@@ -245,7 +245,7 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "eec229f7",
+   "id": "ae2486c9",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -261,7 +261,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e228a59e",
+   "id": "9fe83f7c",
    "metadata": {},
    "source": [
     "Or simply use `enumerate()`."
@@ -270,7 +270,7 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "b1d80985",
+   "id": "807d2dcf",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -284,7 +284,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "42e779c2",
+   "id": "d307a62a",
    "metadata": {},
    "source": [
     "When looping through dictionaries, the key and corresponding value can be retrieved at the same time using the `items()` method."
@@ -293,7 +293,7 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "c14bbdf0",
+   "id": "e1b8498d",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -311,7 +311,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "262fb84f",
+   "id": "963b392e",
    "metadata": {},
    "source": [
     "When looping through a sequence, the position index and corresponding value can be retrieved at the same time using the `enumerate()` function"
@@ -320,7 +320,7 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "id": "0fceaa28",
+   "id": "b0f06d3f",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -336,7 +336,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c68d509d",
+   "id": "46144c27",
    "metadata": {},
    "source": [
     "To loop over two or more sequences at the same time, the entries can be paired with the `zip()` function. In many ways, the object returned by `range()` behaves as if it is a list, but in fact, it isn’t. It is an object which returns the successive items of the desired sequence when you iterate over it, but it doesn’t really make the list, thus saving space.\n",
@@ -349,7 +349,7 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "id": "766a8e4b",
+   "id": "6a6f2eb5",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -361,7 +361,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6c3d7d69",
+   "id": "0f181a11",
    "metadata": {},
    "source": [
     "### The while statement\n",
@@ -381,7 +381,7 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "id": "5ef7280a",
+   "id": "f7116aac",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -399,7 +399,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b316963b",
+   "id": "37b67923",
    "metadata": {},
    "source": [
     "### The try statement\n",
@@ -423,7 +423,7 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "id": "192c874f",
+   "id": "4a8670bb",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -440,7 +440,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "32b3f1d1",
+   "id": "72deed5b",
    "metadata": {},
    "source": [
     "You can define as many exception blocks as you want, e.g. if you want to execute a special block of code for a special kind of error:"
@@ -449,7 +449,7 @@
   {
    "cell_type": "code",
    "execution_count": 15,
-   "id": "eb59f775",
+   "id": "bba7ddc5",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -466,7 +466,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4a644972",
+   "id": "045b84d2",
    "metadata": {},
    "source": [
     "You can use the else keyword to define a block of code to be executed if no errors were raised."
@@ -475,7 +475,7 @@
   {
    "cell_type": "code",
    "execution_count": 16,
-   "id": "4d9444e5",
+   "id": "34035d23",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -493,7 +493,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f3baea6a",
+   "id": "e32640aa",
    "metadata": {},
    "source": [
     "The final block, if specified, will be executed regardless if the `try` block raises an error or not."
@@ -502,7 +502,7 @@
   {
    "cell_type": "code",
    "execution_count": 17,
-   "id": "90427a91",
+   "id": "49d299a4",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -520,7 +520,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "831e3181",
+   "id": "d4eec4da",
    "metadata": {},
    "source": [
     "### The break statement\n",
@@ -535,7 +535,7 @@
   {
    "cell_type": "code",
    "execution_count": 18,
-   "id": "7dd7c84e",
+   "id": "009b69f6",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -557,7 +557,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1734cc46",
+   "id": "69f2c909",
    "metadata": {},
    "source": [
     "### The continue statement\n",
@@ -572,7 +572,7 @@
   {
    "cell_type": "code",
    "execution_count": 19,
-   "id": "76af261c",
+   "id": "792a7885",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -596,7 +596,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3027cceb",
+   "id": "164118f0",
    "metadata": {},
    "source": [
     "## Functions\n",
@@ -614,7 +614,7 @@
   {
    "cell_type": "code",
    "execution_count": 20,
-   "id": "226c8b86",
+   "id": "83e176ad",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -654,7 +654,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b215b361",
+   "id": "3f04bc48",
    "metadata": {},
    "source": [
     "Now call the function we just defined."
@@ -663,7 +663,7 @@
   {
    "cell_type": "code",
    "execution_count": 21,
-   "id": "1a16a10e",
+   "id": "31b41c0f",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -672,7 +672,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8eb7ec72",
+   "id": "773a5bfa",
    "metadata": {},
    "source": [
     "A function definition introduces the function name in the current symbol table. The value of the function name has a type that is recognized by the interpreter as a user-defined function. This value can be assigned to another name which can then also be used as a function.\n",
@@ -683,7 +683,7 @@
   {
    "cell_type": "code",
    "execution_count": 22,
-   "id": "1c7cc9b0",
+   "id": "2c87e71c",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -693,7 +693,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c41b242a",
+   "id": "cf55af3b",
    "metadata": {},
    "source": [
     "In Python, functions are first-class citizens, they are objects and that means we can do a lot of useful stuff with them.\n",
@@ -704,7 +704,7 @@
   {
    "cell_type": "code",
    "execution_count": 23,
-   "id": "3ffebe9d",
+   "id": "abcb9e1f",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -718,7 +718,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9a891494",
+   "id": "8d141e38",
    "metadata": {},
    "source": [
     "Define functions inside other functions."
@@ -727,7 +727,7 @@
   {
    "cell_type": "code",
    "execution_count": 24,
-   "id": "2b13e7c0",
+   "id": "0f027f47",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -743,7 +743,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b2620679",
+   "id": "e42896ee",
    "metadata": {},
    "source": [
     "Functions can be passed as parameters to other functions."
@@ -752,7 +752,7 @@
   {
    "cell_type": "code",
    "execution_count": 25,
-   "id": "620535f8",
+   "id": "305e6454",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -768,7 +768,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "81313c1c",
+   "id": "23d0c56a",
    "metadata": {},
    "source": [
     "Functions can return other functions. In other words, functions generate other functions."
@@ -777,7 +777,7 @@
   {
    "cell_type": "code",
    "execution_count": 26,
-   "id": "84167b1c",
+   "id": "b5753d77",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -793,7 +793,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "87c7779b",
+   "id": "795db666",
    "metadata": {},
    "source": [
     "Inner functions have access to the enclosing scope.\n",
@@ -804,7 +804,7 @@
   {
    "cell_type": "code",
    "execution_count": 27,
-   "id": "3b17a0cc",
+   "id": "d9a6b0e1",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -821,7 +821,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "26d8e0a1",
+   "id": "2592e28d",
    "metadata": {},
    "source": [
     "### Scopes of variables inside functions (global and nonlocal statements)\n",
@@ -849,7 +849,7 @@
   {
    "cell_type": "code",
    "execution_count": 28,
-   "id": "168acc76",
+   "id": "628b0b7f",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -859,7 +859,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "623ff24c",
+   "id": "0b9b70ad",
    "metadata": {},
    "source": [
     "This is a scopes and namespaces example demonstrating how to reference the different scopes and namespaces, and how global and nonlocal affect variable binding:"
@@ -868,7 +868,7 @@
   {
    "cell_type": "code",
    "execution_count": 29,
-   "id": "7b224df8",
+   "id": "3eb20ace",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -936,7 +936,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3d9f1929",
+   "id": "2f7174ed",
    "metadata": {},
    "source": [
     "On this example you may see how accessing and changing global variables from within inner functions might make debugging more difficult and code to be less predictable. Since you might have expected that `test_variable` should still be equal to 'initial global value' but it was changed by \"someone\" you need to know about the CONTEXT of who had changed that. So once again access global and non-local scope only if you know what you're doing otherwise it might be considered a bad practice.\n",
@@ -953,7 +953,7 @@
   {
    "cell_type": "code",
    "execution_count": 30,
-   "id": "c89406dc",
+   "id": "72b3459b",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -966,7 +966,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "15574e48",
+   "id": "34e69e3f",
    "metadata": {},
    "source": [
     "This function `power_of` can be called in several ways because it has default value for the second argument. First, we may call it omitting the second argument at all."
@@ -975,7 +975,7 @@
   {
    "cell_type": "code",
    "execution_count": 31,
-   "id": "91c16c78",
+   "id": "fcb612f1",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -984,7 +984,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5eea665c",
+   "id": "2a36c06a",
    "metadata": {},
    "source": [
     "We may also want to override the second argument by using the following function calls."
@@ -993,7 +993,7 @@
   {
    "cell_type": "code",
    "execution_count": 32,
-   "id": "57d4c50c",
+   "id": "b143255d",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1003,7 +1003,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3ef9ae8f",
+   "id": "356fc541",
    "metadata": {},
    "source": [
     "### Keyword arguments\n",
@@ -1020,7 +1020,7 @@
   {
    "cell_type": "code",
    "execution_count": 33,
-   "id": "fe724481",
+   "id": "f0c98222",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1075,7 +1075,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7036524a",
+   "id": "171a526a",
    "metadata": {},
    "source": [
     "But all the following calls would be invalid:\n",
@@ -1124,7 +1124,7 @@
   {
    "cell_type": "code",
    "execution_count": 34,
-   "id": "8ffa646e",
+   "id": "68c3a9cc",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1137,7 +1137,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c1109aa1",
+   "id": "0029437c",
    "metadata": {},
    "source": [
     "Normally, these variadic arguments will be last in the list of formal parameters, because they scoop up all remaining input arguments that are passed to the function. Any formal parameters which occur after the `*args` parameter are **keyword-only arguments**, meaning that they can only be used as keywords rather than positional arguments."
@@ -1146,7 +1146,7 @@
   {
    "cell_type": "code",
    "execution_count": 35,
-   "id": "09853a6e",
+   "id": "6e098d89",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1159,7 +1159,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "91a08ecb",
+   "id": "eca4aa61",
    "metadata": {},
    "source": [
     "### Unpacking argument lists (* and ** statements)\n",
@@ -1176,7 +1176,7 @@
   {
    "cell_type": "code",
    "execution_count": 36,
-   "id": "bdcc7d05",
+   "id": "c0cf6ac3",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1197,7 +1197,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7ea5707d",
+   "id": "483433c0",
    "metadata": {},
    "source": [
     "### Lambda expressions (lambda statement)\n",
@@ -1214,7 +1214,7 @@
   {
    "cell_type": "code",
    "execution_count": 37,
-   "id": "105e243d",
+   "id": "4a7bf973",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1230,7 +1230,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "103193eb",
+   "id": "06b68cc8",
    "metadata": {},
    "source": [
     "Another use of lambda is to pass a small function as an argument."
@@ -1239,7 +1239,7 @@
   {
    "cell_type": "code",
    "execution_count": 38,
-   "id": "75118e50",
+   "id": "4b1014aa",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1252,7 +1252,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0f3421a4",
+   "id": "dbe94cf9",
    "metadata": {},
    "source": [
     "### Documentation strings\n",
@@ -1271,7 +1271,7 @@
   {
    "cell_type": "code",
    "execution_count": 39,
-   "id": "dc276cd2",
+   "id": "2ae6bdf2",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1285,7 +1285,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6c4ad414",
+   "id": "266b074a",
    "metadata": {},
    "source": [
     "The Python parser does not strip indentation from multi-line string literals in Python, so tools that process documentation have to strip indentation if desired. This is done using the following convention. The first non-blank line after the first line of the string determines the amount of indentation for the entire documentation string. (We can’t use the first line since it is generally adjacent to the string’s opening quotes so its indentation is not apparent in the string literal.) Whitespace “equivalent” to this indentation is then stripped from the start of all lines of the string. Lines that are indented less should not occur, but if they occur all their leading whitespace should be stripped. Equivalence of whitespace should be tested after expansion of tabs (to 8 spaces, normally).\n",
@@ -1313,7 +1313,7 @@
   {
    "cell_type": "code",
    "execution_count": 40,
-   "id": "d248b41f",
+   "id": "aa9ddd86",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1325,7 +1325,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8416b37c",
+   "id": "03a0a432",
    "metadata": {},
    "source": [
     "### Function decorators\n",
@@ -1344,7 +1344,7 @@
   {
    "cell_type": "code",
    "execution_count": 41,
-   "id": "fcb29800",
+   "id": "7f70ab24",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1354,7 +1354,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a66751c7",
+   "id": "d771c667",
    "metadata": {},
    "source": [
     "This function decorates another functions output with `<p>` tag."
@@ -1363,7 +1363,7 @@
   {
    "cell_type": "code",
    "execution_count": 42,
-   "id": "c79b6588",
+   "id": "4a884abc",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1375,7 +1375,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1c91bed6",
+   "id": "39e24490",
    "metadata": {},
    "source": [
     "Now, let's call our decorator and pass the function we want to decorate to it."
@@ -1384,7 +1384,7 @@
   {
    "cell_type": "code",
    "execution_count": 43,
-   "id": "5b5cf1d4",
+   "id": "2c34bd8f",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1393,7 +1393,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "927b1544",
+   "id": "bc8d5b42",
    "metadata": {},
    "source": [
     "Here we go, we've just decorated the function output without changing the function itself."
@@ -1402,7 +1402,7 @@
   {
    "cell_type": "code",
    "execution_count": 44,
-   "id": "5327b69c",
+   "id": "904a5a1a",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1412,7 +1412,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4a4eb9ac",
+   "id": "0b7ce33e",
    "metadata": {},
    "source": [
     "Now, Python makes creating and using decorators a bit cleaner and nicer for the programmer through some syntactic sugar. There is a neat shortcut for that, which is to mention the name of the decorating function before the function to be decorated. The name of the decorator should be prepended with an `@` symbol."
@@ -1421,7 +1421,7 @@
   {
    "cell_type": "code",
    "execution_count": 45,
-   "id": "ee616355",
+   "id": "cc4e4e52",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1434,7 +1434,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "04069218",
+   "id": "612e965f",
    "metadata": {},
    "source": [
     "Now let's consider we wanted to decorate our greeting function with one more function to wrap a `div` in the string output."
@@ -1443,7 +1443,7 @@
   {
    "cell_type": "code",
    "execution_count": 46,
-   "id": "1bad7fd5",
+   "id": "36412a61",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1456,7 +1456,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0d6b25d8",
+   "id": "081bed29",
    "metadata": {},
    "source": [
     "With the basic approach, decorating get_text would be along the lines of `greeting_with_div_p = decorate_with_div(decorate_with_p(greeting_with_p))`\n",
@@ -1467,7 +1467,7 @@
   {
    "cell_type": "code",
    "execution_count": 47,
-   "id": "0faa9d1b",
+   "id": "2eebf6be",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1481,7 +1481,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "90f57ea6",
+   "id": "2162c3c9",
    "metadata": {},
    "source": [
     "One important thing to notice here is that the order of setting our decorators matters. If the order was different in the example above, the output would have been different.\n",
@@ -1494,7 +1494,7 @@
   {
    "cell_type": "code",
    "execution_count": 48,
-   "id": "dfe83b45",
+   "id": "740022a7",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1515,7 +1515,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "69d4caaf",
+   "id": "41fa0a78",
    "metadata": {},
    "source": [
     "## Classes\n",
@@ -1534,7 +1534,7 @@
   {
    "cell_type": "code",
    "execution_count": 49,
-   "id": "3478a416",
+   "id": "c5fb6006",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1559,7 +1559,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "53ace611",
+   "id": "d57f795a",
    "metadata": {},
    "source": [
     "When a class definition is entered, a new namespace is created, and used as the local scope — thus, all assignments to local variables go into this new namespace. In particular, function definitions bind the name of the new function here.\n",
@@ -1570,7 +1570,7 @@
   {
    "cell_type": "code",
    "execution_count": 50,
-   "id": "4f2d8f56",
+   "id": "0a408642",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1582,7 +1582,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c1b6463a",
+   "id": "8c815403",
    "metadata": {},
    "source": [
     "### Class objects\n",
@@ -1606,7 +1606,7 @@
   {
    "cell_type": "code",
    "execution_count": 51,
-   "id": "ca5bcf8b",
+   "id": "3360e6a3",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1627,7 +1627,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "95dd157c",
+   "id": "b8861c47",
    "metadata": {},
    "source": [
     " `__doc__` is also a valid attribute, returning the docstring belonging to the class:\n",
@@ -1642,7 +1642,7 @@
   {
    "cell_type": "code",
    "execution_count": 52,
-   "id": "ef46e117",
+   "id": "6c6ea1fd",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1652,7 +1652,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0db4e414",
+   "id": "05cc8de8",
    "metadata": {},
    "source": [
     "CLASS INSTANTIATION uses function notation. Just pretend that the class object is a parameterless function that returns a new instance of the class. For example (assuming the above class):"
@@ -1661,7 +1661,7 @@
   {
    "cell_type": "code",
    "execution_count": 53,
-   "id": "c5e7e7c7",
+   "id": "415877ee",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1673,7 +1673,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "823fb6df",
+   "id": "edae974f",
    "metadata": {},
    "source": [
     "The instantiation operation (“calling” a class object) creates an empty object. Many classes like to create objects with instances customized to a specific initial state. Therefore a class may define a special method named `__init__()`, like this:"
@@ -1682,7 +1682,7 @@
   {
    "cell_type": "code",
    "execution_count": 54,
-   "id": "0925d346",
+   "id": "e32bd38e",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1707,7 +1707,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "35d09440",
+   "id": "ec37b773",
    "metadata": {},
    "source": [
     "### Instance objects\n",
@@ -1727,7 +1727,7 @@
   {
    "cell_type": "code",
    "execution_count": 55,
-   "id": "5f599524",
+   "id": "4a225fdb",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1746,7 +1746,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "36696979",
+   "id": "1344c879",
    "metadata": {},
    "source": [
     "### Methods objects\n",
@@ -1763,7 +1763,7 @@
   {
    "cell_type": "code",
    "execution_count": 56,
-   "id": "bf98ac80",
+   "id": "17d4cb98",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1782,7 +1782,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "968eaa5b",
+   "id": "f176e42f",
    "metadata": {},
    "source": [
     "A method is a function that “belongs to” an object. (In Python, the term method is not unique to class instances: other object types can have methods as well. For example, list objects have methods called `append`, `insert`, `remove`, `sort`, and so on. However, in the following discussion, we’ll use the term method exclusively to mean methods of class instance objects, unless explicitly stated otherwise.)\n",
@@ -1793,7 +1793,7 @@
   {
    "cell_type": "code",
    "execution_count": 57,
-   "id": "5a107d20",
+   "id": "41e64e25",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1803,7 +1803,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "081ca8e9",
+   "id": "b0f465e4",
    "metadata": {},
    "source": [
     "However, it is not necessary to call a method right away: `counter.get_counter()` is a method object, and can be stored away and called at a later time. For example:"
@@ -1812,7 +1812,7 @@
   {
    "cell_type": "code",
    "execution_count": 58,
-   "id": "a3aa71bc",
+   "id": "51bb302c",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1822,7 +1822,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a9202ec8",
+   "id": "6d63cea9",
    "metadata": {},
    "source": [
     "But be aware that `counter.get_counter()` is not the same thing as `MyCounter.get_counter()` — it is a method object, not a function object.\n",
@@ -1835,7 +1835,7 @@
   {
    "cell_type": "code",
    "execution_count": 59,
-   "id": "b43433fe",
+   "id": "9418c69a",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1845,7 +1845,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "475d1ad8",
+   "id": "eba3bd96",
    "metadata": {},
    "source": [
     "### Class and instance variables\n",
@@ -1862,7 +1862,7 @@
   {
    "cell_type": "code",
    "execution_count": 60,
-   "id": "eaab06bb",
+   "id": "0e44b088",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1889,7 +1889,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fbae4f1c",
+   "id": "695bea74",
    "metadata": {},
    "source": [
     "Shared data can have possibly surprising effects involving mutable objects such as lists and dictionaries. For example, the tricks list in the following code should not be used as a class variable because just a single list would be shared by all Dog instances.\n",
@@ -1900,7 +1900,7 @@
   {
    "cell_type": "code",
    "execution_count": 61,
-   "id": "eedde8df",
+   "id": "4c5b0a07",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1933,7 +1933,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d66f929a",
+   "id": "0fdfa321",
    "metadata": {},
    "source": [
     "The correct design of the class should use an instance variable instead:"
@@ -1942,7 +1942,7 @@
   {
    "cell_type": "code",
    "execution_count": 62,
-   "id": "a7468fa1",
+   "id": "08f5dfb0",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1971,7 +1971,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0a0ef326",
+   "id": "ee03c897",
    "metadata": {},
    "source": [
     "### Inheritance\n",
@@ -1988,7 +1988,7 @@
   {
    "cell_type": "code",
    "execution_count": 63,
-   "id": "0bd9a243",
+   "id": "4b1c628d",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2004,7 +2004,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8599184b",
+   "id": "4bda1d61",
    "metadata": {},
    "source": [
     "The base class (in our case `Person`) must be defined in a scope containing the derived class definition. In place of a base class name, other arbitrary expressions are also allowed.\n",
@@ -2019,7 +2019,7 @@
   {
    "cell_type": "code",
    "execution_count": 64,
-   "id": "902cb223",
+   "id": "d96af58d",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2038,7 +2038,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7f61fcc4",
+   "id": "c4122d12",
    "metadata": {},
    "source": [
     "There’s nothing special about the instantiation of derived classes: `DerivedClassName()` creates a new instance of the class. Method references are resolved as follows: the corresponding class attribute is searched, descending the chain of base classes if necessary, and the method reference is valid if this yields a function object."
@@ -2047,7 +2047,7 @@
   {
    "cell_type": "code",
    "execution_count": 65,
-   "id": "647c72ab",
+   "id": "9dc25ff7",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2061,7 +2061,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f0207e7c",
+   "id": "27642b91",
    "metadata": {},
    "source": [
     "Python has two built-in functions that work with inheritance:\n",
@@ -2073,7 +2073,7 @@
   {
    "cell_type": "code",
    "execution_count": 66,
-   "id": "8b4b136d",
+   "id": "e308e3e4",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2089,7 +2089,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0a085391",
+   "id": "94b9dc7a",
    "metadata": {},
    "source": [
     "### Multiple inheritance\n",
@@ -2104,7 +2104,7 @@
   {
    "cell_type": "code",
    "execution_count": 67,
-   "id": "9d658c5f",
+   "id": "977ddf21",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2137,7 +2137,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7063e4a2",
+   "id": "48a4bac7",
    "metadata": {},
    "source": [
     "Python supports a form of multiple inheritances as well. A class definition with multiple base classes looks like this:"
@@ -2146,7 +2146,7 @@
   {
    "cell_type": "code",
    "execution_count": 68,
-   "id": "1b859be1",
+   "id": "f9c1ff33",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2161,7 +2161,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "15bbf0d3",
+   "id": "5a49deb9",
    "metadata": {},
    "source": [
     "For most purposes, in the simplest cases, you can think of the search for attributes inherited from a parent class as depth-first, left-to-right, not searching twice in the same class where there is an overlap in the hierarchy. Thus, if an attribute is not found in `CalendarClock`, it is searched for in `Clock`, then (recursively) in the base classes of `Clock`, and if it was not found there, it was searched for in `Calendar`, and so on.\n",
@@ -2204,7 +2204,7 @@
   {
    "cell_type": "code",
    "execution_count": 69,
-   "id": "83396c51",
+   "id": "c691c0a4",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2213,7 +2213,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "dc4aeb61",
+   "id": "fcf634f7",
    "metadata": {},
    "source": [
     "There is a variant of the import statement that imports names from a module directly into the importing module’s symbol table. For example:"
@@ -2222,7 +2222,7 @@
   {
    "cell_type": "code",
    "execution_count": 70,
-   "id": "459700b3",
+   "id": "d5f81352",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2232,7 +2232,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2aafec1f",
+   "id": "deaefb6c",
    "metadata": {},
    "source": [
     "There is even a variant to import all names that a module defines. This imports all names except those beginning with an underscore `_`. In most cases Python programmers do not use this facility since it introduces an unknown set of names into the interpreter, possibly hiding some things you have already defined.\n",
@@ -2243,7 +2243,7 @@
   {
    "cell_type": "code",
    "execution_count": 71,
-   "id": "4556bae1",
+   "id": "02e636f2",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2252,7 +2252,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "dfbf85c8",
+   "id": "b0e30927",
    "metadata": {},
    "source": [
     "It can also be used when utilizing from with similar effects:"
@@ -2261,7 +2261,7 @@
   {
    "cell_type": "code",
    "execution_count": 72,
-   "id": "3347d505",
+   "id": "815c6453",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2270,7 +2270,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0490578b",
+   "id": "839f755f",
    "metadata": {},
    "source": [
     "When a module named spam is imported, the interpreter first searches for a built-in module with that name. If not found, it then searches for a file named spam.py in a list of directories given by the variable `sys.path`. `sys.path` is initialized from these locations:\n",
@@ -2283,7 +2283,7 @@
   {
    "cell_type": "code",
    "execution_count": 73,
-   "id": "b950bc24",
+   "id": "7b46f5c9",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2299,7 +2299,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "412aa792",
+   "id": "f58ec859",
    "metadata": {},
    "source": [
     "If you intend to use a function often you can assign it to a local name."
@@ -2308,7 +2308,7 @@
   {
    "cell_type": "code",
    "execution_count": 74,
-   "id": "a46c6ad1",
+   "id": "34958a77",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2318,7 +2318,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4de37c49",
+   "id": "1b3f166e",
    "metadata": {},
    "source": [
     "The built-in function `dir()` is used to find out which names a module defines. It returns a sorted list of strings."
@@ -2327,7 +2327,7 @@
   {
    "cell_type": "code",
    "execution_count": 75,
-   "id": "8bc003fc",
+   "id": "b8d5770e",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2347,7 +2347,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "dc8332f5",
+   "id": "4729a90d",
    "metadata": {},
    "source": [
     "### Packages\n",
@@ -2362,7 +2362,7 @@
   {
    "cell_type": "code",
    "execution_count": 76,
-   "id": "2b3ea1dc",
+   "id": "f786ce18",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2371,7 +2371,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f359e5a7",
+   "id": "48c0b17e",
    "metadata": {},
    "source": [
     "An alternative way of importing the submodule is:"
@@ -2380,7 +2380,7 @@
   {
    "cell_type": "code",
    "execution_count": 77,
-   "id": "236c6a94",
+   "id": "145c6f67",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2390,7 +2390,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "624de547",
+   "id": "471de85f",
    "metadata": {},
    "source": [
     "Yet another variation is to import the desired function or variable directly:"
@@ -2399,7 +2399,7 @@
   {
    "cell_type": "code",
    "execution_count": 78,
-   "id": "8d64ae7d",
+   "id": "fef89fdd",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2408,7 +2408,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2dab110a",
+   "id": "4b55a084",
    "metadata": {},
    "source": [
     "Note that when using from package import item, the item can be either a submodule (or subpackage) of the package or some other name defined in the package, like a function, class, or variable.\n",
@@ -2421,7 +2421,7 @@
   {
    "cell_type": "code",
    "execution_count": 79,
-   "id": "36042f13",
+   "id": "b36d1c5f",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2432,7 +2432,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8ecc1c3b",
+   "id": "56bdeae3",
    "metadata": {},
    "source": [
     "## Errors and exceptions\n",
@@ -2462,7 +2462,7 @@
   {
    "cell_type": "code",
    "execution_count": 80,
-   "id": "3c7eb940",
+   "id": "5749885d",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2480,7 +2480,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f7c407f4",
+   "id": "5607ff93",
    "metadata": {},
    "source": [
     "Let's simulate an undefined variable access exception."
@@ -2489,7 +2489,7 @@
   {
    "cell_type": "code",
    "execution_count": 81,
-   "id": "4b042f9a",
+   "id": "32ebd2c9",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2508,7 +2508,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c62f8617",
+   "id": "cc9bf967",
    "metadata": {},
    "source": [
     "A `try` statement may have more than one `except` clause, to specify handlers for different exceptions. At most one handler will be executed. Handlers only handle exceptions that occur in the corresponding `try` clause, not in other handlers of the same `try` statement. An `except` clause may name multiple exceptions as a parenthesized tuple, for example:"
@@ -2517,7 +2517,7 @@
   {
    "cell_type": "code",
    "execution_count": 82,
-   "id": "4485d983",
+   "id": "37c86216",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2535,7 +2535,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f6dffdd6",
+   "id": "e7124aab",
    "metadata": {},
    "source": [
     "Exception handlers may be chained."
@@ -2544,7 +2544,7 @@
   {
    "cell_type": "code",
    "execution_count": 83,
-   "id": "9e007c1b",
+   "id": "f9fa897c",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2565,7 +2565,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2f1d1750",
+   "id": "88627885",
    "metadata": {},
    "source": [
     "The `try … except` statement has an optional `else` clause, which, when present, must follow all `except` clauses. It is useful for code that must be executed if the `try` clause does not raise an exception. For example:"
@@ -2574,7 +2574,7 @@
   {
    "cell_type": "code",
    "execution_count": 84,
-   "id": "95c35150",
+   "id": "02cf246f",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2597,7 +2597,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "43c069b3",
+   "id": "cc7492bd",
    "metadata": {},
    "source": [
     "### Raising exceptions (raise statement)\n",
@@ -2614,7 +2614,7 @@
   {
    "cell_type": "code",
    "execution_count": 85,
-   "id": "0a748aee",
+   "id": "fb7dc8ae",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2634,7 +2634,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "94a640f0",
+   "id": "622db911",
    "metadata": {},
    "source": [
     "Programs may name their own exceptions by creating a new exception class. Exceptions should typically be derived from the `Exception` class, either directly or indirectly. Most exceptions are defined with names that end in `Error,` similar to the naming of the standard exceptions. Many standard modules define their own exceptions to report errors that may occur in functions they define.\n",
@@ -2645,7 +2645,7 @@
   {
    "cell_type": "code",
    "execution_count": 86,
-   "id": "1b28324c",
+   "id": "357049f2",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2667,7 +2667,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "25564b84",
+   "id": "5dbde032",
    "metadata": {},
    "source": [
     "## Your turn! 🚀\n",
diff --git a/assets/code/prerequisites/__pycache__/fibonacci_module.cpython-39.pyc b/assets/code/prerequisites/__pycache__/fibonacci_module.cpython-39.pyc
index 167020b27a84deabae340c6ace58615eac5a5765..73303411a786bcf077d95d7b82ac6a2cb1b96995 100644
GIT binary patch
delta 20
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     <link rel="stylesheet" type="text/css" href="../../../_static/design-style.b7bb847fb20b106c3d81b95245e65545.min.css" />
     
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diff --git a/assignments/ml-fundamentals/build-classification-model.html b/assignments/ml-fundamentals/build-classification-model.html
index 138be47f6b..98dccaf2f3 100644
--- a/assignments/ml-fundamentals/build-classification-model.html
+++ b/assignments/ml-fundamentals/build-classification-model.html
@@ -27,8 +27,8 @@
     <link rel="stylesheet" type="text/css" href="../../_static/mystnb.css" />
     <link rel="stylesheet" type="text/css" href="../../_static/sphinx-thebe.css" />
     <link rel="stylesheet" type="text/css" href="../../_static/drawio.css" />
-    <link rel="stylesheet" type="text/css" href="../../_static/style.css" />
     <link rel="stylesheet" type="text/css" href="../../_static/youtube.css" />
+    <link rel="stylesheet" type="text/css" href="../../_static/style.css" />
     <link rel="stylesheet" type="text/css" href="../../_static/design-style.b7bb847fb20b106c3d81b95245e65545.min.css" />
     
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diff --git a/assignments/ml-fundamentals/build-classification-models.html b/assignments/ml-fundamentals/build-classification-models.html
index a5cdce9885..edc5a94099 100644
--- a/assignments/ml-fundamentals/build-classification-models.html
+++ b/assignments/ml-fundamentals/build-classification-models.html
@@ -27,8 +27,8 @@
     <link rel="stylesheet" type="text/css" href="../../_static/mystnb.css" />
     <link rel="stylesheet" type="text/css" href="../../_static/sphinx-thebe.css" />
     <link rel="stylesheet" type="text/css" href="../../_static/drawio.css" />
-    <link rel="stylesheet" type="text/css" href="../../_static/style.css" />
     <link rel="stylesheet" type="text/css" href="../../_static/youtube.css" />
+    <link rel="stylesheet" type="text/css" href="../../_static/style.css" />
     <link rel="stylesheet" type="text/css" href="../../_static/design-style.b7bb847fb20b106c3d81b95245e65545.min.css" />
     
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diff --git a/assignments/ml-fundamentals/create-a-regression-model.html b/assignments/ml-fundamentals/create-a-regression-model.html
index c6ac2aa259..7aae48a9d3 100644
--- a/assignments/ml-fundamentals/create-a-regression-model.html
+++ b/assignments/ml-fundamentals/create-a-regression-model.html
@@ -27,8 +27,8 @@
     <link rel="stylesheet" type="text/css" href="../../_static/mystnb.css" />
     <link rel="stylesheet" type="text/css" href="../../_static/sphinx-thebe.css" />
     <link rel="stylesheet" type="text/css" href="../../_static/drawio.css" />
-    <link rel="stylesheet" type="text/css" href="../../_static/style.css" />
     <link rel="stylesheet" type="text/css" href="../../_static/youtube.css" />
+    <link rel="stylesheet" type="text/css" href="../../_static/style.css" />
     <link rel="stylesheet" type="text/css" href="../../_static/design-style.b7bb847fb20b106c3d81b95245e65545.min.css" />
     
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diff --git a/assignments/ml-fundamentals/delicious-asian-and-indian-cuisines.html b/assignments/ml-fundamentals/delicious-asian-and-indian-cuisines.html
index 640e16d2cf..50a6536be6 100644
--- a/assignments/ml-fundamentals/delicious-asian-and-indian-cuisines.html
+++ b/assignments/ml-fundamentals/delicious-asian-and-indian-cuisines.html
@@ -27,8 +27,8 @@
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     <link rel="stylesheet" type="text/css" href="../../_static/sphinx-thebe.css" />
     <link rel="stylesheet" type="text/css" href="../../_static/drawio.css" />
-    <link rel="stylesheet" type="text/css" href="../../_static/style.css" />
     <link rel="stylesheet" type="text/css" href="../../_static/youtube.css" />
+    <link rel="stylesheet" type="text/css" href="../../_static/style.css" />
     <link rel="stylesheet" type="text/css" href="../../_static/design-style.b7bb847fb20b106c3d81b95245e65545.min.css" />
     
   <!-- Pre-loaded scripts that we'll load fully later -->
diff --git a/assignments/ml-fundamentals/explore-classification-methods.html b/assignments/ml-fundamentals/explore-classification-methods.html
index 04ed545e16..70558d4bd4 100644
--- a/assignments/ml-fundamentals/explore-classification-methods.html
+++ b/assignments/ml-fundamentals/explore-classification-methods.html
@@ -27,8 +27,8 @@
     <link rel="stylesheet" type="text/css" href="../../_static/mystnb.css" />
     <link rel="stylesheet" type="text/css" href="../../_static/sphinx-thebe.css" />
     <link rel="stylesheet" type="text/css" href="../../_static/drawio.css" />
-    <link rel="stylesheet" type="text/css" href="../../_static/style.css" />
     <link rel="stylesheet" type="text/css" href="../../_static/youtube.css" />
+    <link rel="stylesheet" type="text/css" href="../../_static/style.css" />
     <link rel="stylesheet" type="text/css" href="../../_static/design-style.b7bb847fb20b106c3d81b95245e65545.min.css" />
     
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diff --git a/assignments/ml-fundamentals/exploring-visualizations.html b/assignments/ml-fundamentals/exploring-visualizations.html
index 86e4988888..58b01ad848 100644
--- a/assignments/ml-fundamentals/exploring-visualizations.html
+++ b/assignments/ml-fundamentals/exploring-visualizations.html
@@ -27,8 +27,8 @@
     <link rel="stylesheet" type="text/css" href="../../_static/mystnb.css" />
     <link rel="stylesheet" type="text/css" href="../../_static/sphinx-thebe.css" />
     <link rel="stylesheet" type="text/css" href="../../_static/drawio.css" />
-    <link rel="stylesheet" type="text/css" href="../../_static/style.css" />
     <link rel="stylesheet" type="text/css" href="../../_static/youtube.css" />
+    <link rel="stylesheet" type="text/css" href="../../_static/style.css" />
     <link rel="stylesheet" type="text/css" href="../../_static/design-style.b7bb847fb20b106c3d81b95245e65545.min.css" />
     
   <!-- Pre-loaded scripts that we'll load fully later -->
diff --git a/assignments/ml-fundamentals/linear-and-polynomial-regression.html b/assignments/ml-fundamentals/linear-and-polynomial-regression.html
index 261ee7cf5d..6e46be06d3 100644
--- a/assignments/ml-fundamentals/linear-and-polynomial-regression.html
+++ b/assignments/ml-fundamentals/linear-and-polynomial-regression.html
@@ -27,8 +27,8 @@
     <link rel="stylesheet" type="text/css" href="../../_static/mystnb.css" />
     <link rel="stylesheet" type="text/css" href="../../_static/sphinx-thebe.css" />
     <link rel="stylesheet" type="text/css" href="../../_static/drawio.css" />
-    <link rel="stylesheet" type="text/css" href="../../_static/style.css" />
     <link rel="stylesheet" type="text/css" href="../../_static/youtube.css" />
+    <link rel="stylesheet" type="text/css" href="../../_static/style.css" />
     <link rel="stylesheet" type="text/css" href="../../_static/design-style.b7bb847fb20b106c3d81b95245e65545.min.css" />
     
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diff --git a/assignments/ml-fundamentals/linear-regression/california_housing.html b/assignments/ml-fundamentals/linear-regression/california_housing.html
index 48ed7b863c..c1564986c7 100644
--- a/assignments/ml-fundamentals/linear-regression/california_housing.html
+++ b/assignments/ml-fundamentals/linear-regression/california_housing.html
@@ -27,8 +27,8 @@
     <link rel="stylesheet" type="text/css" href="../../../_static/mystnb.css" />
     <link rel="stylesheet" type="text/css" href="../../../_static/sphinx-thebe.css" />
     <link rel="stylesheet" type="text/css" href="../../../_static/drawio.css" />
-    <link rel="stylesheet" type="text/css" href="../../../_static/style.css" />
     <link rel="stylesheet" type="text/css" href="../../../_static/youtube.css" />
+    <link rel="stylesheet" type="text/css" href="../../../_static/style.css" />
     <link rel="stylesheet" type="text/css" href="../../../_static/design-style.b7bb847fb20b106c3d81b95245e65545.min.css" />
     
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diff --git a/assignments/ml-fundamentals/linear-regression/gradient-descent.html b/assignments/ml-fundamentals/linear-regression/gradient-descent.html
index 07418c8b9c..36392fe641 100644
--- a/assignments/ml-fundamentals/linear-regression/gradient-descent.html
+++ b/assignments/ml-fundamentals/linear-regression/gradient-descent.html
@@ -27,8 +27,8 @@
     <link rel="stylesheet" type="text/css" href="../../../_static/mystnb.css" />
     <link rel="stylesheet" type="text/css" href="../../../_static/sphinx-thebe.css" />
     <link rel="stylesheet" type="text/css" href="../../../_static/drawio.css" />
-    <link rel="stylesheet" type="text/css" href="../../../_static/style.css" />
     <link rel="stylesheet" type="text/css" href="../../../_static/youtube.css" />
+    <link rel="stylesheet" type="text/css" href="../../../_static/style.css" />
     <link rel="stylesheet" type="text/css" href="../../../_static/design-style.b7bb847fb20b106c3d81b95245e65545.min.css" />
     
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diff --git a/assignments/ml-fundamentals/linear-regression/linear-regression-from-scratch.html b/assignments/ml-fundamentals/linear-regression/linear-regression-from-scratch.html
index 3462f58889..9355285161 100644
--- a/assignments/ml-fundamentals/linear-regression/linear-regression-from-scratch.html
+++ b/assignments/ml-fundamentals/linear-regression/linear-regression-from-scratch.html
@@ -27,8 +27,8 @@
     <link rel="stylesheet" type="text/css" href="../../../_static/mystnb.css" />
     <link rel="stylesheet" type="text/css" href="../../../_static/sphinx-thebe.css" />
     <link rel="stylesheet" type="text/css" href="../../../_static/drawio.css" />
-    <link rel="stylesheet" type="text/css" href="../../../_static/style.css" />
     <link rel="stylesheet" type="text/css" href="../../../_static/youtube.css" />
+    <link rel="stylesheet" type="text/css" href="../../../_static/style.css" />
     <link rel="stylesheet" type="text/css" href="../../../_static/design-style.b7bb847fb20b106c3d81b95245e65545.min.css" />
     
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@@ -1538,6 +1538,18 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal nav-link" href="#model-development">
    37.49.3. Model Development
   </a>
+  <ul class="nav section-nav flex-column">
+   <li class="toc-h3 nav-item toc-entry">
+    <a class="reference internal nav-link" href="#define-loss-function">
+     37.49.3.1. Define loss function
+    </a>
+   </li>
+   <li class="toc-h3 nav-item toc-entry">
+    <a class="reference internal nav-link" href="#use-gradient-descent-to-train-the-model">
+     37.49.3.2. Use gradient descent to train the model
+    </a>
+   </li>
+  </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#model-evaluation">
@@ -1549,11 +1561,6 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
    37.49.5. Conclusion
   </a>
  </li>
- <li class="toc-h2 nav-item toc-entry">
-  <a class="reference internal nav-link" href="#acknowledgments">
-   37.49.6. Acknowledgments
-  </a>
- </li>
 </ul>
 
     </nav>
@@ -1587,6 +1594,18 @@ <h2> Contents </h2>
   <a class="reference internal nav-link" href="#model-development">
    37.49.3. Model Development
   </a>
+  <ul class="nav section-nav flex-column">
+   <li class="toc-h3 nav-item toc-entry">
+    <a class="reference internal nav-link" href="#define-loss-function">
+     37.49.3.1. Define loss function
+    </a>
+   </li>
+   <li class="toc-h3 nav-item toc-entry">
+    <a class="reference internal nav-link" href="#use-gradient-descent-to-train-the-model">
+     37.49.3.2. Use gradient descent to train the model
+    </a>
+   </li>
+  </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#model-evaluation">
@@ -1598,11 +1617,6 @@ <h2> Contents </h2>
    37.49.5. Conclusion
   </a>
  </li>
- <li class="toc-h2 nav-item toc-entry">
-  <a class="reference internal nav-link" href="#acknowledgments">
-   37.49.6. Acknowledgments
-  </a>
- </li>
 </ul>
 
                         </nav>
@@ -1617,11 +1631,14 @@ <h2> Contents </h2>
 <h1><span class="section-number">37.49. </span>Linear Regression Implementation from Scratch<a class="headerlink" href="#linear-regression-implementation-from-scratch" title="Permalink to this headline">#</a></h1>
 <section id="introduction">
 <h2><span class="section-number">37.49.1. </span>Introduction<a class="headerlink" href="#introduction" title="Permalink to this headline">#</a></h2>
+<p>In previous sections, we have gained some understanding of linear regression, gradient descent, evaluation metrics, and the role of the loss function in this regression technique. In summary, linear regression utilizes gradient descent to optimize the model’s parameters by minimizing the loss function. This enables it to establish a linear relationship between the input features and the target variable, making it a powerful algorithm for predicting continuous values.</p>
 <p>Linear regression is a widely used method in data analysis to describe the relationship between independent variables and a dependent variable using a linear equation. It aims to minimize the error between predicted and actual values by finding the best-fit line or surface. Linear regression can be used for predicting trends, exploring relationships, and identifying patterns in the data.</p>
+<p>This chapter will apply the previously learnt knowledge to implement a linear regression model from scratch. The chapter includes steps for data preparation, model development, and model evaluation, and ultimately summarises the process of developing and evaluating linear regression models.</p>
 <p>In Python, we can use libraries like NumPy and Pandas for data handling and analysis. We will also use the Matplotlib library for visualizing data and model performance. Here’s a simple Python code snippet to demonstrate how a linear regression model works:</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">os</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
 <span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
 <span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
 
@@ -1633,8 +1650,12 @@ <h2><span class="section-number">37.49.1. </span>Introduction<a class="headerlin
 <span class="c1"># Create a DataFrame from the data</span>
 <span class="n">data</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">([</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">],</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">),</span> <span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;X&#39;</span><span class="p">,</span> <span class="s1">&#39;y&#39;</span><span class="p">])</span>
 
-<span class="c1"># Save the data to a CSV file</span>
-<span class="n">data</span><span class="o">.</span><span class="n">to_csv</span><span class="p">(</span><span class="s1">&#39;data.csv&#39;</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+<span class="c1"># Create the &#39;Temp&#39; directory if it doesn&#39;t exist</span>
+<span class="k">if</span> <span class="ow">not</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="s1">&#39;./tmp&#39;</span><span class="p">):</span>
+    <span class="n">os</span><span class="o">.</span><span class="n">makedirs</span><span class="p">(</span><span class="s1">&#39;./tmp&#39;</span><span class="p">)</span>
+
+<span class="c1"># Save the data to a CSV file in the &#39;Temp&#39; directory</span>
+<span class="n">data</span><span class="o">.</span><span class="n">to_csv</span><span class="p">(</span><span class="s1">&#39;./tmp/data.csv&#39;</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
 
 <span class="c1"># Plot the data</span>
 <span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
@@ -1645,7 +1666,7 @@ <h2><span class="section-number">37.49.1. </span>Introduction<a class="headerlin
 </div>
 </div>
 <div class="cell_output docutils container">
-<img alt="../../../_images/linear-regression-from-scratch_4_0.png" src="../../../_images/linear-regression-from-scratch_4_0.png" />
+<img alt="../../../_images/linear-regression-from-scratch_6_0.png" src="../../../_images/linear-regression-from-scratch_6_0.png" />
 </div>
 </div>
 </section>
@@ -1659,23 +1680,31 @@ <h2><span class="section-number">37.49.2. </span>Data Preparation<a class="heade
 <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
 <span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
 <span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">train_test_split</span>
 
 <span class="c1"># Load the dataset</span>
-<span class="n">data</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s1">&#39;data.csv&#39;</span><span class="p">)</span>
+<span class="n">data</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s1">&#39;./Temp/data.csv&#39;</span><span class="p">)</span>
 
 <span class="c1"># Perform exploratory data analysis</span>
 <span class="c1"># Display the first few rows of the data</span>
 <span class="nb">print</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">head</span><span class="p">())</span>
 
-<span class="c1"># Check for missing values</span>
-<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;X的缺失值数量：&quot;</span><span class="p">,</span> <span class="n">data</span><span class="p">[</span><span class="s1">&#39;X&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">isnull</span><span class="p">()</span><span class="o">.</span><span class="n">sum</span><span class="p">())</span>
-<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;y的缺失值数量：&quot;</span><span class="p">,</span> <span class="n">data</span><span class="p">[</span><span class="s1">&#39;y&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">isnull</span><span class="p">()</span><span class="o">.</span><span class="n">sum</span><span class="p">())</span>
+<span class="c1"># Split the dataset into training and testing sets</span>
+<span class="n">X</span> <span class="o">=</span> <span class="n">data</span><span class="p">[[</span><span class="s1">&#39;X&#39;</span><span class="p">]]</span>
+<span class="n">y</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="s1">&#39;y&#39;</span><span class="p">]</span>
+<span class="n">X_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">0.2</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">42</span><span class="p">)</span>
 
 <span class="c1"># Visualize the relationship between features and target variable</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="s1">&#39;X&#39;</span><span class="p">],</span> <span class="n">data</span><span class="p">[</span><span class="s1">&#39;y&#39;</span><span class="p">])</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Training Set&#39;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Testing Set&#39;</span><span class="p">)</span>
 <span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">&#39;X&#39;</span><span class="p">)</span>
 <span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">&#39;y&#39;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
 <span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+
+<span class="c1"># Print the sizes of training and testing sets</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Training set size:&quot;</span><span class="p">,</span> <span class="n">X_train</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">y_train</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Testing set size:&quot;</span><span class="p">,</span> <span class="n">X_test</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">y_test</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
 </pre></div>
 </div>
 </div>
@@ -1686,132 +1715,1186 @@ <h2><span class="section-number">37.49.2. </span>Data Preparation<a class="heade
 2  1.463988  8.483724
 3  1.197317  5.604382
 4  0.312037  4.716440
-X的缺失值数量： 0
-y的缺失值数量： 0
 </pre></div>
 </div>
-<img alt="../../../_images/linear-regression-from-scratch_6_1.png" src="../../../_images/linear-regression-from-scratch_6_1.png" />
+<img alt="../../../_images/linear-regression-from-scratch_8_1.png" src="../../../_images/linear-regression-from-scratch_8_1.png" />
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Training set size: (80, 1) (80,)
+Testing set size: (20, 1) (20,)
+</pre></div>
+</div>
 </div>
 </div>
-<p>In this code snippet, we first import the necessary libraries, such as NumPy and Pandas. Then, we load the dataset using the read_csv() function from Pandas, replacing ‘data.csv’ with the actual path to your dataset.</p>
-<p>Next, we perform exploratory data analysis by printing the first few rows of the dataset using head(), displaying the shape of the dataset using shape, and printing summary statistics using describe().</p>
-<p>After that, we check for missing values in the dataset using isnull().sum(). If there are missing values, we can handle them accordingly. In this example, we simply drop rows with missing values using dropna().</p>
-<p>Then, we perform any necessary preprocessing steps, such as feature scaling or encoding categorical variables. Finally, we split the dataset into input features (X) and the target variable (y), convert them to NumPy arrays using np.array(), and verify their dimensions using shape.</p>
 </section>
 <section id="model-development">
 <h2><span class="section-number">37.49.3. </span>Model Development<a class="headerlink" href="#model-development" title="Permalink to this headline">#</a></h2>
 <p>Once we have prepared the data, we can proceed with developing the linear regression model. This involves deriving the mathematical formula for linear regression, implementing the formula in code, defining a cost function, and implementing the gradient descent algorithm to train the model.</p>
 <p>Let’s take a look at the code snippet below to understand how we can develop the linear regression model:</p>
+<section id="define-loss-function">
+<h3><span class="section-number">37.49.3.1. </span>Define loss function<a class="headerlink" href="#define-loss-function" title="Permalink to this headline">#</a></h3>
+<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>Calculate the loss between the true target variable and the predicted target variable.
+
+Parameters:
+- y_true: The true target variable (numpy array or pandas Series)
+- y_pred: The predicted target variable (numpy array or pandas Series)
+
+Returns:
+- loss: The calculated loss (float)
+</pre></div>
+</div>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Deriving the mathematical formula for linear regression</span>
-<span class="c1"># Implementing the formula in code</span>
-<span class="k">class</span> <span class="nc">LinearRegression</span><span class="p">:</span>
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">coefficients</span> <span class="o">=</span> <span class="kc">None</span>
-
-    <span class="k">def</span> <span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
-        <span class="c1"># Add a column of ones to X for the bias term</span>
-        <span class="n">X</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">c_</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="mi">1</span><span class="p">)),</span> <span class="n">X</span><span class="p">]</span>
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Define the loss function</span>
+<span class="k">def</span> <span class="nf">loss_function</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">):</span>
+    <span class="c1"># Calculate the mean squared error</span>
+    <span class="n">mse</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">((</span><span class="n">y_true</span> <span class="o">-</span> <span class="n">y_pred</span><span class="p">)</span> <span class="o">**</span> <span class="mi">2</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">mse</span>
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+<section id="use-gradient-descent-to-train-the-model">
+<h3><span class="section-number">37.49.3.2. </span>Use gradient descent to train the model<a class="headerlink" href="#use-gradient-descent-to-train-the-model" title="Permalink to this headline">#</a></h3>
+<p>The <code class="docutils literal notranslate"><span class="pre">gradient_descent</span></code> function is responsible for performing gradient descent to optimize the coefficients of the linear regression model. Here is a breakdown of the steps involved:</p>
+<ol class="simple">
+<li><p>Scale the data: The input features (<code class="docutils literal notranslate"><span class="pre">X_train</span></code> and <code class="docutils literal notranslate"><span class="pre">X_test</span></code>) are scaled using the <code class="docutils literal notranslate"><span class="pre">StandardScaler</span></code> from scikit-learn. This ensures that all features have a similar scale, which can improve the performance of the gradient descent algorithm.</p></li>
+<li><p>Add a bias term: A column of ones is added to the input features (<code class="docutils literal notranslate"><span class="pre">X_train</span></code>) to account for the bias term in the linear regression model.</p></li>
+<li><p>Initialize coefficients: The coefficients are initialized with zeros. The number of coefficients is equal to the number of features plus one (including the bias term).</p></li>
+<li><p>Perform gradient descent: The function iterates over a specified number of iterations. In each iteration, the following steps are performed:</p>
+<ul class="simple">
+<li><p>Compute the predictions (<code class="docutils literal notranslate"><span class="pre">y_pred</span></code>) by multiplying the input features (<code class="docutils literal notranslate"><span class="pre">X_train_with_bias</span></code>) with the coefficients.</p></li>
+<li><p>Compute the gradients by taking the dot product of the transposed input features (<code class="docutils literal notranslate"><span class="pre">X_train_with_bias.T</span></code>) and the difference between the predictions and the true target variable (<code class="docutils literal notranslate"><span class="pre">y_train</span></code>).</p></li>
+<li><p>Update the coefficients by subtracting the learning rate multiplied by the gradients.</p></li>
+<li><p>Compute the loss by calculating the mean squared error between the true target variable (<code class="docutils literal notranslate"><span class="pre">y_train</span></code>) and the predictions (<code class="docutils literal notranslate"><span class="pre">y_pred</span></code>).</p></li>
+</ul>
+</li>
+<li><p>Make predictions on the test set: The function applies the same preprocessing steps to the test set (<code class="docutils literal notranslate"><span class="pre">X_test</span></code>) and computes the predictions (<code class="docutils literal notranslate"><span class="pre">y_pred_test</span></code>) using the updated coefficients.</p></li>
+</ol>
+<p>This function is a key component in training the linear regression model from scratch. It allows us to iteratively update the coefficients based on the gradients, gradually improving the model’s performance.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Define the gradient descent function</span>
+<span class="k">def</span> <span class="nf">gradient_descent</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">,</span> <span class="n">learning_rate</span><span class="p">,</span> <span class="n">num_iterations</span><span class="p">):</span>
+    <span class="c1"># Scale the data</span>
+    <span class="n">scaler</span> <span class="o">=</span> <span class="n">StandardScaler</span><span class="p">()</span>
+    <span class="n">X_train_scaled</span> <span class="o">=</span> <span class="n">scaler</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">X_train</span><span class="o">.</span><span class="n">values</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>
+    <span class="n">X_test_scaled</span> <span class="o">=</span> <span class="n">scaler</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">X_test</span><span class="o">.</span><span class="n">values</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>
 
-        <span class="c1"># Compute the coefficients using the normal equation</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">coefficients</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">inv</span><span class="p">(</span><span class="n">X</span><span class="o">.</span><span class="n">T</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">X</span><span class="p">))</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">X</span><span class="o">.</span><span class="n">T</span><span class="p">)</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">y</span><span class="p">)</span>
+    <span class="c1"># Add a column of ones to X for the bias term</span>
+    <span class="n">X_train_with_bias</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">c_</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="n">X_train_scaled</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="mi">1</span><span class="p">)),</span> <span class="n">X_train_scaled</span><span class="p">]</span>
 
-    <span class="k">def</span> <span class="nf">predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
-        <span class="c1"># Add a column of ones to X for the bias term</span>
-        <span class="n">X</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">c_</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="mi">1</span><span class="p">)),</span> <span class="n">X</span><span class="p">]</span>
+    <span class="c1"># Initialize the coefficients with zeros</span>
+    <span class="n">coefficients</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">X_train_with_bias</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="mi">1</span><span class="p">))</span>
 
-        <span class="c1"># Predict the target variable</span>
-        <span class="n">y_pred</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">coefficients</span><span class="p">)</span>
+    <span class="c1"># Perform gradient descent</span>
+    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">num_iterations</span><span class="p">):</span>
+        <span class="c1"># Compute the predictions</span>
+        <span class="n">y_pred</span> <span class="o">=</span> <span class="n">X_train_with_bias</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">coefficients</span><span class="p">)</span>
 
-        <span class="k">return</span> <span class="n">y_pred</span>
+        <span class="c1"># Compute the gradients</span>
+        <span class="n">gradients</span> <span class="o">=</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">X_train_with_bias</span><span class="o">.</span><span class="n">T</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">y_pred</span> <span class="o">-</span> <span class="n">y_train</span><span class="o">.</span><span class="n">values</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span> 
 
-<span class="c1"># Defining cost function</span>
-<span class="k">def</span> <span class="nf">mean_squared_error</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">):</span>
-    <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">((</span><span class="n">y_true</span> <span class="o">-</span> <span class="n">y_pred</span><span class="p">)</span> <span class="o">**</span> <span class="mi">2</span><span class="p">)</span>
+        <span class="c1"># Update the coefficients</span>
+        <span class="n">coefficients</span> <span class="o">-=</span> <span class="n">learning_rate</span> <span class="o">*</span> <span class="n">gradients</span>
 
-<span class="c1"># 训练模型</span>
-<span class="n">regressor</span> <span class="o">=</span> <span class="n">LinearRegression</span><span class="p">()</span>
-<span class="n">regressor</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
+        <span class="c1"># Compute the loss</span>
+        <span class="n">loss</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">((</span><span class="n">y_train</span><span class="o">.</span><span class="n">values</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span> <span class="o">-</span> <span class="n">y_pred</span><span class="p">)</span> <span class="o">**</span> <span class="mi">2</span><span class="p">)</span>
+        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Iteration:&quot;</span><span class="p">,</span> <span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="p">,</span> <span class="s2">&quot;Loss:&quot;</span><span class="p">,</span> <span class="n">loss</span><span class="p">)</span>
 
-<span class="c1"># 使用训练好的模型进行预测</span>
-<span class="n">y_pred</span> <span class="o">=</span> <span class="n">regressor</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
+    <span class="c1"># Make predictions on the test set</span>
+    <span class="n">X_test_with_bias</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">c_</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="n">X_test_scaled</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="mi">1</span><span class="p">)),</span> <span class="n">X_test_scaled</span><span class="p">]</span>
+    <span class="n">y_pred_test</span> <span class="o">=</span> <span class="n">X_test_with_bias</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">coefficients</span><span class="p">)</span>
 
-<span class="c1"># Evaluating the model</span>
-<span class="n">mse</span> <span class="o">=</span> <span class="n">mean_squared_error</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Mean Squared Error:&#39;</span><span class="p">,</span> <span class="n">mse</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">coefficients</span><span class="p">,</span> <span class="n">y_pred_test</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>Next, we train the linear regression model and make predictions on the test set.</p>
+<p>First, we import the required library and set the learning rate and number of iterations for gradient descent. We then call the gradient descent function and pass in the training and test data, the learning rate, and the number of iterations. The function returns the optimised coefficients and predictions for the test set. Finally, we store the returned results in the variables coefficients and ypred test.</p>
+<p>By running this code, we can train a linear regression model using gradient descent and get the prediction results on the test set to further analyse and evaluate the performance of the model.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">train_test_split</span>
+<span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="kn">import</span> <span class="n">StandardScaler</span>
+<span class="c1"># Perform gradient descent to train the model</span>
+<span class="n">learning_rate</span> <span class="o">=</span> <span class="mf">0.01</span>
+<span class="n">num_iterations</span> <span class="o">=</span> <span class="mi">1000</span>
+<span class="n">coefficients</span><span class="p">,</span> <span class="n">y_pred_test</span> <span class="o">=</span> <span class="n">gradient_descent</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">,</span> <span class="n">learning_rate</span><span class="p">,</span> <span class="n">num_iterations</span><span class="p">)</span>
 </pre></div>
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Mean Squared Error: 0.8065845639670534
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Iteration: 1 Loss: 49.252973836909085
+Iteration: 2 Loss: 18.27358504939668
+Iteration: 3 Loss: 7.121005085892223
+Iteration: 4 Loss: 3.1060762990306165
+Iteration: 5 Loss: 1.6607019357604422
+Iteration: 6 Loss: 1.1403671649831801
+Iteration: 7 Loss: 0.9530466475033655
+Iteration: 8 Loss: 0.8856112612106326
+Iteration: 9 Loss: 0.8613345221452491
+Iteration: 10 Loss: 0.8525948960817107
+Iteration: 11 Loss: 0.8494486306988371
+Iteration: 12 Loss: 0.8483159751610024
+Iteration: 13 Loss: 0.8479082191673818
+Iteration: 14 Loss: 0.8477614270096787
+Iteration: 15 Loss: 0.8477085818329055
+Iteration: 16 Loss: 0.8476895575692671
+Iteration: 17 Loss: 0.8476827088343573
+Iteration: 18 Loss: 0.8476802432897899
+Iteration: 19 Loss: 0.8476793556937455
+Iteration: 20 Loss: 0.8476790361591695
+Iteration: 21 Loss: 0.847678921126722
+Iteration: 22 Loss: 0.8476788797150412
+Iteration: 23 Loss: 0.8476788648068361
+Iteration: 24 Loss: 0.8476788594398821
+Iteration: 25 Loss: 0.8476788575077785
+Iteration: 26 Loss: 0.8476788568122215
+Iteration: 27 Loss: 0.847678856561821
+Iteration: 28 Loss: 0.8476788564716766
+Iteration: 29 Loss: 0.8476788564392248
+Iteration: 30 Loss: 0.847678856427542
+Iteration: 31 Loss: 0.8476788564233363
+Iteration: 32 Loss: 0.8476788564218222
+Iteration: 33 Loss: 0.8476788564212772
+Iteration: 34 Loss: 0.8476788564210811
+Iteration: 35 Loss: 0.8476788564210102
+Iteration: 36 Loss: 0.8476788564209847
+Iteration: 37 Loss: 0.8476788564209757
+Iteration: 38 Loss: 0.8476788564209723
+Iteration: 39 Loss: 0.8476788564209713
+Iteration: 40 Loss: 0.8476788564209707
+Iteration: 41 Loss: 0.8476788564209705
+Iteration: 42 Loss: 0.8476788564209705
+Iteration: 43 Loss: 0.8476788564209705
+Iteration: 44 Loss: 0.8476788564209704
+Iteration: 45 Loss: 0.8476788564209707
+Iteration: 46 Loss: 0.8476788564209705
+Iteration: 47 Loss: 0.8476788564209705
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+Iteration: 936 Loss: 0.8476788564209705
+Iteration: 937 Loss: 0.8476788564209705
+Iteration: 938 Loss: 0.8476788564209705
+Iteration: 939 Loss: 0.8476788564209705
+Iteration: 940 Loss: 0.8476788564209705
+Iteration: 941 Loss: 0.8476788564209705
+Iteration: 942 Loss: 0.8476788564209705
+Iteration: 943 Loss: 0.8476788564209705
+Iteration: 944 Loss: 0.8476788564209705
+Iteration: 945 Loss: 0.8476788564209705
+Iteration: 946 Loss: 0.8476788564209705
+Iteration: 947 Loss: 0.8476788564209705
+Iteration: 948 Loss: 0.8476788564209705
+Iteration: 949 Loss: 0.8476788564209705
+Iteration: 950 Loss: 0.8476788564209705
+Iteration: 951 Loss: 0.8476788564209705
+Iteration: 952 Loss: 0.8476788564209705
+Iteration: 953 Loss: 0.8476788564209705
+Iteration: 954 Loss: 0.8476788564209705
+Iteration: 955 Loss: 0.8476788564209705
+Iteration: 956 Loss: 0.8476788564209705
+Iteration: 957 Loss: 0.8476788564209705
+Iteration: 958 Loss: 0.8476788564209705
+Iteration: 959 Loss: 0.8476788564209705
+Iteration: 960 Loss: 0.8476788564209705
+Iteration: 961 Loss: 0.8476788564209705
+Iteration: 962 Loss: 0.8476788564209705
+Iteration: 963 Loss: 0.8476788564209705
+Iteration: 964 Loss: 0.8476788564209705
+Iteration: 965 Loss: 0.8476788564209705
+Iteration: 966 Loss: 0.8476788564209705
+Iteration: 967 Loss: 0.8476788564209705
+Iteration: 968 Loss: 0.8476788564209705
+Iteration: 969 Loss: 0.8476788564209705
+Iteration: 970 Loss: 0.8476788564209705
+Iteration: 971 Loss: 0.8476788564209705
+Iteration: 972 Loss: 0.8476788564209705
+Iteration: 973 Loss: 0.8476788564209705
+Iteration: 974 Loss: 0.8476788564209705
+Iteration: 975 Loss: 0.8476788564209705
+Iteration: 976 Loss: 0.8476788564209705
+Iteration: 977 Loss: 0.8476788564209705
+Iteration: 978 Loss: 0.8476788564209705
+Iteration: 979 Loss: 0.8476788564209705
+Iteration: 980 Loss: 0.8476788564209705
+Iteration: 981 Loss: 0.8476788564209705
+Iteration: 982 Loss: 0.8476788564209705
+Iteration: 983 Loss: 0.8476788564209705
+Iteration: 984 Loss: 0.8476788564209705
+Iteration: 985 Loss: 0.8476788564209705
+Iteration: 986 Loss: 0.8476788564209705
+Iteration: 987 Loss: 0.8476788564209705
+Iteration: 988 Loss: 0.8476788564209705
+Iteration: 989 Loss: 0.8476788564209705
+Iteration: 990 Loss: 0.8476788564209705
+Iteration: 991 Loss: 0.8476788564209705
+Iteration: 992 Loss: 0.8476788564209705
+Iteration: 993 Loss: 0.8476788564209705
+Iteration: 994 Loss: 0.8476788564209705
+Iteration: 995 Loss: 0.8476788564209705
+Iteration: 996 Loss: 0.8476788564209705
+Iteration: 997 Loss: 0.8476788564209705
+Iteration: 998 Loss: 0.8476788564209705
+Iteration: 999 Loss: 0.8476788564209705
+Iteration: 1000 Loss: 0.8476788564209705
 </pre></div>
 </div>
 </div>
 </div>
-<p>In this code snippet, we first derive the mathematical formula for linear regression. Then, we implement the formula in code by creating a <code class="docutils literal notranslate"><span class="pre">LinearRegression</span></code> class. The <code class="docutils literal notranslate"><span class="pre">fit()</span></code> method is used to train the model using the normal equation, and the <code class="docutils literal notranslate"><span class="pre">predict()</span></code> method is used to make predictions on new data points.</p>
-<p>We also define a cost function called <code class="docutils literal notranslate"><span class="pre">mean_squared_error()</span></code> to evaluate the performance of the model. Additionally, we can implement the gradient descent algorithm to train the model iteratively if desired.</p>
-<p>Finally, we train the model using the <code class="docutils literal notranslate"><span class="pre">fit()</span></code> method, make predictions using the <code class="docutils literal notranslate"><span class="pre">predict()</span></code> method, and evaluate the model’s performance using the mean squared error (MSE).</p>
+</section>
 </section>
 <section id="model-evaluation">
 <h2><span class="section-number">37.49.4. </span>Model Evaluation<a class="headerlink" href="#model-evaluation" title="Permalink to this headline">#</a></h2>
 <p>After training the linear regression model, it is important to evaluate its performance to assess how well it is able to make predictions. In this section, we will discuss some commonly used evaluation metrics for regression models.</p>
 <p>When evaluating a machine learning model, we often use certain metrics to measure its performance. Here are some commonly used metrics and plotting methods:</p>
-<ol class="simple">
-<li><p><strong>Mean Absolute Error (MAE):</strong> MAE is the average absolute difference between the predicted values and the actual values. It measures the average magnitude of the model’s errors. A lower MAE value indicates better predictive performance of the model.</p></li>
-<li><p><strong>Root Mean Squared Error (RMSE):</strong> RMSE is the square root of the average of the squared differences between the predicted values and the actual values. Compared to MAE, RMSE is more sensitive to large errors because it penalizes squared errors. Similar to MAE, a lower RMSE value indicates better predictive performance of the model.</p></li>
-<li><p><strong>R-squared:</strong> R-squared measures the proportion of the variance in the dependent variable that is predictable from the independent variables. It ranges from 0 to 1. A higher R-squared value indicates a better fit of the model to the data.</p></li>
-<li><p><strong>Mean Squared Error (MSE):</strong> MSE is the average of the squared differences between the predicted values and the actual values. Unlike RMSE, MSE does not take the square root, so its values are typically larger.</p></li>
-</ol>
-<p>Let’s take a look at the code snippet below to understand how we can evaluate the linear regression model:</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <span class="n">mean_absolute_error</span><span class="p">,</span> <span class="n">mean_squared_error</span><span class="p">,</span> <span class="n">r2_score</span>
-<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <span class="n">mean_squared_error</span><span class="p">,</span> <span class="n">mean_absolute_error</span><span class="p">,</span> <span class="n">r2_score</span>
+
+<span class="c1"># Make predictions on the test set</span>
+<span class="n">y_pred</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
 
-<span class="c1"># Evaluating the model</span>
-<span class="c1"># Mean Absolute Error (MAE)</span>
-<span class="n">mae</span> <span class="o">=</span> <span class="n">mean_absolute_error</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Mean Absolute Error:&#39;</span><span class="p">,</span> <span class="n">mae</span><span class="p">)</span>
+<span class="c1"># Compute the evaluation metrics</span>
+<span class="n">mse</span> <span class="o">=</span> <span class="n">mean_squared_error</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">)</span>
+<span class="n">mae</span> <span class="o">=</span> <span class="n">mean_absolute_error</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">)</span>
+<span class="n">r2</span> <span class="o">=</span> <span class="n">r2_score</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">)</span>
 
-<span class="c1"># Root Mean Squared Error (RMSE)</span>
-<span class="n">rmse</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">mean_squared_error</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">))</span>
-<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Root Mean Squared Error:&#39;</span><span class="p">,</span> <span class="n">rmse</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Mean Squared Error:&quot;</span><span class="p">,</span> <span class="n">mse</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Mean Absolute Error:&quot;</span><span class="p">,</span> <span class="n">mae</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;R-squared:&quot;</span><span class="p">,</span> <span class="n">r2</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Mean Squared Error: 0.6536995137169997
+Mean Absolute Error: 0.5913425779189757
+R-squared: 0.8072059636181399
+</pre></div>
+</div>
+</div>
+</div>
+<p>The scatter plot shows the relationship between the features (X) and the actual values (blue dots), along with the predicted values (red line). This visualization helps us understand how well the model captures the underlying patterns in the data.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
 
-<span class="c1"># R-squared</span>
-<span class="n">r2</span> <span class="o">=</span> <span class="n">r2_score</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;R-squared:&#39;</span><span class="p">,</span> <span class="n">r2</span><span class="p">)</span>
+<span class="c1"># Plot the scatter plot of the training set</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Training Set&#39;</span><span class="p">)</span>
 
-<span class="n">mse</span> <span class="o">=</span> <span class="n">mean_squared_error</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Mean Squared Error:&#39;</span><span class="p">,</span> <span class="n">mse</span><span class="p">)</span>
+<span class="c1"># Plot the scatter plot of the testing set</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Testing Set&#39;</span><span class="p">)</span>
 
-<span class="c1"># Print shapes of X and y</span>
-<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Shape of X:&#39;</span><span class="p">,</span> <span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Shape of y:&#39;</span><span class="p">,</span> <span class="n">y</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
+<span class="c1"># Plot the line representing the predicted results</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">X_test</span><span class="p">,</span> <span class="n">y_pred_test</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;red&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Predictions&#39;</span><span class="p">)</span>
 
-<span class="c1"># Plot the data</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">X</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">y</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;blue&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Actual&#39;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">X</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">y_pred</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;red&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Predicted&#39;</span><span class="p">)</span>
+<span class="c1"># Set the title and labels for the chart</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">&#39;Linear Regression&#39;</span><span class="p">)</span>
 <span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">&#39;X&#39;</span><span class="p">)</span>
 <span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">&#39;y&#39;</span><span class="p">)</span>
+
+<span class="c1"># Add a legend</span>
 <span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
+
+<span class="c1"># Display the chart</span>
 <span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
 </pre></div>
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Mean Absolute Error: 0.7010426719637757
-Root Mean Squared Error: 0.8981005311027566
-R-squared: 0.7692735413614223
-Mean Squared Error: 0.8065845639670534
-Shape of X: (100, 1)
-Shape of y: (100, 1)
-</pre></div>
-</div>
-<img alt="../../../_images/linear-regression-from-scratch_12_1.png" src="../../../_images/linear-regression-from-scratch_12_1.png" />
+<img alt="../../../_images/linear-regression-from-scratch_19_0.png" src="../../../_images/linear-regression-from-scratch_19_0.png" />
 </div>
 </div>
-<p>The scatter plot shows the relationship between the features (X) and the actual values (blue dots), along with the predicted values (red line). This visualization helps us understand how well the model captures the underlying patterns in the data.</p>
 </section>
 <section id="conclusion">
 <h2><span class="section-number">37.49.5. </span>Conclusion<a class="headerlink" href="#conclusion" title="Permalink to this headline">#</a></h2>
@@ -1827,10 +2910,6 @@ <h2><span class="section-number">37.49.5. </span>Conclusion<a class="headerlink"
 </ul>
 <p>In conclusion, implementing a linear regression model from scratch is a great way to gain a deeper understanding of how machine learning algorithms work. We hope that this assignment has helped you to better understand the concepts and techniques involved in building and training machine learning models.</p>
 </section>
-<section id="acknowledgments">
-<h2><span class="section-number">37.49.6. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
-<p>Thanks to the ChatGPT platform for providing the inspiration and guidance throughout this assignment.</p>
-</section>
 </section>
 
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@@ -1536,95 +1536,53 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
    37.47.2. Concept of loss function
   </a>
  </li>
- <li class="toc-h2 nav-item toc-entry">
-  <a class="reference internal nav-link" href="#difference-between-a-loss-function-and-a-cost-function">
-   37.47.3. Difference between a Loss Function and a Cost Function
-  </a>
- </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#classification-of-loss-functions">
-   37.47.4. Classification of loss functions
+   37.47.3. Classification of loss functions
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#regression-losses">
-     37.47.4.1. Regression Losses
+     37.47.3.1. Regression Losses
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#classification-losses">
-     37.47.4.2. Classification Losses
+     37.47.3.2. Classification Losses
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#structured-losses">
-     37.47.4.3. Structured Losses
+     37.47.3.3. Structured Losses
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#regularization-losses">
-     37.47.4.4. Regularization Losses
-    </a>
-   </li>
-  </ul>
- </li>
- <li class="toc-h2 nav-item toc-entry">
-  <a class="reference internal nav-link" href="#empirical-risk-and-structural-risk">
-   37.47.5. Empirical Risk and Structural Risk
-  </a>
-  <ul class="nav section-nav flex-column">
-   <li class="toc-h3 nav-item toc-entry">
-    <a class="reference internal nav-link" href="#definition">
-     37.47.5.1. Definition
-    </a>
-   </li>
-   <li class="toc-h3 nav-item toc-entry">
-    <a class="reference internal nav-link" href="#a-detaphor">
-     37.47.5.2. A Detaphor
-    </a>
-   </li>
-   <li class="toc-h3 nav-item toc-entry">
-    <a class="reference internal nav-link" href="#mathematical-explanation">
-     37.47.5.3. Mathematical Explanation
-    </a>
-   </li>
-   <li class="toc-h3 nav-item toc-entry">
-    <a class="reference internal nav-link" href="#cost-function-and-objective-function">
-     37.47.5.4. Cost Function and Objective Function
+     37.47.3.4. Regularization Losses
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#common-loss-functions">
-   37.47.6. Common Loss Functions
+   37.47.4. Common Loss Functions
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#regression-loss-functions">
-     37.47.6.1. Regression Loss Functions
+     37.47.4.1. Regression Loss Functions
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#classification-loss-functions">
-     37.47.6.2. Classification Loss Functions
-    </a>
-   </li>
-   <li class="toc-h3 nav-item toc-entry">
-    <a class="reference internal nav-link" href="#structured-loss-functions">
-     37.47.6.3. Structured Loss Functions
-    </a>
-   </li>
-   <li class="toc-h3 nav-item toc-entry">
-    <a class="reference internal nav-link" href="#regularization">
-     37.47.6.4. Regularization
+     37.47.4.2. Classification Loss Functions
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#conclusion">
-   37.47.7. Conclusion
+   37.47.5. Conclusion
   </a>
  </li>
 </ul>
@@ -1656,95 +1614,53 @@ <h2> Contents </h2>
    37.47.2. Concept of loss function
   </a>
  </li>
- <li class="toc-h2 nav-item toc-entry">
-  <a class="reference internal nav-link" href="#difference-between-a-loss-function-and-a-cost-function">
-   37.47.3. Difference between a Loss Function and a Cost Function
-  </a>
- </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#classification-of-loss-functions">
-   37.47.4. Classification of loss functions
+   37.47.3. Classification of loss functions
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#regression-losses">
-     37.47.4.1. Regression Losses
+     37.47.3.1. Regression Losses
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#classification-losses">
-     37.47.4.2. Classification Losses
+     37.47.3.2. Classification Losses
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#structured-losses">
-     37.47.4.3. Structured Losses
+     37.47.3.3. Structured Losses
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#regularization-losses">
-     37.47.4.4. Regularization Losses
-    </a>
-   </li>
-  </ul>
- </li>
- <li class="toc-h2 nav-item toc-entry">
-  <a class="reference internal nav-link" href="#empirical-risk-and-structural-risk">
-   37.47.5. Empirical Risk and Structural Risk
-  </a>
-  <ul class="nav section-nav flex-column">
-   <li class="toc-h3 nav-item toc-entry">
-    <a class="reference internal nav-link" href="#definition">
-     37.47.5.1. Definition
-    </a>
-   </li>
-   <li class="toc-h3 nav-item toc-entry">
-    <a class="reference internal nav-link" href="#a-detaphor">
-     37.47.5.2. A Detaphor
-    </a>
-   </li>
-   <li class="toc-h3 nav-item toc-entry">
-    <a class="reference internal nav-link" href="#mathematical-explanation">
-     37.47.5.3. Mathematical Explanation
-    </a>
-   </li>
-   <li class="toc-h3 nav-item toc-entry">
-    <a class="reference internal nav-link" href="#cost-function-and-objective-function">
-     37.47.5.4. Cost Function and Objective Function
+     37.47.3.4. Regularization Losses
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#common-loss-functions">
-   37.47.6. Common Loss Functions
+   37.47.4. Common Loss Functions
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#regression-loss-functions">
-     37.47.6.1. Regression Loss Functions
+     37.47.4.1. Regression Loss Functions
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#classification-loss-functions">
-     37.47.6.2. Classification Loss Functions
-    </a>
-   </li>
-   <li class="toc-h3 nav-item toc-entry">
-    <a class="reference internal nav-link" href="#structured-loss-functions">
-     37.47.6.3. Structured Loss Functions
-    </a>
-   </li>
-   <li class="toc-h3 nav-item toc-entry">
-    <a class="reference internal nav-link" href="#regularization">
-     37.47.6.4. Regularization
+     37.47.4.2. Classification Loss Functions
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#conclusion">
-   37.47.7. Conclusion
+   37.47.5. Conclusion
   </a>
  </li>
 </ul>
@@ -1773,18 +1689,10 @@ <h2><span class="section-number">37.47.2. </span>Concept of loss function<a clas
 — Deep Learning, Ian Goodfellow, Yoshua Bengio, Aaron Courville</p></li>
 </ul>
 </section>
-<section id="difference-between-a-loss-function-and-a-cost-function">
-<h2><span class="section-number">37.47.3. </span>Difference between a Loss Function and a Cost Function<a class="headerlink" href="#difference-between-a-loss-function-and-a-cost-function" title="Permalink to this headline">#</a></h2>
-<p>A loss function evaluates the error for a single training example, and it is occasionally referred to as an error function. In contrast, a cost function represents the <strong>average loss</strong> across the entire training dataset. Optimization strategies are designed to minimize this cost function.</p>
-<p>For a simple sample:</p>
-<p>The corresponding cost function of L1 Loss is the Mean of these Squared Errors (MSE).
-You can see the difference of <a class="reference external" href="#regression-loss-functions">Mathematical Expression</a></p>
-<p>However, these terms are frequently used interchangeably in practical settings, they aren’t precisely equivalent. From a definitional standpoint, the cost function represents an aggregation or average of the loss functions.</p>
-</section>
 <section id="classification-of-loss-functions">
-<h2><span class="section-number">37.47.4. </span>Classification of loss functions<a class="headerlink" href="#classification-of-loss-functions" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.47.3. </span>Classification of loss functions<a class="headerlink" href="#classification-of-loss-functions" title="Permalink to this headline">#</a></h2>
 <section id="regression-losses">
-<h3><span class="section-number">37.47.4.1. </span>Regression Losses<a class="headerlink" href="#regression-losses" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.47.3.1. </span>Regression Losses<a class="headerlink" href="#regression-losses" title="Permalink to this headline">#</a></h3>
 <p>These are employed when the objective is to predict a continuous outcome.</p>
 <ul class="simple">
 <li><p>Mean Squared Error (MSE): Measures the average squared discrepancies between predictions and actual values, emphasizing larger errors.</p></li>
@@ -1796,7 +1704,7 @@ <h3><span class="section-number">37.47.4.1. </span>Regression Losses<a class="he
 </ul>
 </section>
 <section id="classification-losses">
-<h3><span class="section-number">37.47.4.2. </span>Classification Losses<a class="headerlink" href="#classification-losses" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.47.3.2. </span>Classification Losses<a class="headerlink" href="#classification-losses" title="Permalink to this headline">#</a></h3>
 <p>Utilized for tasks requiring the prediction of discrete categories.</p>
 <ul class="simple">
 <li><p>Cross Entropy Loss: Quantifies the dissimilarity between the predicted probability distribution and the actual class distribution.</p></li>
@@ -1806,7 +1714,7 @@ <h3><span class="section-number">37.47.4.2. </span>Classification Losses<a class
 </ul>
 </section>
 <section id="structured-losses">
-<h3><span class="section-number">37.47.4.3. </span>Structured Losses<a class="headerlink" href="#structured-losses" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.47.3.3. </span>Structured Losses<a class="headerlink" href="#structured-losses" title="Permalink to this headline">#</a></h3>
 <p>Tailored for intricate tasks involving structured data patterns.</p>
 <ul class="simple">
 <li><p>Sequence Generation Loss: Emblematic examples include the CTC (Connectionist Temporal Classification) designed for undertakings such as speech and text identification.</p></li>
@@ -1814,7 +1722,7 @@ <h3><span class="section-number">37.47.4.3. </span>Structured Losses<a class="he
 </ul>
 </section>
 <section id="regularization-losses">
-<h3><span class="section-number">37.47.4.4. </span>Regularization Losses<a class="headerlink" href="#regularization-losses" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.47.3.4. </span>Regularization Losses<a class="headerlink" href="#regularization-losses" title="Permalink to this headline">#</a></h3>
 <p>Rather than directly influencing the model’s predictions, these losses are integrated into the objective function to counteract excessive model complexity.</p>
 <ul class="simple">
 <li><p>L1 Regularization (Lasso): Enforces sparsity by compelling certain model coefficients to be exactly zero.</p></li>
@@ -1822,75 +1730,10 @@ <h3><span class="section-number">37.47.4.4. </span>Regularization Losses<a class
 </ul>
 </section>
 </section>
-<section id="empirical-risk-and-structural-risk">
-<h2><span class="section-number">37.47.5. </span>Empirical Risk and Structural Risk<a class="headerlink" href="#empirical-risk-and-structural-risk" title="Permalink to this headline">#</a></h2>
-<section id="definition">
-<h3><span class="section-number">37.47.5.1. </span>Definition<a class="headerlink" href="#definition" title="Permalink to this headline">#</a></h3>
-<p>Perhaps you’ve heard of these two concepts before. In the realms of machine learning and statistics, the concepts of empirical risk and structural risk are intricately tied to loss functions. <strong>However, these terms aren’t directly categories of loss functions perse.</strong> Let’s first clarify these concepts:</p>
-<ol class="simple">
-<li><p><strong>Empirical Risk:</strong> Refers to the average loss of a model over a given dataset. Minimizing empirical risk focuses on reducing errors explicitly on the training data.</p></li>
-<li><p><strong>Structural Risk:</strong> Introduces a regularization term in addition to empirical risk, aiming to prevent overfitting. Minimizing structural risk strikes a balance between the empirical risk and the complexity of the model.</p></li>
-</ol>
-<p>Given these definitions:</p>
-<ul class="simple">
-<li><p><strong>Empirical Risk:</strong> Loss functions directly related to dataset performance fall under this category. From the ones been listed, regression losses (e.g., MSE, MAE, Huber Loss, L1 Loss, L2 Loss, Smooth L1 Loss), classification losses (e.g., Cross Entropy Loss, Hinge Loss, Log Loss), and structured losses (e.g., CTC or Image Segmentation Loss) can be seen as manifestations of empirical risk.</p></li>
-<li><p><strong>Structural Risk:</strong> Regularization losses, like L1 and L2 regularization, form part of structural risk. They don’t measure the model’s performance on the data directly but rather serve to rein in model complexity.</p></li>
-</ul>
-</section>
-<section id="a-detaphor">
-<h3><span class="section-number">37.47.5.2. </span>A Detaphor<a class="headerlink" href="#a-detaphor" title="Permalink to this headline">#</a></h3>
-<p>Maybe it’s still abtract. So, now imagine you’re a tailor trying to make a dress for a client.</p>
-<ul class="simple">
-<li><p><strong>Empirical Risk:</strong> This is like ensuring the dress fits the client perfectly based on a single fitting session. You measure every contour and make the dress to match those exact measurements. The dress is a perfect fit for the client on that particular day.</p></li>
-</ul>
-<p>However, what if the client gains or loses a little weight or wants to move more comfortably? A dress tailored too tightly to the exact measurements might not be very adaptable or comfortable in various situations.</p>
-<ul class="simple">
-<li><p><strong>Structural Risk:</strong> Now, consider that you decide to allow a bit more flexibility in the dress. You make it slightly adjustable, perhaps with some elastic portions. This way, even if the client’s measurements change a bit, the dress will still fit comfortably. You’re sacrificing a tiny bit of the “perfect” fit for the adaptability and general comfort.</p></li>
-</ul>
-<p>In the context of machine learning:</p>
-<p>Relying solely on <strong>Empirical Risk</strong> would be like fitting the dress exactly to the client’s measurements, risking overfitting. If the data changes slightly, the model might perform poorly.</p>
-<p>Factoring in <strong>Structural Risk</strong> ensures the model isn’t overly tailored to the training data and can generalize well to new, unseen data. It’s about ensuring a balance between a perfect fit and adaptability.</p>
-</section>
-<section id="mathematical-explanation">
-<h3><span class="section-number">37.47.5.3. </span>Mathematical Explanation<a class="headerlink" href="#mathematical-explanation" title="Permalink to this headline">#</a></h3>
-<p>Now you have a general understanding of the meaning of empirical risk and structural risk. Let’s delve into a more mathematical perspective:</p>
-<p>Given a dataset <span class="math notranslate nohighlight">\(\mathcal{D}\)</span> comprising input-output pairs <span class="math notranslate nohighlight">\((x_1, y_1)\)</span>, <span class="math notranslate nohighlight">\((x_2, y_2)\)</span>, … <span class="math notranslate nohighlight">\((x_n, y_n)\)</span> and a model <span class="math notranslate nohighlight">\(f\)</span> parameterized by <span class="math notranslate nohighlight">\(\theta\)</span>, the empirical risk and structural risk can be formally defined as follows:</p>
-<p><strong>Empirical Risk(Cost Function):</strong>
-$<span class="math notranslate nohighlight">\(
-R_{emp}(f) = \frac{1}{n} \sum_{i=1}^{n} L(y_i, f(x_i; \theta))
-\)</span>$
-Where:</p>
-<ul class="simple">
-<li><p><strong><span class="math notranslate nohighlight">\(L\)</span> is the loss function</strong>, measuring the discrepancy between the predicted value <span class="math notranslate nohighlight">\(f(x_i; \theta)\)</span> and the actual output <span class="math notranslate nohighlight">\(y_i\)</span>.</p></li>
-</ul>
-<p>Empirical risk quantifies how well the model fits the given dataset, representing the average loss of the model on the training data.</p>
-<p><strong>Structural Risk(Objective Function):</strong>
-$<span class="math notranslate nohighlight">\(
-R_{struc}(f) = R_{emp}(f) + \lambda R_{reg}(\theta)
-\)</span>$
-Where:</p>
-<ul class="simple">
-<li><p><span class="math notranslate nohighlight">\(R_{reg}(\theta)\)</span> is the regularization term, penalizing the complexity of the model.</p></li>
-<li><p><span class="math notranslate nohighlight">\(\lambda\)</span> is a regularization coefficient determining the weight of the regularization term relative to the empirical risk.</p></li>
-</ul>
-<p>Structural risk is a combination of the empirical risk and a penalty for model complexity. It strikes a balance between fitting the training data (empirical risk) and ensuring the model isn’t overly complex (which can lead to overfitting).</p>
-<p><strong>Differences and Relations:</strong></p>
-<ol class="simple">
-<li><p><strong>Empirical Risk</strong> focuses solely on minimizing the error on the training data without considering model complexity or how it generalizes to unseen data.</p></li>
-<li><p><strong>Structural Risk</strong> takes into account both the empirical risk and the complexity of the model. By introducing a regularization term, it ensures that the model doesn’t become overly complex and overfit the training data. Thus, it balances performance on training data with generalization to new data.</p></li>
-</ol>
-<p>In essence, while empirical risk aims for performance on the current dataset, structural risk aims for good performance on new data by penalizing overly complex models.</p>
-</section>
-<section id="cost-function-and-objective-function">
-<h3><span class="section-number">37.47.5.4. </span>Cost Function and Objective Function<a class="headerlink" href="#cost-function-and-objective-function" title="Permalink to this headline">#</a></h3>
-<p>The empirical risk and cost functions are in many cases the same and represent the average loss on the training data.</p>
-<p>Structural risk is often viewed as an objective function, especially when regularization is considered. But the term “objective function” is broader and is not limited to structural risk but can also include other optimization objectives.</p>
-</section>
-</section>
 <section id="common-loss-functions">
-<h2><span class="section-number">37.47.6. </span>Common Loss Functions<a class="headerlink" href="#common-loss-functions" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.47.4. </span>Common Loss Functions<a class="headerlink" href="#common-loss-functions" title="Permalink to this headline">#</a></h2>
 <section id="regression-loss-functions">
-<h3><span class="section-number">37.47.6.1. </span>Regression Loss Functions<a class="headerlink" href="#regression-loss-functions" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.47.4.1. </span>Regression Loss Functions<a class="headerlink" href="#regression-loss-functions" title="Permalink to this headline">#</a></h3>
 <ol class="simple">
 <li><p><strong>Mean Squared Error, MSE</strong></p></li>
 </ol>
@@ -1966,26 +1809,7 @@ <h3><span class="section-number">37.47.6.1. </span>Regression Loss Functions<a c
 <p>Corresponds to MSE.</p>
 </section>
 <section id="classification-loss-functions">
-<h3><span class="section-number">37.47.6.2. </span>Classification Loss Functions<a class="headerlink" href="#classification-loss-functions" title="Permalink to this headline">#</a></h3>
-<ol class="simple">
-<li><p><strong>Cross Entropy Loss</strong></p></li>
-</ol>
-<div class="math notranslate nohighlight">
-\[
-L(y, p) = - \sum_{i=1}^{C} y_i \log(p_i)
-\]</div>
-<p>Where <span class="math notranslate nohighlight">\(y_i\)</span> is the actual label (0 or 1) and <span class="math notranslate nohighlight">\(p_i\)</span> is the predicted probability for the respective class.</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">y_true</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">constant</span><span class="p">([[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">]])</span>
-<span class="n">y_pred</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">constant</span><span class="p">([[</span><span class="mf">0.05</span><span class="p">,</span> <span class="mf">0.95</span><span class="p">],</span> <span class="p">[</span><span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.9</span><span class="p">],</span> <span class="p">[</span><span class="mf">0.8</span><span class="p">,</span> <span class="mf">0.2</span><span class="p">]])</span>
-<span class="n">loss</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">losses</span><span class="o">.</span><span class="n">CategoricalCrossentropy</span><span class="p">()(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">)</span>
-
-<span class="nb">print</span><span class="p">(</span><span class="n">loss</span><span class="o">.</span><span class="n">numpy</span><span class="p">())</span>
-</pre></div>
-</div>
-</div>
-</div>
+<h3><span class="section-number">37.47.4.2. </span>Classification Loss Functions<a class="headerlink" href="#classification-loss-functions" title="Permalink to this headline">#</a></h3>
 <ol class="simple">
 <li><p><strong>Hinge Loss</strong></p></li>
 </ol>
@@ -2005,162 +1829,10 @@ <h3><span class="section-number">37.47.6.2. </span>Classification Loss Functions
 </div>
 </div>
 </div>
-<ol class="simple">
-<li><p><strong>Binary Cross Entropy(Log Loss)</strong></p></li>
-</ol>
-<p>Mathematically, it is the preferred loss function under the inference framework of maximum likelihood. It is the loss function to be evaluated first and only changed if you have a good reason.</p>
-<p>Cross-entropy will calculate a score that summarizes the average difference between the actual and predicted probability distributions for predicting class 1. The score is minimized and a perfect cross-entropy value is 0.</p>
-<p>This YouTube video by Andrew Ng explains very well Binary Cross Entropy Loss (make sure that you have access to YouTube for this web page to render correctly):</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">HTML</span>
-
-<span class="n">display</span><span class="p">(</span><span class="n">HTML</span><span class="p">(</span>
-<span class="w">  </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    &lt;iframe src=&quot;https://www.youtube.com/embed/SHEPb1JHw5o&quot; allowfullscreen&gt;&lt;/iframe&gt;</span>
-<span class="sd">  &quot;&quot;&quot;</span>
-<span class="p">))</span>
-</pre></div>
-</div>
-</div>
-</div>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">y_true</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">constant</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">])</span>
-<span class="n">y_pred</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">constant</span><span class="p">([</span><span class="mf">0.05</span><span class="p">,</span> <span class="mf">0.95</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">])</span>
-<span class="n">loss</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">losses</span><span class="o">.</span><span class="n">BinaryCrossentropy</span><span class="p">()(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">)</span>
-
-<span class="nb">print</span><span class="p">(</span><span class="n">loss</span><span class="o">.</span><span class="n">numpy</span><span class="p">())</span>
-</pre></div>
-</div>
-</div>
-</div>
-<ol class="simple">
-<li><p><strong>Multi-Class Cross-Entropy Loss</strong></p></li>
-</ol>
-<p>Mathematically, it is the preferred loss function under the inference framework of maximum likelihood. It is the loss function to be evaluated first and only changed if you have a good reason.</p>
-<p>Cross-entropy will calculate a score that summarizes the average difference between the actual and predicted probability distributions for all classes in the problem. The score is minimized and a perfect cross-entropy value is 0.</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">y_true</span> <span class="o">=</span> <span class="p">[[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span>
-          <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span>
-          <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span>
-          <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span>
-          <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">]]</span>
-
-<span class="c1"># Mock predicted probabilities from a model</span>
-<span class="n">y_pred</span> <span class="o">=</span> <span class="p">[[</span><span class="mf">0.7</span><span class="p">,</span> <span class="mf">0.2</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">],</span>
-          <span class="p">[</span><span class="mf">0.2</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">,</span> <span class="mf">0.3</span><span class="p">],</span>
-          <span class="p">[</span><span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.2</span><span class="p">,</span> <span class="mf">0.7</span><span class="p">],</span>
-          <span class="p">[</span><span class="mf">0.6</span><span class="p">,</span> <span class="mf">0.3</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">],</span>
-          <span class="p">[</span><span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.6</span><span class="p">,</span> <span class="mf">0.3</span><span class="p">]]</span>
-
-<span class="n">y_true</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">constant</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">tf</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
-<span class="n">y_pred</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">constant</span><span class="p">(</span><span class="n">y_pred</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">tf</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
-
-<span class="n">loss</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">reduce_mean</span><span class="p">(</span><span class="o">-</span><span class="n">tf</span><span class="o">.</span><span class="n">reduce_sum</span><span class="p">(</span><span class="n">y_true</span> <span class="o">*</span> <span class="n">tf</span><span class="o">.</span><span class="n">math</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">y_pred</span><span class="p">),</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">))</span>
-
-<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Multi-Class Cross-Entropy Loss:&quot;</span><span class="p">,</span> <span class="n">loss</span><span class="o">.</span><span class="n">numpy</span><span class="p">())</span>
-</pre></div>
-</div>
-</div>
-</div>
-</section>
-<section id="structured-loss-functions">
-<h3><span class="section-number">37.47.6.3. </span>Structured Loss Functions<a class="headerlink" href="#structured-loss-functions" title="Permalink to this headline">#</a></h3>
-<ol class="simple">
-<li><p><strong>CTC Loss (Connectionist Temporal Classification)</strong></p></li>
-</ol>
-<p>Used for sequence-to-sequence problems, like speech recognition.</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-
-<span class="n">y_true</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">]])</span>  <span class="c1"># (batch, timesteps)</span>
-<span class="n">y_pred</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[[</span><span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.6</span><span class="p">,</span> <span class="mf">0.3</span><span class="p">],</span> <span class="p">[</span><span class="mf">0.3</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.6</span><span class="p">]]])</span>  <span class="c1"># (batch, timesteps, num_classes)</span>
-<span class="n">logit_length</span> <span class="o">=</span> <span class="p">[</span><span class="mi">2</span><span class="p">]</span>
-<span class="n">label_length</span> <span class="o">=</span> <span class="p">[</span><span class="mi">2</span><span class="p">]</span>
-<span class="n">loss</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">backend</span><span class="o">.</span><span class="n">ctc_batch_cost</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">,</span> <span class="n">logit_length</span><span class="p">,</span> <span class="n">label_length</span><span class="p">)</span>
-
-<span class="nb">print</span><span class="p">(</span><span class="n">loss</span><span class="o">.</span><span class="n">numpy</span><span class="p">())</span>
-</pre></div>
-</div>
-</div>
-</div>
-<ol class="simple">
-<li><p><strong>Dice Loss, IoU Loss</strong></p></li>
-</ol>
-<p>Used for image segmentation tasks.</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">dice_loss</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">):</span>
-    <span class="n">numerator</span> <span class="o">=</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">tf</span><span class="o">.</span><span class="n">reduce_sum</span><span class="p">(</span><span class="n">y_true</span> <span class="o">*</span> <span class="n">y_pred</span><span class="p">,</span> <span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
-    <span class="n">denominator</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">reduce_sum</span><span class="p">(</span><span class="n">y_true</span> <span class="o">+</span> <span class="n">y_pred</span><span class="p">,</span> <span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
-    <span class="k">return</span> <span class="mi">1</span> <span class="o">-</span> <span class="p">(</span><span class="n">numerator</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">/</span> <span class="p">(</span><span class="n">denominator</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span>
-
-<span class="n">y_true</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">constant</span><span class="p">([[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">]])</span>
-<span class="n">y_pred</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">constant</span><span class="p">([[</span><span class="mf">0.8</span><span class="p">,</span> <span class="mf">0.2</span><span class="p">,</span> <span class="mf">0.6</span><span class="p">],</span> <span class="p">[</span><span class="mf">0.3</span><span class="p">,</span> <span class="mf">0.7</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">]])</span>
-<span class="n">loss</span> <span class="o">=</span> <span class="n">dice_loss</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">)</span>
-
-<span class="nb">print</span><span class="p">(</span><span class="n">loss</span><span class="o">.</span><span class="n">numpy</span><span class="p">())</span>
-</pre></div>
-</div>
-</div>
-</div>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">iou_loss</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">):</span>
-    <span class="n">intersection</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">reduce_sum</span><span class="p">(</span><span class="n">y_true</span> <span class="o">*</span> <span class="n">y_pred</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">])</span>
-    <span class="n">union</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">reduce_sum</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">])</span> <span class="o">+</span> <span class="n">tf</span><span class="o">.</span><span class="n">reduce_sum</span><span class="p">(</span><span class="n">y_pred</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">])</span> <span class="o">-</span> <span class="n">intersection</span>
-    <span class="k">return</span> <span class="mf">1.</span> <span class="o">-</span> <span class="p">(</span><span class="n">intersection</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">/</span> <span class="p">(</span><span class="n">union</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span>
-
-<span class="c1"># For simplicity, using 2D tensors. Typically, these are images (3D tensors).</span>
-<span class="n">y_true</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">constant</span><span class="p">([[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">]])</span>
-<span class="n">y_pred</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">constant</span><span class="p">([[</span><span class="mf">0.8</span><span class="p">,</span> <span class="mf">0.2</span><span class="p">,</span> <span class="mf">0.6</span><span class="p">],</span> <span class="p">[</span><span class="mf">0.3</span><span class="p">,</span> <span class="mf">0.7</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">]])</span>
-<span class="n">loss</span> <span class="o">=</span> <span class="n">iou_loss</span><span class="p">(</span><span class="n">y_true</span><span class="p">[</span><span class="n">tf</span><span class="o">.</span><span class="n">newaxis</span><span class="p">,</span> <span class="o">...</span><span class="p">],</span> <span class="n">y_pred</span><span class="p">[</span><span class="n">tf</span><span class="o">.</span><span class="n">newaxis</span><span class="p">,</span> <span class="o">...</span><span class="p">])</span>  <span class="c1"># Add batch dimension</span>
-
-<span class="nb">print</span><span class="p">(</span><span class="n">loss</span><span class="o">.</span><span class="n">numpy</span><span class="p">())</span>
-</pre></div>
-</div>
-</div>
-</div>
-</section>
-<section id="regularization">
-<h3><span class="section-number">37.47.6.4. </span>Regularization<a class="headerlink" href="#regularization" title="Permalink to this headline">#</a></h3>
-<ol class="simple">
-<li><p><strong>L1 Regularization (Lasso)</strong></p></li>
-</ol>
-<p>Produces sparse model parameters.</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">keras.regularizers</span> <span class="kn">import</span> <span class="n">l1</span>
-
-<span class="n">model</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">models</span><span class="o">.</span><span class="n">Sequential</span><span class="p">([</span>
-    <span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="mi">64</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s1">&#39;relu&#39;</span><span class="p">,</span> <span class="n">kernel_regularizer</span><span class="o">=</span><span class="n">l1</span><span class="p">(</span><span class="mf">0.01</span><span class="p">),</span> <span class="n">input_shape</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,))</span>
-<span class="p">])</span>
-</pre></div>
-</div>
-</div>
-</div>
-<ol class="simple">
-<li><p><strong>L2 Regularization (Ridge)</strong></p></li>
-</ol>
-<p>Prevents model parameters from becoming too large but doesn’t force them to become exactly zero.</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">keras.regularizers</span> <span class="kn">import</span> <span class="n">l2</span>
-
-<span class="n">model</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">models</span><span class="o">.</span><span class="n">Sequential</span><span class="p">([</span>
-    <span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="mi">64</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s1">&#39;relu&#39;</span><span class="p">,</span> <span class="n">kernel_regularizer</span><span class="o">=</span><span class="n">l2</span><span class="p">(</span><span class="mf">0.01</span><span class="p">),</span> <span class="n">input_shape</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,))</span>
-<span class="p">])</span>
-</pre></div>
-</div>
-</div>
-</div>
 </section>
 </section>
 <section id="conclusion">
-<h2><span class="section-number">37.47.7. </span>Conclusion<a class="headerlink" href="#conclusion" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.47.5. </span>Conclusion<a class="headerlink" href="#conclusion" title="Permalink to this headline">#</a></h2>
 <p>Loss functions hold a pivotal role in machine learning. By minimizing the loss, we enhance the accuracy of our model’s predictions. A deep understanding of various loss functions aids in selecting the most appropriate optimization technique for specific challenges.</p>
 </section>
 </section>
diff --git a/assignments/ml-fundamentals/managing-data.html b/assignments/ml-fundamentals/managing-data.html
index 01246324c6..295a5595d2 100644
--- a/assignments/ml-fundamentals/managing-data.html
+++ b/assignments/ml-fundamentals/managing-data.html
@@ -27,8 +27,8 @@
     <link rel="stylesheet" type="text/css" href="../../_static/mystnb.css" />
     <link rel="stylesheet" type="text/css" href="../../_static/sphinx-thebe.css" />
     <link rel="stylesheet" type="text/css" href="../../_static/drawio.css" />
-    <link rel="stylesheet" type="text/css" href="../../_static/style.css" />
     <link rel="stylesheet" type="text/css" href="../../_static/youtube.css" />
+    <link rel="stylesheet" type="text/css" href="../../_static/style.css" />
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diff --git a/assignments/ml-fundamentals/ml-logistic-regression-1.html b/assignments/ml-fundamentals/ml-logistic-regression-1.html
index 1c99bdd38d..5cc702057c 100644
--- a/assignments/ml-fundamentals/ml-logistic-regression-1.html
+++ b/assignments/ml-fundamentals/ml-logistic-regression-1.html
@@ -27,8 +27,8 @@
     <link rel="stylesheet" type="text/css" href="../../_static/mystnb.css" />
     <link rel="stylesheet" type="text/css" href="../../_static/sphinx-thebe.css" />
     <link rel="stylesheet" type="text/css" href="../../_static/drawio.css" />
-    <link rel="stylesheet" type="text/css" href="../../_static/style.css" />
     <link rel="stylesheet" type="text/css" href="../../_static/youtube.css" />
+    <link rel="stylesheet" type="text/css" href="../../_static/style.css" />
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diff --git a/assignments/ml-fundamentals/ml-logistic-regression-2.html b/assignments/ml-fundamentals/ml-logistic-regression-2.html
index 756e3960b0..d855d7f039 100644
--- a/assignments/ml-fundamentals/ml-logistic-regression-2.html
+++ b/assignments/ml-fundamentals/ml-logistic-regression-2.html
@@ -27,8 +27,8 @@
     <link rel="stylesheet" type="text/css" href="../../_static/mystnb.css" />
     <link rel="stylesheet" type="text/css" href="../../_static/sphinx-thebe.css" />
     <link rel="stylesheet" type="text/css" href="../../_static/drawio.css" />
-    <link rel="stylesheet" type="text/css" href="../../_static/style.css" />
     <link rel="stylesheet" type="text/css" href="../../_static/youtube.css" />
+    <link rel="stylesheet" type="text/css" href="../../_static/style.css" />
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diff --git a/assignments/ml-fundamentals/ml-neural-network-1.html b/assignments/ml-fundamentals/ml-neural-network-1.html
index f8c32ed7d0..1da4bfeb1f 100644
--- a/assignments/ml-fundamentals/ml-neural-network-1.html
+++ b/assignments/ml-fundamentals/ml-neural-network-1.html
@@ -27,8 +27,8 @@
     <link rel="stylesheet" type="text/css" href="../../_static/mystnb.css" />
     <link rel="stylesheet" type="text/css" href="../../_static/sphinx-thebe.css" />
     <link rel="stylesheet" type="text/css" href="../../_static/drawio.css" />
-    <link rel="stylesheet" type="text/css" href="../../_static/style.css" />
     <link rel="stylesheet" type="text/css" href="../../_static/youtube.css" />
+    <link rel="stylesheet" type="text/css" href="../../_static/style.css" />
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diff --git a/assignments/ml-fundamentals/ml-overview-iris.html b/assignments/ml-fundamentals/ml-overview-iris.html
index a5f6ca1f2c..413ead0a8e 100644
--- a/assignments/ml-fundamentals/ml-overview-iris.html
+++ b/assignments/ml-fundamentals/ml-overview-iris.html
@@ -27,8 +27,8 @@
     <link rel="stylesheet" type="text/css" href="../../_static/mystnb.css" />
     <link rel="stylesheet" type="text/css" href="../../_static/sphinx-thebe.css" />
     <link rel="stylesheet" type="text/css" href="../../_static/drawio.css" />
-    <link rel="stylesheet" type="text/css" href="../../_static/style.css" />
     <link rel="stylesheet" type="text/css" href="../../_static/youtube.css" />
+    <link rel="stylesheet" type="text/css" href="../../_static/style.css" />
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diff --git a/assignments/ml-fundamentals/ml-overview-mnist-digits.html b/assignments/ml-fundamentals/ml-overview-mnist-digits.html
index 9ff7216a17..5097b25c79 100644
--- a/assignments/ml-fundamentals/ml-overview-mnist-digits.html
+++ b/assignments/ml-fundamentals/ml-overview-mnist-digits.html
@@ -27,8 +27,8 @@
     <link rel="stylesheet" type="text/css" href="../../_static/mystnb.css" />
     <link rel="stylesheet" type="text/css" href="../../_static/sphinx-thebe.css" />
     <link rel="stylesheet" type="text/css" href="../../_static/drawio.css" />
-    <link rel="stylesheet" type="text/css" href="../../_static/style.css" />
     <link rel="stylesheet" type="text/css" href="../../_static/youtube.css" />
+    <link rel="stylesheet" type="text/css" href="../../_static/style.css" />
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+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/tmp/ipykernel_6031/49960699.py:1: UserWarning: KDE cannot be estimated (0 variance or perfect covariance). Pass `warn_singular=False` to disable this warning.
   sns.kdeplot(data=filteredBirds, x=&quot;MinLength&quot;, y=&quot;MaxLength&quot;, hue=&quot;ConservationStatus&quot;)
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index 6469a6bf8f..9be3a79297 100644
--- a/deep-learning/autoencoder.html
+++ b/deep-learning/autoencoder.html
@@ -27,8 +27,8 @@
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     <link rel="stylesheet" type="text/css" href="../_static/sphinx-thebe.css" />
     <link rel="stylesheet" type="text/css" href="../_static/drawio.css" />
-    <link rel="stylesheet" type="text/css" href="../_static/style.css" />
     <link rel="stylesheet" type="text/css" href="../_static/youtube.css" />
+    <link rel="stylesheet" type="text/css" href="../_static/style.css" />
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diff --git a/deep-learning/cnn.html b/deep-learning/cnn.html
index 9ec0e177e8..273c11d6b9 100644
--- a/deep-learning/cnn.html
+++ b/deep-learning/cnn.html
@@ -27,8 +27,8 @@
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     <link rel="stylesheet" type="text/css" href="../_static/sphinx-thebe.css" />
     <link rel="stylesheet" type="text/css" href="../_static/drawio.css" />
-    <link rel="stylesheet" type="text/css" href="../_static/style.css" />
     <link rel="stylesheet" type="text/css" href="../_static/youtube.css" />
+    <link rel="stylesheet" type="text/css" href="../_static/style.css" />
     <link rel="stylesheet" type="text/css" href="../_static/design-style.b7bb847fb20b106c3d81b95245e65545.min.css" />
     
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index 3c19ffa8e6..a4136d9171 100644
--- a/deep-learning/difussion-model.html
+++ b/deep-learning/difussion-model.html
@@ -27,8 +27,8 @@
     <link rel="stylesheet" type="text/css" href="../_static/mystnb.css" />
     <link rel="stylesheet" type="text/css" href="../_static/sphinx-thebe.css" />
     <link rel="stylesheet" type="text/css" href="../_static/drawio.css" />
-    <link rel="stylesheet" type="text/css" href="../_static/style.css" />
     <link rel="stylesheet" type="text/css" href="../_static/youtube.css" />
+    <link rel="stylesheet" type="text/css" href="../_static/style.css" />
     <link rel="stylesheet" type="text/css" href="../_static/design-style.b7bb847fb20b106c3d81b95245e65545.min.css" />
     
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diff --git a/deep-learning/dl-overview.html b/deep-learning/dl-overview.html
index 0edf62a17f..870a6a4d61 100644
--- a/deep-learning/dl-overview.html
+++ b/deep-learning/dl-overview.html
@@ -27,8 +27,8 @@
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     <link rel="stylesheet" type="text/css" href="../_static/sphinx-thebe.css" />
     <link rel="stylesheet" type="text/css" href="../_static/drawio.css" />
-    <link rel="stylesheet" type="text/css" href="../_static/style.css" />
     <link rel="stylesheet" type="text/css" href="../_static/youtube.css" />
+    <link rel="stylesheet" type="text/css" href="../_static/style.css" />
     <link rel="stylesheet" type="text/css" href="../_static/design-style.b7bb847fb20b106c3d81b95245e65545.min.css" />
     
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diff --git a/deep-learning/dqn.html b/deep-learning/dqn.html
index 715ad3a407..30513a75dd 100644
--- a/deep-learning/dqn.html
+++ b/deep-learning/dqn.html
@@ -27,8 +27,8 @@
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     <link rel="stylesheet" type="text/css" href="../_static/sphinx-thebe.css" />
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-    <link rel="stylesheet" type="text/css" href="../_static/style.css" />
     <link rel="stylesheet" type="text/css" href="../_static/youtube.css" />
+    <link rel="stylesheet" type="text/css" href="../_static/style.css" />
     <link rel="stylesheet" type="text/css" href="../_static/design-style.b7bb847fb20b106c3d81b95245e65545.min.css" />
     
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diff --git a/deep-learning/gan.html b/deep-learning/gan.html
index e9aa2b4aef..63dcdc6b6b 100644
--- a/deep-learning/gan.html
+++ b/deep-learning/gan.html
@@ -27,8 +27,8 @@
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     <link rel="stylesheet" type="text/css" href="../_static/sphinx-thebe.css" />
     <link rel="stylesheet" type="text/css" href="../_static/drawio.css" />
-    <link rel="stylesheet" type="text/css" href="../_static/style.css" />
     <link rel="stylesheet" type="text/css" href="../_static/youtube.css" />
+    <link rel="stylesheet" type="text/css" href="../_static/style.css" />
     <link rel="stylesheet" type="text/css" href="../_static/design-style.b7bb847fb20b106c3d81b95245e65545.min.css" />
     
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diff --git a/deep-learning/image-classification.html b/deep-learning/image-classification.html
index a5b5735c11..aac361c9eb 100644
--- a/deep-learning/image-classification.html
+++ b/deep-learning/image-classification.html
@@ -27,8 +27,8 @@
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     <link rel="stylesheet" type="text/css" href="../_static/sphinx-thebe.css" />
     <link rel="stylesheet" type="text/css" href="../_static/drawio.css" />
-    <link rel="stylesheet" type="text/css" href="../_static/style.css" />
     <link rel="stylesheet" type="text/css" href="../_static/youtube.css" />
+    <link rel="stylesheet" type="text/css" href="../_static/style.css" />
     <link rel="stylesheet" type="text/css" href="../_static/design-style.b7bb847fb20b106c3d81b95245e65545.min.css" />
     
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diff --git a/deep-learning/image-segmentation.html b/deep-learning/image-segmentation.html
index 3a0d6d0818..5a3bd49e9e 100644
--- a/deep-learning/image-segmentation.html
+++ b/deep-learning/image-segmentation.html
@@ -27,8 +27,8 @@
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-    <link rel="stylesheet" type="text/css" href="../_static/style.css" />
     <link rel="stylesheet" type="text/css" href="../_static/youtube.css" />
+    <link rel="stylesheet" type="text/css" href="../_static/style.css" />
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diff --git a/deep-learning/lstm.html b/deep-learning/lstm.html
index a91895b6e5..ae7e74b6bc 100644
--- a/deep-learning/lstm.html
+++ b/deep-learning/lstm.html
@@ -27,8 +27,8 @@
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-    <link rel="stylesheet" type="text/css" href="../_static/style.css" />
     <link rel="stylesheet" type="text/css" href="../_static/youtube.css" />
+    <link rel="stylesheet" type="text/css" href="../_static/style.css" />
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diff --git a/deep-learning/object-detection.html b/deep-learning/object-detection.html
index f16dfc9c3a..1d7b5ed742 100644
--- a/deep-learning/object-detection.html
+++ b/deep-learning/object-detection.html
@@ -27,8 +27,8 @@
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-    <link rel="stylesheet" type="text/css" href="../_static/style.css" />
     <link rel="stylesheet" type="text/css" href="../_static/youtube.css" />
+    <link rel="stylesheet" type="text/css" href="../_static/style.css" />
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diff --git a/deep-learning/rnn.html b/deep-learning/rnn.html
index aa16a18e48..a2c6c80009 100644
--- a/deep-learning/rnn.html
+++ b/deep-learning/rnn.html
@@ -27,8 +27,8 @@
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     <link rel="stylesheet" type="text/css" href="../_static/sphinx-thebe.css" />
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-    <link rel="stylesheet" type="text/css" href="../_static/style.css" />
     <link rel="stylesheet" type="text/css" href="../_static/youtube.css" />
+    <link rel="stylesheet" type="text/css" href="../_static/style.css" />
     <link rel="stylesheet" type="text/css" href="../_static/design-style.b7bb847fb20b106c3d81b95245e65545.min.css" />
     
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diff --git a/deep-learning/time-series.html b/deep-learning/time-series.html
index 286485055c..bd48932485 100644
--- a/deep-learning/time-series.html
+++ b/deep-learning/time-series.html
@@ -27,8 +27,8 @@
     <link rel="stylesheet" type="text/css" href="../_static/mystnb.css" />
     <link rel="stylesheet" type="text/css" href="../_static/sphinx-thebe.css" />
     <link rel="stylesheet" type="text/css" href="../_static/drawio.css" />
-    <link rel="stylesheet" type="text/css" href="../_static/style.css" />
     <link rel="stylesheet" type="text/css" href="../_static/youtube.css" />
+    <link rel="stylesheet" type="text/css" href="../_static/style.css" />
     <link rel="stylesheet" type="text/css" href="../_static/design-style.b7bb847fb20b106c3d81b95245e65545.min.css" />
     
   <!-- Pre-loaded scripts that we'll load fully later -->
@@ -1326,7 +1326,7 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <div class="menu-dropdown__content">
     <ul>
       <li>
-        <a href="https://mybinder.org/v2/gh/ocademy-ai/machine-learning/release?urlpath=lab/tree/open-machine-learning-jupyter-book/deep-learning/time-series.md"
+        <a href="https://mybinder.org/v2/gh/ocademy-ai/machine-learning/release?urlpath=lab/tree/open-machine-learning-jupyter-book/deep-learning/time-series.ipynb"
    class="headerbtn"
    data-toggle="tooltip"
 data-placement="left"
@@ -1344,7 +1344,7 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
       </li>
       
       <li>
-        <a href="https://colab.research.google.com/github/ocademy-ai/machine-learning/blob/release/open-machine-learning-jupyter-book/deep-learning/time-series.md"
+        <a href="https://colab.research.google.com/github/ocademy-ai/machine-learning/blob/release/open-machine-learning-jupyter-book/deep-learning/time-series.ipynb"
    class="headerbtn"
    data-toggle="tooltip"
 data-placement="left"
@@ -1439,7 +1439,7 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
       </li>
       
       <li>
-        <a href="https://github.com/ocademy-ai/machine-learning/edit/release/open-machine-learning-jupyter-book/deep-learning/time-series.md"
+        <a href="https://github.com/ocademy-ai/machine-learning/edit/release/open-machine-learning-jupyter-book/deep-learning/time-series.ipynb"
    class="headerbtn"
    data-toggle="tooltip"
 data-placement="left"
@@ -1471,23 +1471,6 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
    class="headerbtn"
    data-toggle="tooltip"
 data-placement="left"
-title="Download notebook file"
->
-  
-
-<span class="headerbtn__icon-container">
-  <i class="fas fa-code"></i>
-  </span>
-<span class="headerbtn__text-container">.ipynb</span>
-</a>
-
-      </li>
-      
-      <li>
-        <a href="../_sources/deep-learning/time-series.md"
-   class="headerbtn"
-   data-toggle="tooltip"
-data-placement="left"
 title="Download source file"
 >
   
@@ -1495,7 +1478,7 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
 <span class="headerbtn__icon-container">
   <i class="fas fa-file"></i>
   </span>
-<span class="headerbtn__text-container">.md</span>
+<span class="headerbtn__text-container">.ipynb</span>
 </a>
 
       </li>
@@ -1802,7 +1785,37 @@ <h2> Contents </h2>
                 
               <div>
                 
-  <section class="tex2jax_ignore mathjax_ignore" id="time-series">
+  <hr class="docutils" />
+<p>jupytext:
+cell_metadata_filter: -all
+formats: md:myst
+text_representation:
+extension: .md
+format_name: myst
+format_version: 0.13
+jupytext_version: 1.11.5
+kernelspec:
+display_name: Python 3
+language: python
+name: python3</p>
+<div class="cell tag_hide-input docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Install the necessary dependencies</span>
+
+<span class="kn">import</span> <span class="nn">os</span>
+<span class="kn">import</span> <span class="nn">sys</span> 
+<span class="o">!{</span>sys.executable<span class="o">}</span><span class="w"> </span>-m<span class="w"> </span>pip<span class="w"> </span>install<span class="w"> </span>--quiet<span class="w"> </span>pandas<span class="w"> </span>scikit-learn<span class="w"> </span>numpy<span class="w"> </span>matplotlib<span class="w"> </span>jupyterlab_myst<span class="w"> </span>ipython<span class="w"> </span>requests
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>WARNING: Ignoring invalid distribution -ytest (c:\users\victor\anaconda3\envs\open-machine-learning-jupyter-book\lib\site-packages)
+WARNING: Ignoring invalid distribution -ytest (c:\users\victor\anaconda3\envs\open-machine-learning-jupyter-book\lib\site-packages)
+</pre></div>
+</div>
+</div>
+</div>
+<section class="tex2jax_ignore mathjax_ignore" id="time-series">
 <h1><span class="section-number">26. </span>Time series<a class="headerlink" href="#time-series" title="Permalink to this headline">#</a></h1>
 <section id="overiew">
 <h2><span class="section-number">26.1. </span>Overiew<a class="headerlink" href="#overiew" title="Permalink to this headline">#</a></h2>
@@ -1825,9 +1838,9 @@ <h2><span class="section-number">26.2. </span>What is a Time Series?<a class="he
 </div>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
 <span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span>
-    <span class="s2">&quot;../assets/data/book_sales.csv&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;https://static-1300131294.cos.ap-shanghai.myqcloud.com/data/book_sales.csv&quot;</span><span class="p">,</span>
     <span class="n">index_col</span><span class="o">=</span><span class="s1">&#39;Date&#39;</span><span class="p">,</span>
     <span class="n">parse_dates</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;Date&#39;</span><span class="p">],</span>
 <span class="p">)</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="s1">&#39;Paperback&#39;</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
@@ -1835,6 +1848,56 @@ <h2><span class="section-number">26.2. </span>What is a Time Series?<a class="he
 </pre></div>
 </div>
 </div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>Hardcover</th>
+    </tr>
+    <tr>
+      <th>Date</th>
+      <th></th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>2000-04-01</th>
+      <td>139</td>
+    </tr>
+    <tr>
+      <th>2000-04-02</th>
+      <td>128</td>
+    </tr>
+    <tr>
+      <th>2000-04-03</th>
+      <td>172</td>
+    </tr>
+    <tr>
+      <th>2000-04-04</th>
+      <td>139</td>
+    </tr>
+    <tr>
+      <th>2000-04-05</th>
+      <td>191</td>
+    </tr>
+  </tbody>
+</table>
+</div></div></div>
 </div>
 <p>This series records the number of hardcover book sales at a retail store over 30 days. Notice that we have a single column of observations <span class="math notranslate nohighlight">\(Hardcover\)</span> with a time index <span class="math notranslate nohighlight">\(Date\)</span>.</p>
 </section>
@@ -1850,19 +1913,76 @@ <h3><span class="section-number">26.3.1. </span>Time-step features<a class="head
 <p>Time-step features are features we can derive directly from the time index. The most basic time-step feature is the time dummy, which counts off time steps in the series from beginning to end.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
 <span class="n">df</span><span class="p">[</span><span class="s1">&#39;Time&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">df</span><span class="o">.</span><span class="n">index</span><span class="p">))</span>
 <span class="n">df</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
 </pre></div>
 </div>
 </div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>Hardcover</th>
+      <th>Time</th>
+    </tr>
+    <tr>
+      <th>Date</th>
+      <th></th>
+      <th></th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>2000-04-01</th>
+      <td>139</td>
+      <td>0</td>
+    </tr>
+    <tr>
+      <th>2000-04-02</th>
+      <td>128</td>
+      <td>1</td>
+    </tr>
+    <tr>
+      <th>2000-04-03</th>
+      <td>172</td>
+      <td>2</td>
+    </tr>
+    <tr>
+      <th>2000-04-04</th>
+      <td>139</td>
+      <td>3</td>
+    </tr>
+    <tr>
+      <th>2000-04-05</th>
+      <td>191</td>
+      <td>4</td>
+    </tr>
+  </tbody>
+</table>
+</div></div></div>
 </div>
 <p>Linear regression with the time dummy produces the model:</p>
 <p><span class="math notranslate nohighlight">\(target = weight * time + bias\)</span></p>
 <p>The time dummy then lets us fit curves to time series in a time plot, where Time forms the x-axis.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
 <span class="kn">import</span> <span class="nn">seaborn</span> <span class="k">as</span> <span class="nn">sns</span>
 <span class="n">plt</span><span class="o">.</span><span class="n">style</span><span class="o">.</span><span class="n">use</span><span class="p">(</span><span class="s2">&quot;seaborn-whitegrid&quot;</span><span class="p">)</span>
 <span class="n">plt</span><span class="o">.</span><span class="n">rc</span><span class="p">(</span>
@@ -1880,7 +2000,7 @@ <h3><span class="section-number">26.3.1. </span>Time-step features<a class="head
     <span class="n">titlesize</span><span class="o">=</span><span class="mi">16</span><span class="p">,</span>
     <span class="n">titlepad</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span>
 <span class="p">)</span>
-<span class="o">%</span><span class="n">config</span> <span class="n">InlineBackend</span><span class="o">.</span><span class="n">figure_format</span> <span class="o">=</span> <span class="s1">&#39;retina&#39;</span>
+<span class="o">%</span><span class="k">config</span> InlineBackend.figure_format = &#39;retina&#39;
 
 <span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">()</span>
 <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="s1">&#39;Time&#39;</span><span class="p">,</span> <span class="s1">&#39;Hardcover&#39;</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="n">df</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;0.75&#39;</span><span class="p">)</span>
@@ -1889,6 +2009,9 @@ <h3><span class="section-number">26.3.1. </span>Time-step features<a class="head
 </pre></div>
 </div>
 </div>
+<div class="cell_output docutils container">
+<img alt="../_images/time-series_7_0.png" src="../_images/time-series_7_0.png" />
+</div>
 </div>
 <p>Time-step features let you model time dependence. A series is time dependent if its values can be predicted from the time they occured. In the Hardcover Sales series, we can predict that sales later in the month are generally higher than sales earlier in the month.</p>
 </section>
@@ -1897,23 +2020,83 @@ <h3><span class="section-number">26.3.2. </span>Lag features<a class="headerlink
 <p>To make a lag feature we shift the observations of the target series so that they appear to have occured later in time. Here we’ve created a 1-step lag feature, though shifting by multiple steps is possible too.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;Lag_1&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s1">&#39;Hardcover&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">shift</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;Lag_1&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s1">&#39;Hardcover&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">shift</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
 <span class="n">df</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">reindex</span><span class="p">(</span><span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;Hardcover&#39;</span><span class="p">,</span> <span class="s1">&#39;Lag_1&#39;</span><span class="p">])</span>
 <span class="n">df</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
 </pre></div>
 </div>
 </div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>Hardcover</th>
+      <th>Lag_1</th>
+    </tr>
+    <tr>
+      <th>Date</th>
+      <th></th>
+      <th></th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>2000-04-01</th>
+      <td>139</td>
+      <td>NaN</td>
+    </tr>
+    <tr>
+      <th>2000-04-02</th>
+      <td>128</td>
+      <td>139.0</td>
+    </tr>
+    <tr>
+      <th>2000-04-03</th>
+      <td>172</td>
+      <td>128.0</td>
+    </tr>
+    <tr>
+      <th>2000-04-04</th>
+      <td>139</td>
+      <td>172.0</td>
+    </tr>
+    <tr>
+      <th>2000-04-05</th>
+      <td>191</td>
+      <td>139.0</td>
+    </tr>
+  </tbody>
+</table>
+</div></div></div>
 </div>
 <p>Linear regression with a lag feature produces the model: <span class="math notranslate nohighlight">\(target = weight * lag + bias\)</span>. So lag features let us fit curves to lag plots where each observation in a series is plotted against the previous observation.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">()</span>
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">()</span>
 <span class="n">ax</span> <span class="o">=</span> <span class="n">sns</span><span class="o">.</span><span class="n">regplot</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="s1">&#39;Lag_1&#39;</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="s1">&#39;Hardcover&#39;</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="n">df</span><span class="p">,</span> <span class="n">ci</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">scatter_kws</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span><span class="n">color</span><span class="o">=</span><span class="s1">&#39;0.25&#39;</span><span class="p">))</span>
 <span class="n">ax</span><span class="o">.</span><span class="n">set_aspect</span><span class="p">(</span><span class="s1">&#39;equal&#39;</span><span class="p">)</span>
 <span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">&#39;Lag Plot of Hardcover Sales&#39;</span><span class="p">);</span>
 </pre></div>
 </div>
 </div>
+<div class="cell_output docutils container">
+<img alt="../_images/time-series_11_0.png" src="../_images/time-series_11_0.png" />
+</div>
 </div>
 <p>You can see from the lag plot that sales on one day <span class="math notranslate nohighlight">\((Hardcover)\)</span> are correlated with sales from the previous day <span class="math notranslate nohighlight">\((Lag_1)\)</span>. When you see a relationship like this, you know a lag feature will be useful.</p>
 <p>More generally, lag features let you model serial dependence. A time series has serial dependence when an observation can be predicted from previous observations. In Hardcover Sales, we can predict that high sales on one day usually mean high sales the next day.</p>
@@ -1925,12 +2108,14 @@ <h2><span class="section-number">26.4. </span>Example - Tunnel Traffic<a class="
 <p>Tunnel Traffic is a time series describing the number of vehicles traveling through the Baregg Tunnel in Switzerland each day from November 2003 to November 2005. In this example, we’ll get some practice applying linear regression to time-step features and lag features.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">pathlib</span> <span class="kn">import</span> <span class="n">Path</span>
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">pathlib</span> <span class="kn">import</span> <span class="n">Path</span>
 <span class="kn">from</span> <span class="nn">warnings</span> <span class="kn">import</span> <span class="n">simplefilter</span>
 
 <span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
 <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
 <span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+<span class="kn">import</span> <span class="nn">requests</span>
+<span class="kn">from</span> <span class="nn">io</span> <span class="kn">import</span> <span class="n">StringIO</span>
 
 <span class="n">simplefilter</span><span class="p">(</span><span class="s2">&quot;ignore&quot;</span><span class="p">)</span>  <span class="c1"># ignore warnings to clean up output cells</span>
 
@@ -1952,13 +2137,16 @@ <h2><span class="section-number">26.4. </span>Example - Tunnel Traffic<a class="
     <span class="n">markerfacecolor</span><span class="o">=</span><span class="s2">&quot;0.25&quot;</span><span class="p">,</span>
     <span class="n">legend</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
 <span class="p">)</span>
-<span class="o">%</span><span class="n">config</span> <span class="n">InlineBackend</span><span class="o">.</span><span class="n">figure_format</span> <span class="o">=</span> <span class="s1">&#39;retina&#39;</span>
+<span class="o">%</span><span class="k">config</span> InlineBackend.figure_format = &#39;retina&#39;
 
 
 <span class="c1"># Load Tunnel Traffic dataset</span>
-<span class="n">data_dir</span> <span class="o">=</span> <span class="n">Path</span><span class="p">(</span><span class="s2">&quot;../assets/data&quot;</span><span class="p">)</span>
-<span class="n">tunnel</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">data_dir</span> <span class="o">/</span> <span class="s2">&quot;tunnel.csv&quot;</span><span class="p">,</span> <span class="n">parse_dates</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;Day&quot;</span><span class="p">])</span>
+<span class="n">cloud_link</span> <span class="o">=</span> <span class="s2">&quot;https://static-1300131294.cos.ap-shanghai.myqcloud.com/data/tunnel.csv&quot;</span>
+<span class="n">response</span> <span class="o">=</span> <span class="n">requests</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">cloud_link</span><span class="p">)</span>
+<span class="n">response</span><span class="o">.</span><span class="n">raise_for_status</span><span class="p">()</span>
 
+<span class="n">data</span> <span class="o">=</span> <span class="n">StringIO</span><span class="p">(</span><span class="n">response</span><span class="o">.</span><span class="n">text</span><span class="p">)</span>
+<span class="n">tunnel</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">parse_dates</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;Day&quot;</span><span class="p">])</span>
 <span class="c1"># Create a time series in Pandas by setting the index to a date</span>
 <span class="c1"># column. We parsed &quot;Day&quot; as a date type by using `parse_dates` when</span>
 <span class="c1"># loading the data.</span>
@@ -1976,13 +2164,63 @@ <h2><span class="section-number">26.4. </span>Example - Tunnel Traffic<a class="
 </pre></div>
 </div>
 </div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>NumVehicles</th>
+    </tr>
+    <tr>
+      <th>Day</th>
+      <th></th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>2003-11-01</th>
+      <td>103536</td>
+    </tr>
+    <tr>
+      <th>2003-11-02</th>
+      <td>92051</td>
+    </tr>
+    <tr>
+      <th>2003-11-03</th>
+      <td>100795</td>
+    </tr>
+    <tr>
+      <th>2003-11-04</th>
+      <td>102352</td>
+    </tr>
+    <tr>
+      <th>2003-11-05</th>
+      <td>106569</td>
+    </tr>
+  </tbody>
+</table>
+</div></div></div>
 </div>
 <section id="time-step-feature">
 <h3><span class="section-number">26.4.1. </span>Time-step feature<a class="headerlink" href="#time-step-feature" title="Permalink to this headline">#</a></h3>
 <p>Provided the time series doesn’t have any missing dates, we can create a time dummy by counting out the length of the series.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">df</span> <span class="o">=</span> <span class="n">tunnel</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span> <span class="o">=</span> <span class="n">tunnel</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
 
 <span class="n">df</span><span class="p">[</span><span class="s1">&#39;Time&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">tunnel</span><span class="o">.</span><span class="n">index</span><span class="p">))</span>
 
@@ -1990,11 +2228,68 @@ <h3><span class="section-number">26.4.1. </span>Time-step feature<a class="heade
 </pre></div>
 </div>
 </div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>NumVehicles</th>
+      <th>Time</th>
+    </tr>
+    <tr>
+      <th>Day</th>
+      <th></th>
+      <th></th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>2003-11-01</th>
+      <td>103536</td>
+      <td>0</td>
+    </tr>
+    <tr>
+      <th>2003-11-02</th>
+      <td>92051</td>
+      <td>1</td>
+    </tr>
+    <tr>
+      <th>2003-11-03</th>
+      <td>100795</td>
+      <td>2</td>
+    </tr>
+    <tr>
+      <th>2003-11-04</th>
+      <td>102352</td>
+      <td>3</td>
+    </tr>
+    <tr>
+      <th>2003-11-05</th>
+      <td>106569</td>
+      <td>4</td>
+    </tr>
+  </tbody>
+</table>
+</div></div></div>
 </div>
 <p>The procedure for fitting a linear regression model follows the standard steps for scikit-learn.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">LinearRegression</span>
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">LinearRegression</span>
 
 <span class="c1"># Training data</span>
 <span class="n">X</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">loc</span><span class="p">[:,</span> <span class="p">[</span><span class="s1">&#39;Time&#39;</span><span class="p">]]</span>  <span class="c1"># features</span>
@@ -2014,12 +2309,15 @@ <h3><span class="section-number">26.4.1. </span>Time-step feature<a class="heade
 <p>The model actually created is (approximately): <span class="math notranslate nohighlight">\(Vehicles = 22.5 * Time + 98176\)</span>. Plotting the fitted values over time shows us how fitting linear regression to the time dummy creates the trend line defined by this equation.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">ax</span> <span class="o">=</span> <span class="n">y</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="o">**</span><span class="n">plot_params</span><span class="p">)</span>
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">ax</span> <span class="o">=</span> <span class="n">y</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="o">**</span><span class="n">plot_params</span><span class="p">)</span>
 <span class="n">ax</span> <span class="o">=</span> <span class="n">y_pred</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>
 <span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">&#39;Time Plot of Tunnel Traffic&#39;</span><span class="p">);</span>
 </pre></div>
 </div>
 </div>
+<div class="cell_output docutils container">
+<img alt="../_images/time-series_19_0.png" src="../_images/time-series_19_0.png" />
+</div>
 </div>
 </section>
 <section id="lag-feature">
@@ -2027,16 +2325,80 @@ <h3><span class="section-number">26.4.2. </span>Lag feature<a class="headerlink"
 <p>Pandas provides us a simple method to lag a series, the <span class="math notranslate nohighlight">\(shift\)</span> method.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;Lag_1&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s1">&#39;NumVehicles&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">shift</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;Lag_1&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s1">&#39;NumVehicles&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">shift</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
 <span class="n">df</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
 </pre></div>
 </div>
 </div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>NumVehicles</th>
+      <th>Time</th>
+      <th>Lag_1</th>
+    </tr>
+    <tr>
+      <th>Day</th>
+      <th></th>
+      <th></th>
+      <th></th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>2003-11-01</th>
+      <td>103536</td>
+      <td>0</td>
+      <td>NaN</td>
+    </tr>
+    <tr>
+      <th>2003-11-02</th>
+      <td>92051</td>
+      <td>1</td>
+      <td>103536.0</td>
+    </tr>
+    <tr>
+      <th>2003-11-03</th>
+      <td>100795</td>
+      <td>2</td>
+      <td>92051.0</td>
+    </tr>
+    <tr>
+      <th>2003-11-04</th>
+      <td>102352</td>
+      <td>3</td>
+      <td>100795.0</td>
+    </tr>
+    <tr>
+      <th>2003-11-05</th>
+      <td>106569</td>
+      <td>4</td>
+      <td>102352.0</td>
+    </tr>
+  </tbody>
+</table>
+</div></div></div>
 </div>
 <p>When creating lag features, we need to decide what to do with the missing values produced. Filling them in is one option, maybe with 0.0 or “backfilling” with the first known value. Instead, we’ll just drop the missing values, making sure to also drop values in the target from corresponding dates.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">LinearRegression</span>
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">LinearRegression</span>
 
 <span class="n">X</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">loc</span><span class="p">[:,</span> <span class="p">[</span><span class="s1">&#39;Lag_1&#39;</span><span class="p">]]</span>
 <span class="n">X</span><span class="o">.</span><span class="n">dropna</span><span class="p">(</span><span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>  <span class="c1"># drop missing values in the feature set</span>
@@ -2054,7 +2416,7 @@ <h3><span class="section-number">26.4.2. </span>Lag feature<a class="headerlink"
 <p>The lag plot shows us how well we were able to fit the relationship between the number of vehicles one day and the number the previous day.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">()</span>
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">()</span>
 <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">X</span><span class="p">[</span><span class="s1">&#39;Lag_1&#39;</span><span class="p">],</span> <span class="n">y</span><span class="p">,</span> <span class="s1">&#39;.&#39;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;0.25&#39;</span><span class="p">)</span>
 <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">X</span><span class="p">[</span><span class="s1">&#39;Lag_1&#39;</span><span class="p">],</span> <span class="n">y_pred</span><span class="p">)</span>
 <span class="n">ax</span><span class="o">.</span><span class="n">set_aspect</span><span class="p">(</span><span class="s1">&#39;equal&#39;</span><span class="p">)</span>
@@ -2064,15 +2426,21 @@ <h3><span class="section-number">26.4.2. </span>Lag feature<a class="headerlink"
 </pre></div>
 </div>
 </div>
+<div class="cell_output docutils container">
+<img alt="../_images/time-series_25_0.png" src="../_images/time-series_25_0.png" />
+</div>
 </div>
 <p>What does this prediction from a lag feature mean about how well we can predict the series across time? The following time plot shows us how our forecasts now respond to the behavior of the series in the recent past.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">ax</span> <span class="o">=</span> <span class="n">y</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="o">**</span><span class="n">plot_params</span><span class="p">)</span>
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">ax</span> <span class="o">=</span> <span class="n">y</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="o">**</span><span class="n">plot_params</span><span class="p">)</span>
 <span class="n">ax</span> <span class="o">=</span> <span class="n">y_pred</span><span class="o">.</span><span class="n">plot</span><span class="p">()</span>
 </pre></div>
 </div>
 </div>
+<div class="cell_output docutils container">
+<img alt="../_images/time-series_27_0.png" src="../_images/time-series_27_0.png" />
+</div>
 </div>
 <p>The best time series models will usually include some combination of time-step features and lag features. Over the next few lessons, we’ll learn how to engineer features modeling the most common patterns in time series using the features from this lesson as a starting point.</p>
 </section>
@@ -2089,7 +2457,7 @@ <h3><span class="section-number">26.5.1. </span>Defining the Forecasting Task<a
 <p>The forecast origin is time at which you are making a forecast. Practically, you might consider the forecast origin to be the last time for which you have training data for the time being predicted. Everything up to he origin can be used to create features.</p>
 <p>The forecast horizon is the time for which you are making a forecast. We often describe a forecast by the number of time steps in its horizon: a “1-step” forecast or “5-step” forecast, say. The forecast horizon describes the target.</p>
 <figure class="align-default" id="def">
-<a class="bg-white mb-1 reference internal image-reference" href="../_images/01_def.png"><img alt="../_images/01_def.png" class="bg-white mb-1" src="../_images/01_def.png" style="width: 90%;" /></a>
+<a class="bg-white mb-1 reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/time-series/01_def.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/time-series/01_def.png" class="bg-white mb-1" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/time-series/01_def.png" style="width: 90%;" /></a>
 <figcaption>
 <p><span class="caption-number">Fig. 26.1 </span><span class="caption-text">A three-step forecast horizon with a two-step lead time, using four lag features. The figure represents what would be a single row of training data – data for a single prediction, in other words.</span><a class="headerlink" href="#def" title="Permalink to this image">#</a></p>
 </figcaption>
@@ -2103,7 +2471,7 @@ <h3><span class="section-number">26.5.2. </span>Preparing Data for Forecasting<a
 <p>Each row in a dataframe represents a single forecast. The time index of the row is the first time in the forecast horizon, but we arrange values for the entire horizon in the same row. For multistep forecasts, this means we are requiring a model to produce multiple outputs, one for each step.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
 <span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
 
 <span class="n">N</span> <span class="o">=</span> <span class="mi">20</span>
@@ -2119,7 +2487,7 @@ <h3><span class="section-number">26.5.2. </span>Preparing Data for Forecasting<a
     <span class="s1">&#39;y_lag_3&#39;</span><span class="p">:</span> <span class="n">ts</span><span class="o">.</span><span class="n">shift</span><span class="p">(</span><span class="mi">3</span><span class="p">),</span>
     <span class="s1">&#39;y_lag_4&#39;</span><span class="p">:</span> <span class="n">ts</span><span class="o">.</span><span class="n">shift</span><span class="p">(</span><span class="mi">4</span><span class="p">),</span>
     <span class="s1">&#39;y_lag_5&#39;</span><span class="p">:</span> <span class="n">ts</span><span class="o">.</span><span class="n">shift</span><span class="p">(</span><span class="mi">5</span><span class="p">),</span>
-    <span class="s1">&#39;y_lag_6&#39;</span><span class="p">:</span> <span class="n">ts</span><span class="o">.</span><span class="n">shift</span><span class="p">(</span><span class="mi">6</span><span class="p">),</span>    
+    <span class="s1">&#39;y_lag_6&#39;</span><span class="p">:</span> <span class="n">ts</span><span class="o">.</span><span class="n">shift</span><span class="p">(</span><span class="mi">6</span><span class="p">),</span>
 <span class="p">})</span>
 
 <span class="c1"># Multistep targets</span>
@@ -2136,6 +2504,159 @@ <h3><span class="section-number">26.5.2. </span>Preparing Data for Forecasting<a
 </pre></div>
 </div>
 </div>
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+</style>
+<table id="T_7d221">
+  <thead>
+    <tr>
+      <th class="blank level0" >&nbsp;</th>
+      <th id="T_7d221_level0_col0" class="col_heading level0 col0" colspan="3">Targets</th>
+      <th id="T_7d221_level0_col3" class="col_heading level0 col3" colspan="5">Features</th>
+    </tr>
+    <tr>
+      <th class="blank level1" >&nbsp;</th>
+      <th id="T_7d221_level1_col0" class="col_heading level1 col0" >y_step_3</th>
+      <th id="T_7d221_level1_col1" class="col_heading level1 col1" >y_step_2</th>
+      <th id="T_7d221_level1_col2" class="col_heading level1 col2" >y_step_1</th>
+      <th id="T_7d221_level1_col3" class="col_heading level1 col3" >y_lag_2</th>
+      <th id="T_7d221_level1_col4" class="col_heading level1 col4" >y_lag_3</th>
+      <th id="T_7d221_level1_col5" class="col_heading level1 col5" >y_lag_4</th>
+      <th id="T_7d221_level1_col6" class="col_heading level1 col6" >y_lag_5</th>
+      <th id="T_7d221_level1_col7" class="col_heading level1 col7" >y_lag_6</th>
+    </tr>
+    <tr>
+      <th class="index_name level0" >Year</th>
+      <th class="blank col0" >&nbsp;</th>
+      <th class="blank col1" >&nbsp;</th>
+      <th class="blank col2" >&nbsp;</th>
+      <th class="blank col3" >&nbsp;</th>
+      <th class="blank col4" >&nbsp;</th>
+      <th class="blank col5" >&nbsp;</th>
+      <th class="blank col6" >&nbsp;</th>
+      <th class="blank col7" >&nbsp;</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th id="T_7d221_level0_row0" class="row_heading level0 row0" >2010</th>
+      <td id="T_7d221_row0_col0" class="data row0 col0" >2</td>
+      <td id="T_7d221_row0_col1" class="data row0 col1" >1</td>
+      <td id="T_7d221_row0_col2" class="data row0 col2" >0</td>
+      <td id="T_7d221_row0_col3" class="data row0 col3" >None</td>
+      <td id="T_7d221_row0_col4" class="data row0 col4" >None</td>
+      <td id="T_7d221_row0_col5" class="data row0 col5" >None</td>
+      <td id="T_7d221_row0_col6" class="data row0 col6" >None</td>
+      <td id="T_7d221_row0_col7" class="data row0 col7" >None</td>
+    </tr>
+    <tr>
+      <th id="T_7d221_level0_row1" class="row_heading level0 row1" >2011</th>
+      <td id="T_7d221_row1_col0" class="data row1 col0" >3</td>
+      <td id="T_7d221_row1_col1" class="data row1 col1" >2</td>
+      <td id="T_7d221_row1_col2" class="data row1 col2" >1</td>
+      <td id="T_7d221_row1_col3" class="data row1 col3" >None</td>
+      <td id="T_7d221_row1_col4" class="data row1 col4" >None</td>
+      <td id="T_7d221_row1_col5" class="data row1 col5" >None</td>
+      <td id="T_7d221_row1_col6" class="data row1 col6" >None</td>
+      <td id="T_7d221_row1_col7" class="data row1 col7" >None</td>
+    </tr>
+    <tr>
+      <th id="T_7d221_level0_row2" class="row_heading level0 row2" >2012</th>
+      <td id="T_7d221_row2_col0" class="data row2 col0" >4</td>
+      <td id="T_7d221_row2_col1" class="data row2 col1" >3</td>
+      <td id="T_7d221_row2_col2" class="data row2 col2" >2</td>
+      <td id="T_7d221_row2_col3" class="data row2 col3" >0</td>
+      <td id="T_7d221_row2_col4" class="data row2 col4" >None</td>
+      <td id="T_7d221_row2_col5" class="data row2 col5" >None</td>
+      <td id="T_7d221_row2_col6" class="data row2 col6" >None</td>
+      <td id="T_7d221_row2_col7" class="data row2 col7" >None</td>
+    </tr>
+    <tr>
+      <th id="T_7d221_level0_row3" class="row_heading level0 row3" >2013</th>
+      <td id="T_7d221_row3_col0" class="data row3 col0" >5</td>
+      <td id="T_7d221_row3_col1" class="data row3 col1" >4</td>
+      <td id="T_7d221_row3_col2" class="data row3 col2" >3</td>
+      <td id="T_7d221_row3_col3" class="data row3 col3" >1</td>
+      <td id="T_7d221_row3_col4" class="data row3 col4" >0</td>
+      <td id="T_7d221_row3_col5" class="data row3 col5" >None</td>
+      <td id="T_7d221_row3_col6" class="data row3 col6" >None</td>
+      <td id="T_7d221_row3_col7" class="data row3 col7" >None</td>
+    </tr>
+    <tr>
+      <th id="T_7d221_level0_row4" class="row_heading level0 row4" >2014</th>
+      <td id="T_7d221_row4_col0" class="data row4 col0" >6</td>
+      <td id="T_7d221_row4_col1" class="data row4 col1" >5</td>
+      <td id="T_7d221_row4_col2" class="data row4 col2" >4</td>
+      <td id="T_7d221_row4_col3" class="data row4 col3" >2</td>
+      <td id="T_7d221_row4_col4" class="data row4 col4" >1</td>
+      <td id="T_7d221_row4_col5" class="data row4 col5" >0</td>
+      <td id="T_7d221_row4_col6" class="data row4 col6" >None</td>
+      <td id="T_7d221_row4_col7" class="data row4 col7" >None</td>
+    </tr>
+    <tr>
+      <th id="T_7d221_level0_row5" class="row_heading level0 row5" >2015</th>
+      <td id="T_7d221_row5_col0" class="data row5 col0" >7</td>
+      <td id="T_7d221_row5_col1" class="data row5 col1" >6</td>
+      <td id="T_7d221_row5_col2" class="data row5 col2" >5</td>
+      <td id="T_7d221_row5_col3" class="data row5 col3" >3</td>
+      <td id="T_7d221_row5_col4" class="data row5 col4" >2</td>
+      <td id="T_7d221_row5_col5" class="data row5 col5" >1</td>
+      <td id="T_7d221_row5_col6" class="data row5 col6" >0</td>
+      <td id="T_7d221_row5_col7" class="data row5 col7" >None</td>
+    </tr>
+    <tr>
+      <th id="T_7d221_level0_row6" class="row_heading level0 row6" >2016</th>
+      <td id="T_7d221_row6_col0" class="data row6 col0" >8</td>
+      <td id="T_7d221_row6_col1" class="data row6 col1" >7</td>
+      <td id="T_7d221_row6_col2" class="data row6 col2" >6</td>
+      <td id="T_7d221_row6_col3" class="data row6 col3" >4</td>
+      <td id="T_7d221_row6_col4" class="data row6 col4" >3</td>
+      <td id="T_7d221_row6_col5" class="data row6 col5" >2</td>
+      <td id="T_7d221_row6_col6" class="data row6 col6" >1</td>
+      <td id="T_7d221_row6_col7" class="data row6 col7" >0</td>
+    </tr>
+    <tr>
+      <th id="T_7d221_level0_row7" class="row_heading level0 row7" >2017</th>
+      <td id="T_7d221_row7_col0" class="data row7 col0" >9</td>
+      <td id="T_7d221_row7_col1" class="data row7 col1" >8</td>
+      <td id="T_7d221_row7_col2" class="data row7 col2" >7</td>
+      <td id="T_7d221_row7_col3" class="data row7 col3" >5</td>
+      <td id="T_7d221_row7_col4" class="data row7 col4" >4</td>
+      <td id="T_7d221_row7_col5" class="data row7 col5" >3</td>
+      <td id="T_7d221_row7_col6" class="data row7 col6" >2</td>
+      <td id="T_7d221_row7_col7" class="data row7 col7" >1</td>
+    </tr>
+    <tr>
+      <th id="T_7d221_level0_row8" class="row_heading level0 row8" >2018</th>
+      <td id="T_7d221_row8_col0" class="data row8 col0" >10</td>
+      <td id="T_7d221_row8_col1" class="data row8 col1" >9</td>
+      <td id="T_7d221_row8_col2" class="data row8 col2" >8</td>
+      <td id="T_7d221_row8_col3" class="data row8 col3" >6</td>
+      <td id="T_7d221_row8_col4" class="data row8 col4" >5</td>
+      <td id="T_7d221_row8_col5" class="data row8 col5" >4</td>
+      <td id="T_7d221_row8_col6" class="data row8 col6" >3</td>
+      <td id="T_7d221_row8_col7" class="data row8 col7" >2</td>
+    </tr>
+    <tr>
+      <th id="T_7d221_level0_row9" class="row_heading level0 row9" >2019</th>
+      <td id="T_7d221_row9_col0" class="data row9 col0" >11</td>
+      <td id="T_7d221_row9_col1" class="data row9 col1" >10</td>
+      <td id="T_7d221_row9_col2" class="data row9 col2" >9</td>
+      <td id="T_7d221_row9_col3" class="data row9 col3" >7</td>
+      <td id="T_7d221_row9_col4" class="data row9 col4" >6</td>
+      <td id="T_7d221_row9_col5" class="data row9 col5" >5</td>
+      <td id="T_7d221_row9_col6" class="data row9 col6" >4</td>
+      <td id="T_7d221_row9_col7" class="data row9 col7" >3</td>
+    </tr>
+  </tbody>
+</table>
+</div></div>
 </div>
 <p>The above illustrates how a dataset would be prepared similar to the Defining a Forecast figure: a three-step forecasting task with a two-step lead time using five lag features. The original time series is y_step_1. The missing values we could either fill-in or drop.</p>
 </section>
@@ -2146,7 +2667,7 @@ <h3><span class="section-number">26.5.3. </span>Multistep Forecasting Strategies
 <h4><span class="section-number">26.5.3.1. </span>Multioutput model<a class="headerlink" href="#multioutput-model" title="Permalink to this headline">#</a></h4>
 <p>Use a model that produces multiple outputs naturally. Linear regression and neural networks can both produce multiple outputs. This strategy is simple and efficient, but not possible for every algorithm you might want to use. XGBoost can’t do this, for instance.</p>
 <figure class="align-default" id="multioutput">
-<a class="bg-white mb-1 reference internal image-reference" href="../_images/02_multioutput.png"><img alt="../_images/02_multioutput.png" class="bg-white mb-1" src="../_images/02_multioutput.png" style="width: 90%;" /></a>
+<a class="bg-white mb-1 reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/time-series/02_multioutput.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/time-series/02_multioutput.png" class="bg-white mb-1" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/time-series/02_multioutput.png" style="width: 90%;" /></a>
 <figcaption>
 <p><span class="caption-number">Fig. 26.2 </span><span class="caption-text">Multioutput Model</span><a class="headerlink" href="#multioutput" title="Permalink to this image">#</a></p>
 </figcaption>
@@ -2156,7 +2677,7 @@ <h4><span class="section-number">26.5.3.1. </span>Multioutput model<a class="hea
 <h4><span class="section-number">26.5.3.2. </span>Direct strategy<a class="headerlink" href="#direct-strategy" title="Permalink to this headline">#</a></h4>
 <p>Train a separate model for each step in the horizon: one model forecasts 1-step ahead, another 2-steps ahead, and so on. Forecasting 1-step ahead is a different problem than 2-steps ahead (and so on), so it can help to have a different model make forecasts for each step. The downside is that training lots of models can be computationally expensive.</p>
 <figure class="align-default" id="direct">
-<a class="bg-white mb-1 reference internal image-reference" href="../_images/03_direct.png"><img alt="../_images/03_direct.png" class="bg-white mb-1" src="../_images/03_direct.png" style="width: 90%;" /></a>
+<a class="bg-white mb-1 reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/time-series/03_direct.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/time-series/03_direct.png" class="bg-white mb-1" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/time-series/03_direct.png" style="width: 90%;" /></a>
 <figcaption>
 <p><span class="caption-number">Fig. 26.3 </span><span class="caption-text">Direct Strategy</span><a class="headerlink" href="#direct" title="Permalink to this image">#</a></p>
 </figcaption>
@@ -2166,7 +2687,7 @@ <h4><span class="section-number">26.5.3.2. </span>Direct strategy<a class="heade
 <h4><span class="section-number">26.5.3.3. </span>Recursive strategy<a class="headerlink" href="#recursive-strategy" title="Permalink to this headline">#</a></h4>
 <p>Train a single one-step model and use its forecasts to update the lag features for the next step. With the recursive method, we feed a model’s 1-step forecast back in to that same model to use as a lag feature for the next forecasting step. We only need to train one model, but since errors will propagate from step to step, forecasts can be inaccurate for long horizons.</p>
 <figure class="align-default" id="recursive">
-<a class="bg-white mb-1 reference internal image-reference" href="../_images/04_recursive.png"><img alt="../_images/04_recursive.png" class="bg-white mb-1" src="../_images/04_recursive.png" style="width: 90%;" /></a>
+<a class="bg-white mb-1 reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/time-series/04_recursive.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/time-series/04_recursive.png" class="bg-white mb-1" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/time-series/04_recursive.png" style="width: 90%;" /></a>
 <figcaption>
 <p><span class="caption-number">Fig. 26.4 </span><span class="caption-text">Recursive Strategy</span><a class="headerlink" href="#recursive" title="Permalink to this image">#</a></p>
 </figcaption>
@@ -2176,7 +2697,7 @@ <h4><span class="section-number">26.5.3.3. </span>Recursive strategy<a class="he
 <h4><span class="section-number">26.5.3.4. </span>DirRec strategy<a class="headerlink" href="#dirrec-strategy" title="Permalink to this headline">#</a></h4>
 <p>A combination of the direct and recursive strategies: train a model for each step and use forecasts from previous steps as new lag features. Step by step, each model gets an additional lag input. Since each model always has an up-to-date set of lag features, the DirRec strategy can capture serial dependence better than Direct, but it can also suffer from error propagation like Recursive.</p>
 <figure class="align-default" id="dirrec">
-<a class="bg-white mb-1 reference internal image-reference" href="../_images/05_dirrec.png"><img alt="../_images/05_dirrec.png" class="bg-white mb-1" src="../_images/05_dirrec.png" style="width: 90%;" /></a>
+<a class="bg-white mb-1 reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/time-series/05_dirrec.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/time-series/05_dirrec.png" class="bg-white mb-1" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/time-series/05_dirrec.png" style="width: 90%;" /></a>
 <figcaption>
 <p><span class="caption-number">Fig. 26.5 </span><span class="caption-text">DirRec Strategy</span><a class="headerlink" href="#dirrec" title="Permalink to this image">#</a></p>
 </figcaption>
@@ -2190,7 +2711,7 @@ <h3><span class="section-number">26.5.4. </span>Example - Flu Trends<a class="he
 <p>The hidden cell sets up the example and defines a helper function <span class="math notranslate nohighlight">\(plot_multistep\)</span>.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">pathlib</span> <span class="kn">import</span> <span class="n">Path</span>
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">pathlib</span> <span class="kn">import</span> <span class="n">Path</span>
 <span class="kn">from</span> <span class="nn">warnings</span> <span class="kn">import</span> <span class="n">simplefilter</span>
 
 <span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
@@ -2221,7 +2742,7 @@ <h3><span class="section-number">26.5.4. </span>Example - Flu Trends<a class="he
     <span class="n">markeredgecolor</span><span class="o">=</span><span class="s2">&quot;0.25&quot;</span><span class="p">,</span>
     <span class="n">markerfacecolor</span><span class="o">=</span><span class="s2">&quot;0.25&quot;</span><span class="p">,</span>
 <span class="p">)</span>
-<span class="o">%</span><span class="n">config</span> <span class="n">InlineBackend</span><span class="o">.</span><span class="n">figure_format</span> <span class="o">=</span> <span class="s1">&#39;retina&#39;</span>
+<span class="o">%</span><span class="k">config</span> InlineBackend.figure_format = &#39;retina&#39;
 
 
 <span class="k">def</span> <span class="nf">plot_multistep</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">every</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">palette_kwargs</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
@@ -2238,8 +2759,7 @@ <h3><span class="section-number">26.5.4. </span>Example - Flu Trends<a class="he
     <span class="k">return</span> <span class="n">ax</span>
 
 
-<span class="n">data_dir</span> <span class="o">=</span> <span class="n">Path</span><span class="p">(</span><span class="s2">&quot;../assets/data&quot;</span><span class="p">)</span>
-<span class="n">flu_trends</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">data_dir</span> <span class="o">/</span> <span class="s2">&quot;flu-trends.csv&quot;</span><span class="p">)</span>
+<span class="n">flu_trends</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s2">&quot;https://static-1300131294.cos.ap-shanghai.myqcloud.com/data/flu-trends.csv&quot;</span><span class="p">)</span>
 <span class="n">flu_trends</span><span class="o">.</span><span class="n">set_index</span><span class="p">(</span>
     <span class="n">pd</span><span class="o">.</span><span class="n">PeriodIndex</span><span class="p">(</span><span class="n">flu_trends</span><span class="o">.</span><span class="n">Week</span><span class="p">,</span> <span class="n">freq</span><span class="o">=</span><span class="s2">&quot;W&quot;</span><span class="p">),</span>
     <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
@@ -2252,7 +2772,7 @@ <h3><span class="section-number">26.5.4. </span>Example - Flu Trends<a class="he
 <p>First we’ll prepare our target series (weekly office visits for the flu) for multistep forecasting. Once this is done, training and prediction will be very straightfoward.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">make_lags</span><span class="p">(</span><span class="n">ts</span><span class="p">,</span> <span class="n">lags</span><span class="p">,</span> <span class="n">lead_time</span><span class="o">=</span><span class="mi">1</span><span class="p">):</span>
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">make_lags</span><span class="p">(</span><span class="n">ts</span><span class="p">,</span> <span class="n">lags</span><span class="p">,</span> <span class="n">lead_time</span><span class="o">=</span><span class="mi">1</span><span class="p">):</span>
     <span class="k">return</span> <span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">(</span>
         <span class="p">{</span>
             <span class="sa">f</span><span class="s1">&#39;y_lag_</span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">:</span> <span class="n">ts</span><span class="o">.</span><span class="n">shift</span><span class="p">(</span><span class="n">i</span><span class="p">)</span>
@@ -2288,7 +2808,7 @@ <h4><span class="section-number">26.5.4.1. </span>Multioutput model<a class="hea
 <p>We’ll use linear regression as a MultiOutput strategy. Once we have our data prepared for multiple outputs, training and prediction is the same as always.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Create splits</span>
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Create splits</span>
 <span class="n">X_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">0.25</span><span class="p">,</span> <span class="n">shuffle</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
 
 <span class="n">model</span> <span class="o">=</span> <span class="n">LinearRegression</span><span class="p">()</span>
@@ -2303,7 +2823,7 @@ <h4><span class="section-number">26.5.4.1. </span>Multioutput model<a class="hea
 <p>Remember that a multistep model will produce a complete forecast for each instance used as input. There are 269 weeks in the training set and 90 weeks in the test set, and we now have an 8-step forecast for each of these weeks.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">train_rmse</span> <span class="o">=</span> <span class="n">mean_squared_error</span><span class="p">(</span><span class="n">y_train</span><span class="p">,</span> <span class="n">y_fit</span><span class="p">,</span> <span class="n">squared</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">train_rmse</span> <span class="o">=</span> <span class="n">mean_squared_error</span><span class="p">(</span><span class="n">y_train</span><span class="p">,</span> <span class="n">y_fit</span><span class="p">,</span> <span class="n">squared</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
 <span class="n">test_rmse</span> <span class="o">=</span> <span class="n">mean_squared_error</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">,</span> <span class="n">squared</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
 <span class="nb">print</span><span class="p">((</span><span class="sa">f</span><span class="s2">&quot;Train RMSE: </span><span class="si">{</span><span class="n">train_rmse</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="se">\n</span><span class="s2">&quot;</span> <span class="sa">f</span><span class="s2">&quot;Test RMSE: </span><span class="si">{</span><span class="n">test_rmse</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">))</span>
 
@@ -2318,6 +2838,13 @@ <h4><span class="section-number">26.5.4.1. </span>Multioutput model<a class="hea
 </pre></div>
 </div>
 </div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Train RMSE: 389.12
+Test RMSE: 582.33
+</pre></div>
+</div>
+<img alt="../_images/time-series_37_1.png" src="../_images/time-series_37_1.png" />
+</div>
 </div>
 </section>
 <section id="id2">
@@ -2325,7 +2852,7 @@ <h4><span class="section-number">26.5.4.2. </span>Direct strategy<a class="heade
 <p>XGBoost can’t produce multiple outputs for regression tasks. But by applying the Direct reduction strategy, we can still use it to produce multi-step forecasts. This is as easy as wrapping it with scikit-learn’s <span class="math notranslate nohighlight">\(MultiOutputRegressor\)</span>.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.multioutput</span> <span class="kn">import</span> <span class="n">MultiOutputRegressor</span>
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.multioutput</span> <span class="kn">import</span> <span class="n">MultiOutputRegressor</span>
 
 <span class="n">model</span> <span class="o">=</span> <span class="n">MultiOutputRegressor</span><span class="p">(</span><span class="n">XGBRegressor</span><span class="p">())</span>
 <span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
@@ -2339,7 +2866,7 @@ <h4><span class="section-number">26.5.4.2. </span>Direct strategy<a class="heade
 <p>XGBoost here is clearly overfitting on the training set. But on the test set it seems it was able to capture some of the dynamics of the flu season better than the linear regression model. It would likely do even better with some hyperparameter tuning.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">train_rmse</span> <span class="o">=</span> <span class="n">mean_squared_error</span><span class="p">(</span><span class="n">y_train</span><span class="p">,</span> <span class="n">y_fit</span><span class="p">,</span> <span class="n">squared</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">train_rmse</span> <span class="o">=</span> <span class="n">mean_squared_error</span><span class="p">(</span><span class="n">y_train</span><span class="p">,</span> <span class="n">y_fit</span><span class="p">,</span> <span class="n">squared</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
 <span class="n">test_rmse</span> <span class="o">=</span> <span class="n">mean_squared_error</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">,</span> <span class="n">squared</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
 <span class="nb">print</span><span class="p">((</span><span class="sa">f</span><span class="s2">&quot;Train RMSE: </span><span class="si">{</span><span class="n">train_rmse</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="se">\n</span><span class="s2">&quot;</span> <span class="sa">f</span><span class="s2">&quot;Test RMSE: </span><span class="si">{</span><span class="n">test_rmse</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">))</span>
 
@@ -2354,6 +2881,13 @@ <h4><span class="section-number">26.5.4.2. </span>Direct strategy<a class="heade
 </pre></div>
 </div>
 </div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Train RMSE: 1.22
+Test RMSE: 526.45
+</pre></div>
+</div>
+<img alt="../_images/time-series_41_1.png" src="../_images/time-series_41_1.png" />
+</div>
 </div>
 <p>To use the DirRec strategy, you would only need to replace <span class="math notranslate nohighlight">\(MultiOutputRegressor\)</span> with another scikit-learn wrapper, <span class="math notranslate nohighlight">\(RegressorChain\)</span>. The Recursive strategy we would need to code ourselves.</p>
 </section>
@@ -2366,8 +2900,7 @@ <h2><span class="section-number">26.6. </span>Your turn! 🚀<a class="headerlin
 <section id="acknowledgments">
 <h2><span class="section-number">26.7. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to <a class="reference external" href="https://www.kaggle.com/">kaggle</a> for creating the open-source course <a class="reference external" href="https://www.kaggle.com/learn/time-series">Time Series</a>. It inspires the majority of the content in this chapter.</p>
-<hr class="docutils" />
-<span class="target" id="id3"></span></section>
+</section>
 </section>
 
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-    <link rel="stylesheet" type="text/css" href="_static/style.css" />
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+    <link rel="stylesheet" type="text/css" href="_static/style.css" />
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     <link rel="stylesheet" type="text/css" href="_static/sphinx-thebe.css" />
     <link rel="stylesheet" type="text/css" href="_static/drawio.css" />
-    <link rel="stylesheet" type="text/css" href="_static/style.css" />
     <link rel="stylesheet" type="text/css" href="_static/youtube.css" />
+    <link rel="stylesheet" type="text/css" href="_static/style.css" />
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+++ b/machine-learning-productionization/data-engineering.html
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     <link rel="stylesheet" type="text/css" href="../_static/mystnb.css" />
     <link rel="stylesheet" type="text/css" href="../_static/sphinx-thebe.css" />
     <link rel="stylesheet" type="text/css" href="../_static/drawio.css" />
-    <link rel="stylesheet" type="text/css" href="../_static/style.css" />
     <link rel="stylesheet" type="text/css" href="../_static/youtube.css" />
+    <link rel="stylesheet" type="text/css" href="../_static/style.css" />
     <link rel="stylesheet" type="text/css" href="../_static/design-style.b7bb847fb20b106c3d81b95245e65545.min.css" />
     
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diff --git a/machine-learning-productionization/model-deployment.html b/machine-learning-productionization/model-deployment.html
index 0040ad16ac..e0f82f9c9a 100644
--- a/machine-learning-productionization/model-deployment.html
+++ b/machine-learning-productionization/model-deployment.html
@@ -27,8 +27,8 @@
     <link rel="stylesheet" type="text/css" href="../_static/mystnb.css" />
     <link rel="stylesheet" type="text/css" href="../_static/sphinx-thebe.css" />
     <link rel="stylesheet" type="text/css" href="../_static/drawio.css" />
-    <link rel="stylesheet" type="text/css" href="../_static/style.css" />
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+    <link rel="stylesheet" type="text/css" href="../_static/style.css" />
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"}}]}}, 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DuSxgtQL5PKoBLL75rZj73/8MvbbO7CqJqARks4uIxWJQlAiGh4eg6zqAwt0+brcbmqYZO9NWV9eYWCmVAwYWWr18Ht73HoLjZ4ch6Es7Ri5fcwbgOPwT1PSomPnJz6FXec2rk4hKws9+9ggGBvqMr1taWiFJYfj9QTidTvMKo7LEwEKr5vzxD+F85OGTPi7k87AqCtyf/2cs3MEeUkRbyfT0MchyBLt374HHU1h/EgyGMDsbhyiGIYoSvN5qk6ukcsbAQqvm+vL/gW6xQtDyJ/0eQcvD/bWvYOGjHwPs9g2sjog22sJCEtGoCkWJYHJyEgDgdruxd+95AIBQSIQoSlyXQuuCgYVWR9dhf+43pwwryyzTx2Dt70N+qacHEW0euVwOfX0xyLKMoaEBaFrh8rDVakVnpw+NjU3G9y6vTyFaDwwstDq6Dmhn0Ol0aW0LEW0u+Xwejz/+qLFnSnPzNohiGIFAEG632+TqaDNjYKHVsViQDwRh7YlCWFrpfzK6y4X80q6URFS+ZmbiUBQZ8Xgcb3rT1QAAp9OJHTt2wW63QxQlo88PUbExsNCqpd77AVT+7d+c8nt0qxXpG28BuOkTUVlKpVLo6YlCUSIYHx8zxuPxOGprC+Hk4osvNas82sIYWGjV0jfeAvdXv1w4y5J/7VoW3WqFXlWFhf/rIxtfHBGdlbGxURw58jz6+/uQX5rfFosF7e2dkCTuPEvmY2Ch1ausxMz3f4zqQzfC/vzvodtsQC4HYWnzOK2lFbMPfA9ap8/sSonoNHRdRz6fh81W+DOQTCYRi/UAABoaGiGKEkIhERUVHjPLJDIwsNAZ0ZubMXP4cdifeRrOB78D6+QEHA11mL/qGqQO/Alg468UUSlLJBJLW+RHEAyKuOCCfQAAn68L5513PoLBEOrrG0yukui1+NeFzpwgILv/YmT3XwybzQJHrQfZeBLIncFdRES0YTKZDGKxKGQ5gtHREWO8tzdmBBabzYZ9+/abVSLRaTGwEBFtYk888RgURTZuQxYEAW1t7RDFMPx+v8nVEa0eAwsR0Sah6zri8WnU1tYZu8tqmo5cLofa2jpIUhihEBfQUnliYCEiKnPz8/NQVQWKEsH09DFcd90NaGpqBgDs3Xsedu7chcbGJm6RT2WNgYWIqAxls4uIxWJQVRlDQ4PQlzZ0tNlsmJ4+ZgSW5b1TiModAwsRUZmZnj6Ghx76HrLZrDHW0tIKUZQQCITgdDpNrI6oOBhYiIhK3PT0MczNzcHn6wIA1NTUwuFwoKKiAqIYhihK8HqrzS2SqMgYWIiISlAqtWCsS5mcnITH48E73/kXsFgssFgseNvbrkdlZSXXpdCWwcBCRFQistks+vv7oCgRDA4OQFvqkG6xWNDY2IRMJg23uwIAUFVVZWapRBuOgYWIqET89rfP4MiRF4yvm5qaIYphBIMhuN1u8wojKgEMLEREJpiZiUNVZXR2dqG5eRsAIBAQ0dPTY+yXwjt8iF7BwEJEtEHS6TSiURWKEsH4+BgAYH4+aQSWpqYmvPOd7+K6FKITYGAhIioiXdfR19cLRYmgv78P+XweQGGL/I4On3Hnz/IYEZ0YAwsRUZE9/fR/IpGYBQA0NDRCFCWEQiIqKjwmV0ZUPhhYiE4mmYTzZz+FZXICmrcaiwffBL2hweyqqIQlEgmoqozBwX68+923AiicNdm9ew/m5uYgSWHU1/N3iGgtGFiIXi2fR8U//k+4/88XYEkmoVssEDQNus2G9NtvRPJ/fg56JW8ppYLFxUXEYlHIcgQjI8MAAItFQG9vLxoaWgEA55yzx8wSiTYFBhai4+k6qm77IJwPfQ/CUm8WYWkvDCGXg+t734bt5Zcw88OfAh6ezt/K4vE4nnvuWfT19SKXywEonE1pa2vH9u1hdHZ2YmEhZ3KVRJsHAwvRcRyHH4br+9896eNCPg/bH/+Aii/+CxY++rENrIzMpus6crkc7Ha78XU0qgIAamvrIElhBIMiqqqqYLNZ4HQ6GViI1hEDC9Fx3F/+InSrFcLSnRwnImga3Pd/CQt//VHAxim02SWT81CUwhb5dXV1uPLKqwAAdXV1uOii16OtrR2NjU28w4eoyPhuS7RM12H/9VOnDCvLLJMTsPb1Ih8MbUBhtNGy2UX09hZuRR4aGoS+dHkwmZxHLpeDbSmonnvu68wsk6gsZLNZ9PXFoCgybrrpOlitrjU9z4YHlmQyiauvvhrj4+N48MEHcc4552x0CUQnt4qwYlhcLF4dZJrf/vZZHDnyPLLZrDHW0tIKUZTg9weNsEJEJ6frOkZGhqEoMmKxKBaX3i+z2SxcrjIJLF/4wheMjZOISoogIN/VDWtfr7Hg9mR0uwNaR8cGFUbFND09jaqqStjtDgCAzWZHNptFdXU1RDEMUZTg9VabXCVReYjH41CUCBRFxvz8nDHu9VZDFEW4XGvvibWhgaWnpwf/9m//ho997GP41Kc+tZEvTbQq6Xe/D55P/fdTfo9utSJ93Q3Qq7wbVBWtt1RqAdGoClmOYHJyAgcOXAlJCgMAwuEwtm3bhm3bWrguhWgVUqmU0XJiYmLcGHc6nQgEggiFJLS0tEIQBFgsljW/zoYGls985jO46aab0N3dvZEvS7Rq6VsOwf2V+2AZGjzhWhbdYoXuciH117ebUB2djVwuZ2yRPzg4AG3pdnWLxWLsQgsAbncF3O4Ks8okKgu5XA79/X1QlAgGBvpXzKeOjk6IYhhdXd3regl1wwLL4cOHoSgK7rnnHrz00kvr8pxW69qTGq2P5WOwaY5FbQ3m/uMwqm54G6yRo8YdQ8bmcXW1mP/WgxAkqeRWrG+6Y7GOMpkMHnjg60in08ZYS8s241Zkt3vtp6lPhMeidPBYrB9d1zE2NgZZjqCnR10xn7Zta4YkbUcwGEJFxckD/9mctNyQ99xUKoW7774bt99+OyorK9fteb3e9X2TobXbVMeiVgL++CLwyCMQvvlNYHgYQm0t8Pa3w3L99fCuccHYRtlUx2KN4vE4RkZGsHPnzqURDzo6WjE7O4vt27dj586dqKurK3odPBalg8di7WZmZvDyyy/j6NGjmJmZAVAIHo2NdcZ8qq+vL3odGxJY7r33XtTX1+O6665b1+dNJFLI57V1fU46M1arBV6ve3Mei/1vKPzveKk8kEqaU89pbOpjsQrpdNpYlzI2NgqLxYKamkajweAllxyA2+021qXE48U7jlv9WJQSHou1yWQyxnwaHR0xxu12OwKBIERRQltbu7EmZbXzqbraveZ1LEUPLMPDw7j//vvx+c9/HnNzhRXDCwsLxv8nk0l41rjFeT6vIZfjL2Ap4LEoHVvpWOTzeQwM9ENRIujv7zPuQBQEAa2t7Ugm03A4Cp+sHQ4X8nkdwKnvAFvf+rbOsSh1PBanl8/nMTjYD0VR0N+/suVEe3sHQiEJfr/fuKNO02CsXVmt09yAeUpFDyxDQ0PIZrP4wAc+8JrHDh06hD179uC73z35VuhERCcjy0fxxBOPG1/X1zdAFCWEQiI8nvW7/Ey0Wem6jsnJCSiKjGhURSq1YDxWV1cPUQwjFBLXdTnHWhU9sGzfvh3f+MY3VowdPXoUn/3sZ3HXXXdx4zgiWpVEIgFVlVFdXYPg0g7Dfn8Qv//97+D3ByCKEhoaGk2ukqg8zM3NQVVlKIqMeHzaGK+oqEAoJEIUw6ivbyipW/uLHli8Xi/27dt3wsd27tx53KI4IqKVFhcXEYtFIcsRjIwMAwCam7cZgcXlcuGWWw6V1JsqUakqzKceKEphPi23nLDZbOjq6oYohtHR0XlWe6UUU6ndmUlEhMHBAcjyUfT2xlZcR29tbYMohqHruhFSGFaITk7TNAwNDUJVZcRiMeRyr7ScaG1tgySF0d0dgNPpNLHK1TElsOzbtw+yLJvx0kRUBl588Qj6+/sAALW1tUvX0SVUVVWZWxhRmTh2bAqyHEE0qiCZfOUOnpqaGmM+eb3ltVs3z7AQkWmSyXmoqgJVVfCmN73ZeAPduXPXUu8RCY2NTTyLQrQKCwtJqKoCRZExNTVpjLtcLgQCIUjSdjQ1le98YmAhog2VzS6it7ewRf7Q0KBxHV1VZbzudRcAAHy+bvh8bOFBdDrZbBZ9fTEoiozBwQFjPlmtVvh8XRDFMDo7fbBarSZXevYYWIhoQyST83j22WcQi0WRzb5yHb2lpRWiKMHvD5pYHVH50HUdIyPDUBQZsVgUi4uLxmPbtrVAFCUEAiG4SnxX7jPFwEJERbO4uAiHo7DJlMPhMMLK8uUeUZRQXV1jbpFEZSIej0NRIlAUGfPzc8Z4VZUXkiQhFJJQU1NrYoXFxcBCROsqlVowtvTO5/O44YZ3QBAE2O0OXHLJG1BdXY1t21rK9jo60UZKpVKIRlUoSgQTE+PGuNPpRCAQRCgkoaWldUvMJwYWIjprp2o1PzeXgNdbDQAIh7ebWSZRWVieT6oqo7+/b8V86ujohCiG4fN1wW63m1zpxmJgIaKzcvToS/j1r59CJpMxxhobmyBJYQSDIbjdJ281T0QFuq5jfHwMihJBT08U6XTaeKyxsdHYIn8rzycGFiI6I7OzM7DZbEavnooKDzKZDCorK439Herq6kyukqg8JBKzUBQZihLB7OysMV5ZWYlQqLDOq66u3sQKSwcDCxGdVjqdRk+PCkWRMTY2inPPfR0uuuj1AICOjk786Z++Da2tbVviOjrR2cpkMsZ8Gh0dMcbtdvtSX6wwWlvbSnaLfLMwsBDRCeXzeQwM9ENRZPT39yKfzwMobIW/sPBKR1eLxYK2tnazyiQqC/l8HkNDA5Dlwnw6vuVEe3sHRDGM7u5u2O0OkystXQwsRPQauq7je9/7FuLxuDFWX98AUZQQConG5SAiOjld1zE5OWHs5pxKvRL06+rqjXUplZWcT6vBwEJEmJubQ19fDLt27YYgCEuNBtuRyWSM6+gNDY1ml0lUFubn56GqMmQ5gnh82hh3uysQConGfOIl1DPDwEK0RZ2o1XxDQyNaWloBAPv27ccll1zG6+hEq5DNLiIWi0FRIhgeHjK2yLfZbOjq6oYohtHR0cn5dBYYWIi2kOVW84oSQW9v74pW869eh1IO7eaJzKRpGoaHh6AoEcRisRXzqaWlFZIUht8f5FxaJwwsRFvI2NgofvKTHxlf19bWIhSSyrLVPJFZpqePQZYjUFUZyWTSGK+pqVm6hBrmfCoCBhaiTSqZnIeqKgCAvXvPA1D41NfY2ITm5m0QxXBZt5on2kgLC0moqgJFkTE1NWmMu1wuBAIhSFIYTU3NnE9FxMBCtIlks1koioyjR48areZdLjfOOWcPrFYrBEHAddfdwDdVolXIZrPo7++FLMsYGhowtsi3Wq3w+boQCkno7PTBZuOf0o3A/8pEm8DY2Chk+WWMjAwgkUhC0woL/pZbzS8vAATAsEJ0CrquY2RkGKqqoKdHxeLiovHY8pnJQCAIt9ttYpVbEwMLUZnSdd0IH729MUQiR+F2O+D1ViMYLNw6WV1dY26RRGUiHo9DVWUoioy5uYQxXlXlhSgWbu2vqak1sUJiYCEqI6nUAqJRFbIcwYUXXoTOTh8AQJLCyOezuPDC8+B2VyOf10/zTESUSqUQjapQVRnj42PGuMPhQCAQQigksuVECWFgISpxy63mFSWCgYF+4zq6okSMwFJXV483vvEAams9iMeTABhYiE4kl8sttZwozKfllhMWiwXt7Z2QJAk+XzfsdrvJldKrMbAQlahcLoennvoVenqiyGQyxnhjYxMkKYxgMGRidUTlQ9d1jI6O4ujRo+jpUZFOp43HGhsbIYqF+VRR4TGxSjodBhaiEpJOp+FyuQAU7kQYGxtDJpM5rtV8GHV1dSZXSVQeEolZ9PSoGByMYXR0wliM7vF4EApJkKQw6urqTa6SVouBhchk6XQasVgUiiLj2LEpHDr0HtjtdgiCgIsuej2sVitbzROtUiaTQSwWhSxHMDo6AotFgNvtgM1mR3e3H6IY5nwqUwwsRCbI5/MYHOw3Ws0vX0cXBAHj42Nob+8AAPh8XSZWSVQe8vk8hoYGjPmUy+UAFOZTe3sHLrjgXDQ2tkIQ+CevnPHoEW2wgYF+PProz5FOp4yxurp6SFKh1bzHw1bzRKej6zqmpiahKDJUVUEqtWA8VltbtzSfJNTUeI3F6LmcZmLFdLYYWIiKbG5uDtls1lh7UlNTg3Q6hYqK5VbzYdTXN/DWSaJVmJ+fX9oiP4Lp6WPGuNu9PJ8kNDQ0cj5tQgwsREWwuLiIWKwHihLByMgwfL4uXH31NQAAr7cab3vb29HU1Mzr6ESrkM0uIhaLQVEiGB4eMnZuttls6OrqhihKaG/vhNVqNblSKiYGFqJ1omkahoYGoSgR9Pb2rmg1n8/noWmaEVC2bWsxq0yisqBpGoaHh6CqMnp6elbMp5aWVkhSGH5/EE6n08QqaSMxsBCtk8OHf4L+/j7j69raWoRCEkIhia3miVZpevoYZDkCVZWRTCaN8ZqamqVb+yV4vdUmVkhmYWAhWoNksnAdPRzeYeyb0tHhw/j4OILBEEQxjKamJl5HJ1qFhYUkVFWBqsqYnJw0xl0uFwKBECQpjKamZs6nLY6BhWiVstks+vpiUBQZg4MD0HUdDocDO3bsAgCEw9uxY8dOXkcnWoVsNov+/l7IsoyhoQGj5YTVakVnpw+iGEZnpw82G/9MUQF/E2jryOchJGahO11ARcWqfmS51byiyIjFoitazW/b1oKK456HvUeITq2wRf4IFEVGT4+6Yj41N2+DKIYRCAThdrtNrJJKFQMLbXqWkWG477sXrm9+DZaltvHZ885H6v0fQuZtbwdOcadOKrWA//iPfzfuSvB6q41W89XVNRtRPlHZm5mJQ1FkKIqMuaU5CABVVV5jPtXU1JpYIZUDBhba1GwvHkH1n78VwvwchKXdZAHA9sLv4f3L9yH9s8OY+8KXAKvVaDU/OzuDSy65DABQUeFBMBiC3W6HKIaxbVsLr6MTrUIqlUJPTxSKEsH4+Jgx7nA4EAiEIIoSWlpaOZ9o1RhYaPNKJlF949teE1YAQFi+Xv7v38dIczOO7L8YAwP90DQNgiBgz55zUVVVBQA4ePBNG146UTnK5/MYGOiHokTQ399ntJywWCxob++EJEnw+bp5+ZTWhIGFNi3XQ9+DMDWFE31+m7RY8ILdDsVuR+r738VCUzNgtRqt5h0Ox4bXS1SOdF3HxMQ4ZDmCnh4V6XTaeGx5PgWDIVRUeEyskjYDBhbatJwPfgcQBGBp/YkOGOFl3GLBH5ZCSdXCAnZarfDfeDNbzROtUiIxu9THR8bMzIwx7vF4jP1S6usbzCuQNh0GFtq0LFOTyOg6FLsdL9tsCOZyOD9b2C0zlMthOJtFOJtFRz6P+bp6ZBhWiE4pk8kgFotCliMYHR0xxm02O/x+P0QxjLa2dracoKIoemB54okn8KUvfQnRaBTz8/Nobm7GwYMHcdtttxlrBIjWUz6fx+BgP/7T7UZ/VRWWV69kBcEILE4Abzru1LW+1JiQiFbSNA2DgwNQlAj6+nqRy+UAAIIgoK2tHaIYht/vh93Oy6hUXEUPLDMzM9i9ezduvfVW1NTUQFVV3HPPPVBVFffff3+xX562mF//+ilEIkeRTqdgC4Tg6I2hIZ/HzlwO4Wz2hD+jeauxeOkbN7ZQohKm6zqmpiaXLvkoSKUWjMdqa+sgSWEEgyI/dNKGKnpgufbaa1d8vW/fPjgcDnziE5/A+Pg4mpubi10CbWILC8kVi/kSiQTS6RQqKioQuuEm7PvlY2hKLsCi5U/487ogIPX+DwFL2+sTbWXz84WWE4oSwfT0MWPc7a5AKCQiFBLR2MiWE2QOU9aw1NTUAChszUx0phYXFxGL9UBVZQwPD+HGG29BbW1h06lzzz0P4fB2dHR0wmKxwPatB4Hrr4W+sLDi1mbdYgE0DYtXX4OFj37MrH8VItNls4uIxWJQVRlDQ4PGJok2mw0+XzckSUJ7eydbTpDpNiyw5PN55HI5RKNRfP7zn8eBAwfQ3t6+US9PZW651byiRBCLxVa0mh8dHTYCS1PTyjN2ufPOR/yxp+D+P5+H+9++CWGhcGo7H96B1Af+Eukbbwb4RkxbjKZpSy0nIojFelZ8eGxpaYUoSggEQnA6nSZWSbSSoC/H6SK77LLLMD4+DgC49NJL8c///M8r+rCsRSKRQj6vrUd5tEZWqwVer7uox2Jqago//vEPX9NqXpK2L7Wa967uiRYXIRw7Bric0GtqC7c8byIbcSxodUr1WBw7dgyKEoGiyJifnzfGq6trIIoSJCmM6upqEytcf6V6LLaq6mr3mu8i27DAEolElrY+j+Lee+9Fe3s7vvrVr/I0I71GMpnE7OwsWltbAQC5XA5f/OIXIQgCJEnCjh070NLCLfKJVmNhYQFHjx7Fyy+/jImJCWPc6XQa86m1lVvkU+nbsMByvEgkgmuvvRb/9E//hKuuumrNz8PEbL71+vSSzWbR19cLWY5gcHAAVVVe3HLLrcab6NTUJGpr6xhwT4GfJEuH2cdieT4pimy0nAAKW+T7fF2QpDB8vi7YbJt/Ky6zjwWtdDZnWEz5bZUkCXa7HQMDA2f1PPm8hlyOv4ClYC3HQtf1pevoMmKx6IpW8w6HE/PzC0ab+Zqaeug6eLxXgfOidGzksdB1HaOjI1AUGT096or51NTUbGyRvzyngK01nzgvSsPZnCIxJbAcOXIE2WyWi263uKef/k/84Q8vGF9XVXkhSRJCIbaaJ1qtmZk4VFWGLMuYm0sY45WVVZCkMEIhyViUTlTOih5YbrvtNuzatQuSJMHlciESieArX/kKJEnCwYMHi/3yVCIK65dUtLW1Gf16urq6EYm8zFbzRGconU4jGlWhKBGMj48Z4w6HA35/EKIoobW1jfOJNpWiB5bdu3fj4Ycfxn333Qdd19HW1obrr78e733ve9kRd5PL5XIrWs1rmobdu/fi4osvBQC0trbh0KH3sNU80Srk8/kV8ym/tK+QIAjo6OiEKEro6vJzPtGmVfTA8oEPfAAf+MAHiv0yVCJ0Xcf4+BgUJYKenuhrWs3X17/SYFAQBL65Ep2CruuYmBiHosiIRlWk0ynjsYaGRoiihGAwBI+n0sQqiTbG5l8iThtK13U88sjDWFjaoG251bwkhY1LQUR0aolEAqoqQ1EimJmZMcY9Hg+CQRGSFEZ9fYN5BRKZgIGF1iyTyUCWe3Ds2BguueQAgMJtkzt27MTc3BxEMYzW1ja2midahUwmg1gsCkWRMTIybIzbbHb4/X6IYhhtbe2cT7RlMbDQGcnn8xgaGoAsy+jv74Wm5eF2O9DdLaKlpXDX1wUXXGRylUTlQdM0DA4OQFEi6OvrRS6XA1C4XNra2gZRDMPvD3C9HxEYWGiVEolZvPjiH17Tar6xsQHnn3/uirUpRHRyuq5jamoSqipDVRXj8ikA1NbWQZLCCAZFVFVVmVglUelhYKGT0nXduC0ylUobe6Yst5oXRQnbtjWjrq4S8XiSmzIRncL8/DxUVYGiRDA9fcwYd7ncxnxqbGzirchEJ8HAQisst5pXlAi8Xi/e8IbC2pSmpibs3r0X7e0daG/vMLbI55sr0clls4tQ1R4oSgRDQ4NY7oRis9ng83VDkiS0t3ey5QTRKjCwEDRNw/Dw0FKr+RhyuUKreafTiUsueQOsVisEQTD2TyGikyusSxnCM8/04Q9/eAmZzCtb5Le0tEIUJQQCITidThOrJCo/DCxb3JEjz+PIkeeRTCaNsZqaGohiGKGQyE9+RKs0PX0MiiJDVWUsLCThdjuQzWZRXV0NUQxDFCV4vdVml0lUthhYtpiFhSScTpcRRBYXM0gmk3C5XAgEQpCkMJqamnmph2gVUqkFY13K5OSkMe52u7B79zloa+tGQwPXpRCtBwaWLSCbzaK/vxeyLGNoaABXXnkV/P4AACAc3on6+kZ0dvq2RKt5orOVy+XQ19cLRYlgcHAAmlZYbG6xWODzdSEUkhAI+NHYWM3F6ETriH+hNild1zEyMgxVVV7Tan5iYtwILFVVVbx9kug0dF3H6OgIVFVGT08UmUzGeKypqRmiGEYwGILb7QYA2Gzc3I1ovTGwbELpdBoPPvidFa3mq6q8kCQJoZCEmhq2midajdnZGShKBIqiIJGYNcYrK6sgihJEMYzaWs4noo3AwLIJpFIpTE5OoLPTBwBwuVxwuVzIZNIIBEIQRQktLa28jk60Cul0Gj09KhRFxtjYqDFut9uN+dTa2sb5RLQKmUwGPT0q+vp68aY3vfmsnouBpUzlcjmj1fzAQD8EQcChQ+8xbpU8ePBP4PFUshsy0Srk83ljPvX39yGfzwMo7DPU0dEJUZTQ1eXnfCJahXw+j8HBfiiKgv7+V1pODA72o7HxnDU/LwNLGdF1HePjY1AUGT09KtLptPFYY2Mjksl5I7Dwsg/Rqem6jomJCShKBNGoinQ6ZTxWX98AUZQQConweCpNrJKofCQSCfzhDy8gGlVXtHCpq6uHKBbuQD0bDCxl5KWX/ognn/yl8bXH40EoJEGSwqirYy8fotVIJBKIRgu3IsfjcWO8oqICoZAEUZTQ0NBoYoVE5UPTNKODeC6XxYsvHgFQaOEiiiJEMYz6+oZ1uYTKwFKillvNV1ZWoaOjEwDQ3d2NZ599Gt3dhVbzra1tbDVPtAqLi4uIxaKQ5QhGRoaNcZvNju7ubohiGO3tHZxPRKtQmE+FlhMejwdXXPEnAApnUvbuPQ+trW3o6Ohc9/nEwFJC8vk8hoYGIMuycd2vo6PTCCweTyXe9a73cr8UolXQNA1DQ4NQlAh6e2PGdXRBENDa2gZRlOD3B+FwOEyulKj0nayFi81mx2WXZY31Xfv3X1y0GviXrwRMTk4sbemtrLjuV1tbh/b2jhVdkxlWiE5tamoSihKBqipYWDh+PtUutZyQuPcQ0Rn4wx9ewAsv/P4kLVykDVuMzr9+JeDpp//TOE3tdlcYreYbGhp56yTRKiST80tb5Ms4dmzKGHe53AiFRIRCEpqauEU+0WosLCRhtzuMIJLP540WLsFg4e+TGS1cGFg2UDa7iFgsBlWVccUVV8LtrgAAbN++ExUVFRBFtponWq1sdhG9vYUt8oeGBqHrOgDAarXC5+uGJEno6PBxPhGtQjabRV9fDIoiY3BwAFdccSVCIQkAIIqFDUc7O82dTwwsRaZpGkZGhpeu+/Ugmy1c91NVBbt37wWApR0zJROrJCoPyy0nZDmCWCxqzCcAaGlpRSgkIhAIweVymVglUXlYnk+KIiMWi65o4TI5OWEEFo+nEt3d5t/ez8BSJKnUAl544Xmoqrziut9yq/muLr+J1RGVl+np6aV1KTLm5+eNca+32gj81dU15hVIVGay2UV85zvfWtHCpTCfxJJt4cLAso6Ovx8dEPDii0eQz+fhcrkQCIQgSWFTrvsRlaNUagHRqApZjmBycsIYdzqdCASCEMUwtm1r4XwiWoVUKoWJiTH4fN0AALvdgcrKSiwuZspmPjGwnKVsNov+/l7IsoxsdhF/9mfXAQDcbjcuuGAfqqtr0Nnp4909RKuQy+XQ11dYlzI4OABN0wAAFosFnZ0+iGIYPl8X5xPRKuRyOfT39xktXADg0KH3GF3FDxw4CLe7omxaTnDWr8Fyq/nlLfKPv+43Nzdn3DJ57rmvM6tEorKh6zrGxkahKBH09ESRyWSMx5qamiGKEoLBkLFInYhO7pUWLoX5dKIWLsuBxeutNqvMNWFgOUOqKuPZZ59Zcd2vqsq71HeE+zsQrdbs7AwUJQJFUZBIzBrjlZVVS7f2h1FXV2dihUTlR5aP4vHHHzW+rqysNFpOlHsLFwaW00ilUhAEwbjrQBAEzM0l4HA44PcH2Wqe6Ayk02n09KhQFBljY6PGuN1uh98fhCSFOZ+IVimTyaCnR0VFRYVxI4fP1w2n04muru5N18KFgeUEcrmc0Wp+YKAf559/Ic4773wAhV+GK698E3y+7rK57kdkpnw+vzSfCi0n8vk8gEL47+johChK6Orycz4RrcKJWri0tLQagcXtduNd73rvptx/iIFlSaHV/DhkOYKeHnXFdb/JyUnjn+12O4JB0YwSicpGYT5NQFEiiEZVpNMp47H6+oalS6giPB7z93YgKgeTkxOQ5cJ8Or6FS11dPXy+7hUtXDZjWAEYWAAU3ly///3vrAgmHo/HuO5XX99gYnVE5WNubg6qKkNRIojH48Z4RUWFMZ8aGhpNrJCoPP3mN88Yd/q43RXGfilbqYXLlgwsmUwGg4MDCAZDAAqnpuvq6hGPz8Dv90MUw2hra9801/2IiqnQaj4KWY4YPbGAQhfX7u7CdfT29g7OJ6JVWG7hoigRvPGNVxg3coTDO+BwOCCKYXR0dG7J+bRlAoumaRgcHICiRNDXV7juV1NTY3za27dvPy699A2w29lqnuh0NE3D0NAgFCWC3t5eo9U8ALS1tUMUJfj9QTgcnE9Ep6NpGoaHh5ZauMSM+RSNKsb2GIFAEIFA0MwyTbepA4uu60ut5mVEo69uNV+3Yp0Kr6UTnV5hPkWgqq+eT7VGq3ne2k+0OqlUCi+88PvXtHCpqamBKIYRCIRMrK70bOrAMjIyjB/96AfG1253BYLBEERRQmMjW80TrUYyOQ9VVaAoMo4dmzLGXS43QqHCdfSmJs4notXI5/PGolir1Yo//vFF5HJZtnBZhU0TWJav++m6jnB4OwBg27YWVFZWobl5GyRJQnt756ZdPU20nrLZRfT29kJVC63mdV0HUHiD9fm6IUkSOjrMbTVPVC6Ob+GSSi3g7W+/EQDgcDiwb99FqKryorOT8+l0yjqwHH8dPRbrQTabRWVlJSQpDEEQYLVacfPNt/KXgGgVllvNy3IEsVgU2ewr61K2bWuBKEoIBELGJopEdHLL80lRZMRi0RUtXGZm4kY35N2795pUYfkp28CSSqXwzW9+DYnEnDFWXV0NUQwjl8sZm1AxrBCd2vT09NKtyDLm51+ZT4VW84Vbkaura8wrkKjMRKMKnnnm169p4SJJhRYuy2GFzkzZBpZMJoP5+Xk4nc6ldSlhNDdv43U/olVIpRYQjaqQ5QgmJyeMcafTWTat5olKRSpV2BhxuamgzWYzWrgEAoV1ky0trZxPZ6lsA4vdbsdVV70ZbW2dbDVPtAqvbjWvaRoAwGKxoLPTh1BIQldXN+cT0Sq8uoXL3r3n4cILLwIAdHT4cOWVV8Hn62LLiXVU9Hemn/70p/jRj36El156CYlEAj6fD7feeiuuu+66s0qbHo8HgUAQuZy2jtUSbS7Ht5qPRlVkMhnjscbGJkhSGMFgCG53hYlVEpWH4+dTT090xdYY09PHjH+2Wq3GxqS0fooeWL72ta+hra0Nd9xxB2pra/H000/jE5/4BMbGxnDbbbcV++WJtqTZ2RkoigxVlTE7O2uMV1ZWGvul1NXVmVghUXnRdR0PPfQ9TEyMG2OVlZVGy4m6unoTq9saih5Y7r333hVvjPv378fMzAy++tWv4r/8l/+yJbcXJiqGdDqNP/yhB7/97fMYHn5li3y73Q6/PwhJKrSa53V0otPLZDLo7+9DKCRCEAQIgoCGhgbE49Pw+wMQxcJ84t+wjVP0wHKiT3Hbt2/Hd7/7XSwsLKCykjvMEq1VPp/H4GA/ZFnG4GAfHA4rUqlFCIKAjo5OiGJhXQpbThCdXj6fx9DQAGRZRn9/oYWL1+vFtm0tAIALLtiH17/+Es4nk5iyuu53v/sdmpubzzqsWK1MtmZbPgY8FhtH13Wj1byqKsYdChaLgIaGZnR1FXqOsN2EeTgvSsfpjsVyCxdZjkBRFKRSr7ScaGioh6blYbMVftbrZduJs3U2J3g3PLA899xzePjhh/Gxj33srJ/L63WvQ0W0Hngsii+RSODo0aN4+eWXMT09bYw3NNQiHA5jx44daGpqMrFCejXOi9JxsmMxPDyM//iPh4yv6+trVswnXkItHRsaWMbGxnD77bdj3759OHTo0Fk/XyKRQj7Pu4TMZLVa4PW6eSyKZHFxEbFYFLIcwfDwsLFFvs1mW7qOLhmt5pc/QfJYmI/zonQcfyzS6TR6enqQy+Wwa9c5AAC3uxpOZwUaGhohSWF0dLzSwmVmZuFUT01rUF3tXvO6nw0LLIlEAu9///tRU1ODe+65Z10WKuXzGm9rLhE8Fuvn+JYTvb29Rqt5AGhtbYMkheH3B+FwOJa+H8aeKgCPRSnhsTCfpmno6+vDb37ze0SjPcjlsnC7KxAKhY1gcv31Nxt/k3QdPGZFtPSZa002JLCk02l88IMfxNzcHL7zne+w/TzRCRw7NgVZjiAaVU7Yaj4UkuD1ek2skKh8TE9PQ5aPoqdHgaZlkUotQtN01NTUIBSSoGmvdE3mnT7loeiBJZfL4SMf+QhisRgeeOABNDc3F/slicrGwkISqqpAUWRMTU0a4y6X22g5wevoRGcuEnkJR468AItFQG2tF4GAhGBQQlNTM+dTmSp6YLnrrrvw+OOP44477sD8/DxeeOEF47EdO3YYp7WJtopsNou+vhgURcbg4ICxLsVqtcLn64IohtlqnmiVstks+vt7Icsy9uzZi/b2DgCAKIaRSCSwfft27N27E3NzGV7qKXNFDyxPPfUUAODuu+9+zWOPPvoo2tvbi10CkelO1Wq+uXkbJCmMQCAEl8tlYpVE5UHXdYyOjkBRZPT0qMZ8crtdRmBpaGjEVVe9BTabZak/VuYUz0jloOiB5bHHHiv2SxCVrHg8DkWJQFFkzM/PGeNebzVEUWSreaIzkMvl8PvfPwdFkTE3lzDGq6q8EMXCFvm0ebEtK9E6S6VSiEZVKEpkRd8Rp9OJQCAIUQxj27YWXkcnWoVcLmd0ELdarYjFopibS8DhcCAQCEEUJbS0tHI+bQEMLETrIJfLob+/z2g1v3ybscViWdoiP4yurm7jjZeITi6Xy2FgoB+KEsHY2BhuueUQ7HY7BEHA+efvA6DD5+uG3W43u1TaQHz3JFqj41vNR6MqMplXrpE3NjZBksIIBkNwuytMrJKoPOi6jomJcchyBD09KtLptPHYyMgwfL4uAEAwGDKpQjIbAwvRGZqdnYGiyFBVGbOzs8b4K63mwyds+klEJzY8PIRf/epxzMzMGGMej2dpPkmor28wrzgqGQwsRKuQTqcRi0WhKDJGR0eMcbvdDr8/CFGU2GqeaJUymQwymYyxEaLH48HMzAxsNjv8fj9EMYy2tnbOJ1qBgYXoJPL5PAYH+yHLMgYG+pDL5QAAgiCgvb0DohhGd3c3W80TrUI+n8fQ0AAURUZfXy86O31405veDACoqanF1Ve/BW1t7ZxPdFIMLETH0XUdk5MTS5d8FKTTKeOxurr6pS3yRVRWVppYJVF50HUdU1OTxnxKpV5pJphIJKBpmnEWpavLb1aZVCYYWIgAzM3NQVVlKEoE8XjcGK+oqEAoJEIUw6ivb+Ctk0Rn4Be/eATRqGp8XWg6KCIUEtHYyJYTdGYYWGjLWlxcRCzWA0WJYGRk2Ngi32azoaurG6JYaDXP6+hEp5fNLiIWi6GrqxtOpxMAsG1bC/r6euHzdUOSJLS3d7LlBK0ZAwttKZqmYWhoEKoqIxaLIZfLGo+1trZBksLo7g4Yb7hEdHKapi21nIggFutBNpvF5ZdfgXB4BwBAkrZDFMOcT7QuGFhoSzh2bAqyHEE0qiCZTBrjNTU1S+tSJOOOBSI6tenpY5DlCFRVXjGfqqurIQivnJFkc1taTwwstGktLCShqgoURcbU1KQx7nK5EAiEIEnb0dTE6+hEZ2JhIYnvfvdbxiXUwnwqtJxobt7G+URFw8BCm0o2m0VfXwyKImNwcMB4U7VarfD5uiCKYXR2+ngdnWgVstks+vt7EY9P44ILLgIAVFR40NHhg9VqMeYTW07QRuBvGZU9XdeXrqPLiMWiRqt5oLDoTxQlBAIhuFwuE6skKg+6rmN0dASKIqOnR8Xi4iIEQcD27buM2/nf/OZreCaFNhwDC5WteDwOVZUhyxHMz88Z41VVXkiShFBIQk1NrYkVEpWPRGIWsnwUsixjbi5hjFdWVkGSwrBYXgkoDCtkBgYWKiupVArRqApFiWBiYtwYZ6t5orMzPDyE5577LYDCfDq+5QTnE5UCBhYqece3mu/v74OmaQAAi8WCjo5OiGIYPl8XW80TrUI+nzfmU1tbB3btOgcA4PcH0dfXi2AwhK4uP+cTlRwGFipJuq5jfHwMihJBT090Rav5xsZGY4t8t7vCxCqJyoOu65iYGIcsR9DToxrzaW5uzggsTqcTV199jZllEp0SAwuVlERiFopS2CJ/dnbWGK+srDRazdfV1ZtYIVF5ef753yMSeQkzMzPGmMfjMeYTUblgYCHTZTIZ9PSoUBQZo6MjxrjdboffH4AohtHa2sYt8olWIZtdXNHxeGJiDDMzM7DZ7PD7/RDFMNra2jmfqOwwsJApllvNy7KM/v5e5HI5AIW7D9ra2iGKYfj9fraaJ1oFTdMwODgARYmgr68PN9xwE6qrawAAu3fvhc/XDb8/wJ1nqawxsNCG0XUdk5MTUFXlNa3m6+rqjXUpy3s9ENHJ6bqOqalJKIqMaFTBwsIr86mvrxd79pwLAGhpaUVLS6tZZRKtGwYWKrq5uTlEowpkOYJ4fNoYX241L4oSGhoaeesk0SrF43H87Gc/xfT0MWPM5XIb86mxscnE6oiKg4GFimK51byiRDA8PGRskW+z2dDV1Q1RDKO9vYNb5BOtQja7iLm5OWPBeWVlJebn52Cz2eDzdUMUxaXt8jmfaPNiYKF1o2kahoeHllrNx5DLZY3HWlpaIUlh+P1BtponWgVN05ZaTkQQi/WgsrISN954CwRBgN1ux9VXX4P6+gbOJ9oyGFjorE1OTuI3v3kekcjRFa3ma2pqltalSPB6vSZWSFQ+pqePQVFkqKqM+fl5Y1zTNKRSC6io8AAAWlvbzCqRyBQMLLQmCwtJqKqCaFRBMjmLVGoRmqYvtZoPQZLCaGpq5roUojPwm988g9/97rfG106nE8FgCKIYRnPzNs4n2tIYWGjVllvNy7KMoaEBaJoGi0WAx+OC3x9AICCy1TzRKuVyOfT19aKhocFo0tnS0gqLxQKfrwuhkASfr4vziWgJZwKdkq7rS9fRZcRiUSwuLhqPNTdvw/bt23HBBXuRTmvI5TQTKyUqfbquY3R0BKoqo6cnikwmgz179uL1r78UANDW1o5Dh97NlhNEJ8DAQicUj8ehqjIUZWWr+aoqL0SxsKV3TU0tbDYL3G430unkKZ6NaGubnZ2BokSgKAoSieNbTlQZa1KAQkNPhhWiE2NgIUMqlUI0qkJVZYyPjxnjDocDgUAIoiihpaWV19GJzoCmafjBD75vbJRot9uN+dTa2sb5RLRKDCxbXC6XM1rNDwz0I5/PAyh80mtv74QkSfD5utlqnmgV8vk8Bgb6MTTUj7e97a0ACnNJFCXE49MQRQldXX7OJ6I1YGDZgk7Wah4AGhsbIYphBIOhFaeqiejECvNpAooSQTSqIp1OwWIRcP75e1FVVdjobf/+i3kmhegsMbBsIYnErLG/w4lazUtS2NhJk2grsVh64HZ/A1brUQB2ZLP7kU7fAl2vPenPLCwkcfToy1CUyIr5VFFRgXB4O7xeL5Y2eC7jsJKBzfY8BCEFTetAPh80uyDawhhYNrlMJoNYLApZjmB0dMQYt9nsCAQCEMUwWlvb2GqetqgcKiv/Bm73l6HrVgB5AAIcjh/D47kLc3P/LzKZd57wJxOJBH7zm2cAFOaT3+9HKCShvb0DDocNNTUexOPluhg9hYqKf4Tb/WVYLDPGaDZ7IZLJjyObvcK80mjLYmDZhPL5PIaGBqAoMvr6epHL5QAUPuW1tbVDFMPw+/2w29lqnra2ysq/gcv1FQCAIOSXRgunRXQ9A6/3v2Bmxolo9AKoqgy3uwIXX1y4Bbm5eRskKYzW1nb4/QE4HJtlPqVQU/OnsNl+C0FYuVWBzfYcqqv/HHNz9yKTudmk+mirYmDZJI5vNa+qinFHAgDU1tZBkgpb5FdWVppYJVHpsFqjcLu/fMLHdB2YnLTgpZfsOHr0TkxNfRiAAKfTiX379sNms0EQBBw4cOXGFr0BPJ7PnjCsAIAgaNB1oKrqw8hmL4am+UyokLYqBpYyNz8/D1WVIcsRxOPTxrjbXYFQSEQoJKKxsamMr6ETFYfL9XXouvW4MysFf/yjDb/7nQNTU8udj+dRWTmE7u5rEApJm7wjcgou11dOGFaWCUIh0LndX0Uy+XcbVxpteQwsZSibXUQsFoOiRDA8PAR9aWWfzWZDV1c3RFFCe3vnJn9jJTo7VuvLAPJYXARsNmB5GVc8bsHUlBUWi45AIIcdOzQ0NrZgcfEyU+vdCHb7s7BY5k77fYKQh8PxIwYW2lAMLGVC0zQMDw8tbendg1wuazzW0tIKSQrD7w+y1TzRKui6jsFBHarqgqra8OY3pxAMFs607NqVRXW1jlAoC7cb0HULksmtsW+KICyc/puM7y3XBcVUrooeWPr7+/GVr3wFR44cgaqq8Pv9+PGPf1zsl900pqePQZYjUFUZyeQrbxA1NTUIhQpb5Hu91SZWSFQ+pqenoSgRqKqCTKYCdrsdggD09dmMwFJbq6O29pUPBIKgIZvdb1bJG0rT2lb1fbpugaZ1FLkaopWKHlhUVcUTTzyBPXv2QNM04/IFndzCQhLRqApFiWByctIYd7lcCARCkKQwmpqauS6FaJUymQz+4z/+HZOTE8aY03k+9ux5FDt2pNDaeuI1G7puRS63C7nc6zaqVFPlcruRy4VhtcoQhFO9V2tIpf5io8oiArABgeXAgQM4ePAgAOCOO+7AH//4x2K/ZFnKZrPo7++FLMsYGhqAphXeQK1WKzo7fRDFMDo7fWw1T7QKuVwOx45Nobl5GwDA6XRC13VYLBZjPvl8XaisrEFV1V/hRJ+jCvuyODA/f8/GFm8qAcnk36K6+tBJv0PXrdC0NmQyf76BdRFtQGDhhmQnt9xqXlFk9PSoWFxcNB5rbt4GUQwjEAjC7XabWCVRedB1HWNjo1CUCHp6osjnNbzrXe8x9kd54xsPoLKyckU35HT6L6Drbng8d8JqHV8KKToEQUM+vx1zc19ALrfXnH8hkywu/hnm5++Gx/NxABbjLipdFwDo0LRWzMz8BwB2laaNVdYf163W8gxDMzNxyHIEiiIjkUgY4zU11RDFwhb5NTUn3xK8lCwfg3I9FpvJVj0Ws7MzkOUIZFlGIjFrjFdVVSGZTKCiogkA0NKy7YQ/n8+/A4nE9bDbfwaLpbA1fy73euTzrwMgYC0nNcv9WGSztyGROACn80twOH4CIAVN60Qm814sLt4Ii8WDcvksWu7HYrM5m5UMZR1YvN7yOfOQSqUgyzJefvlljI6OGuPV1ZWQJAnbt29He3t72a5LKadjsdltpWPxxz/+EY888ojxtdfrgSiK2LFjBzo6Os5wPl2/7vWV97G4YOl/BRZL4fZvT5n2RC3vY0FAmQeWRCKFfP7kGxyZLZfLob+/D7IcwcBAP/L5wqlVi8WCjo5OSFIYXV3dRqv5mZnV31JYKqxWC7xed8kfi61gsx+LfD6P/v4+uFwutLYW7mbxehuQyeTQ3t4BSQqju9tfEvNpsx+LcsJjUVqqq91rXipS1oEln9eQy5XWL2Ch1fw4ZDmCnh4V6XTaeKyxsRGiGEYwGEJFxSsfU0rt32EtSvFYbFWb6VgU5tMEFCWCaFRFOp1CZ6cPb3lLCwDA7fbg1lvfA5fLZfxMKf27b6ZjUe54LErD2dwoXNaBpZQkErNLfXzkFa3mPR4PQqHCupS6unrzCiQqI4lEAtGoAkWJIB6PG+MVFRWoq6uHruvG5Z7jwwoRbV4MLGchk8kgFotCliMYHR0xxpdbzYtiGG1t7bxTiugMPfbYz405ZbPZ0d3dDVEMo729g/OJaIsqemBJpVJ44oknAADDw8OYn5/H4cOHAQAXXngh6urqil3CutI0DYODA1CUCPr6epHL5QAAgiCgra0dohiG3++H3b5ZWs0TFY+maRgaGoSqynj96y81buGXpDAsFsvSfAoYtyYT0dYl6EXeenZoaAhXXHHFCR/7xje+gX379q35uePx5IZck9R1HVNTk0uXfBSkUq8s5qutrYMkhREKSaisrCx6LaXGZrOgttazYceCTq6cjkVhPhW2yF9YKMynSy55A845ZzcArLjkU47K6VhsdjwWpaWuzrPmW8yLfoalvb0dsiwX+2WKYn5+HqpauI4+PX3MGHe7KxAKiQiFRDQ2NpX1GyvRRslkMjh69CUoioxjx6aMcZfLjWAwhNbWVmOMc4qIXo1rWF4lm11ELBaDqsoYGho0eh/ZbDb4fN2QJAnt7Z2wWq0mV0pU+o4/U6JpeTz77K+haRqsVqsxnzo6fJxPRHRaDCwoXEcfGRmGokQQi/Ugm32lU2tLSytEUUIgEILT6TSxSqLyoOs6RkaGIcsRpFIpvOUtbwVQODO5d+95qKysRCAQ2lR39wjCNJzOB2G1DkDXncjn/wTAiS+FE9HabOnAMj19zLgVeX5+3hivrq6GKIYhihK83moTKyQqH9PT01BVGYoiY35+zhhPJGaNebRv336zyiuSPDyez8DtvgdAFoW3VB2C8I8AdsFiuR9A2NQKiTaLLRdYUqkFY13K5OSkMe50OhEMhiCKYTQ3b+M1dKJV6uuL4bnnfovJyQljzOl0IhAIQhTDqKrymlhdcVVW/je4XPdDEJbvXcge9+hRVFUdxMzM48jnQ2aUR7SpbInAks1m0d/fB0WJYHBwAJpWWClusVjg83VBFMPo7PTBtpYuZ0RbTC6Xg65rxq372WwWk5MTsFgs6Oz0IRSS0NXVbdJ8ysNu/yWs1kHouhuLi5dD15uK8ko22+/gdn/llLUIQhIez39HIvHdotRAtJVs2r/Quq5jdHQEiiKjp0fF4uKi8VhTU7OxRf7yvg9EdHK6rmNsbBSKEkFPTxTnnvs6nHvu6wAAXV1+XHLJZUvzqcK0Gl2ur6Oi4n/Aah0zxnTdikzmbZif/0fo+vruNO1yfQW6boMg5E76PYKQh8PxCCyWIWha+7q+PtFWs+kCy8xMHKoqQ5ZlzM0ljPHKyipjv5Ta2loTKyQqH7OzM1CUwrqURGLWGB8aGjQCi91uxznn7DGrRABARcXn4PF85jV9SgQhD6fzB7DZnsfMzC/WNbTY7b8+ZVh5pQYdNtvzWFzcqoGlENocjl8ASEPTupBO38wAR2dsUwSWdDqNaFSFqsoYGxs1xh0OB/z+IERRQmtrG9elEK2Sruv4yU9+hMHBAWPMbrfD7w9CksJGt+RSYLW+BI/nMwCAE01xQcjDau2Dx/NpzM//f+v4ymey52ZR9+csWTbbs/B63wWrdQS6vvznRkdFxf9AOv1uzM//IwC7mSVSGSnbwKLrOmKxHhw9ehT9/X3I5/MAChtOdXT4IIoiurpeaTVPRCeXz+cxNjaKtrbCp15BEOByuZbmUydEsbAupRRbTrjdX17VpRmX69+QTN4FXV+fO/9yuXNhtQ6s6ixLLrdrXV6znNhsR1BT81YsL0R+9X8nl+urEIQ5zM19GQA/TNLplW1gSSQS+OlPfwJNK3xyaWhohChKCAZD8Hi23hb5RGdK13VMTk4YLSfS6RRuvPFmo6v4hRdehP37Ly75+eRw/GKVl2bSsNl+h2z2wLq8bjr9Prhc3z/l9+i6FdnspdA0/7q8ZjnxeO4EkIUg5E/4uCDocLm+h1TqA8jl1t6ihbaOsg0suq7D4/HA7w9BksKor28wuySisjA3N7e0X0oE8XjcGK+oqMDcXMIILOWzB1Fm1d8pCKv/3tPJZl+PdPptcDp/CEE4UY8aCwA7kslPr9trlguLpQcOxxOn/T5dt8Ht/jLm5hhY6PTKNrB4PB4cOvRuaOxlRbRqo6Mj+OEPH1rRcqK7249QSEJHRycslrU1JTNTPi/CYpk86Sf5ld8bXMdXFjA39yXoehVcrm+iEFAK44UzPs2Ym/smcjlzFySbwWZ7cVXfJwg52Gy/K3I1tFmUbWCx2+2wWBaNPVWIaCVN0zA0NIhsNotAoPCHuqmpGS6XG7W1tZCkMPz+IByO0luXcibS6ffA4fjVKb9H1y3I5S4swgZuDszP/wsWFv5vuFwPwGrth667kc9ficrKtyOfzwDYiu9RZ7ImhetXaHXKNrAQ0YlNTU3h5ZdfhqrKWFhYQFWVF35/AIIgwGq14uabby37kHK8TOatyGb3wGb74wnPsui6AEBAMvmJotWgaT4sLPyt8bXNZkHh7XX9LkGVk1xuL3RdOG4H4BMrrPHh5SBaHQYWok0gmZxHLBbF4GAMg4MjxmJ0l8sNn68LuVzWuMNnM4WVAjtmZ/8d1dVvh93+O+i6FYKQX9qTRQDgQCJxP7LZS02uc+vQNB8WFw/C4XjslJfqBCGPdPp9G1gZlTMGFqJN4LnnfoNI5GW43Q5YrVZ0dXVBkiR0dPhgtVrNLq/odL0eMzOPwm5/DC7XN2G19kLXK7C4+Gak07es+y63dHrJ5P+E3X45gNRJz3yl03+BXO68jS+OyhIDC1EZ0XUdIyPDUBQZO3bsQnNzMwBAFMOYmYnjggvORVNTO2y2zXYWZTUsyGYPIps9aHYhBCCflzAz8wi83r+AzaYubRwnAMgDsCKVug3J5CdNrpLKCQMLURmIx+NQlAgURcb8/BwAwGq1GoGlpaUV1113PWprPYjHk8jltuJCTyo1+fw5iMefg93+FByORwGkoGk+pNM38KwXnTEGFqISlcvlcPToy1CUCCYmxo1xp9OJQKCwRT5R6ROQzV6CbPYSswuhMsfAQlRCdF03el5ZrVa88MLvMD8/D4vFsrRFfhhdXd2w2Th1iWhr4bsekcl0Xcf4+BgUJYKRkWHccMPNsFgsEAQB5557PjQtj1BIhNtdYXapRESmYWAhMkkiMQtZjkBVZczOzhrjQ0OD6Oz0AQB27TrHrPKIiEoKAwvRBhsbG8UzzzyN0dERY8xut8PvD0AUw2htbTOxOiKi0sTAQlRk+Xwei4uLcLvdAAr9e0ZHRyAIAtrbOyCKYXR3dxsbuxER0WsxsBAVga7rmJycgKLIUFUFPp8PBw5cCQCor2/AG95wOTo7u1BZWWlypURE5YGBhWgdzc3NQVVlKIqMeHzaGB8bG4WmacZi2h07dplYJRFR+WFgIVonv/zlY4hEXoZeaGIDm82Grq5uiGIYHR2dsFgsJldIRFS+GFiI1kDTNAwPD2HbthbY7XYAQFVVFXRdR2trGyQpjO7uAJxOp8mVEhFtDgwsRGfg2LEpyHIE0aiCZDKJK6+8CsFgCACwY8cuhEISvF6vyVUSEW0+DCxEp7GwkISqKlAUGVNTk8a4y+XG4mLG+Nrtdht3AhER0fpiYCE6hWRyHv/6r1+HphWaCVqtVvh8XRDFMDo7fbBarSZXSES0NTCwEC3RdR0jI8OYnp7GOefsBgB4PJVobGyCIAgQRQmBQAgul8vkSomIth4GFtry4vE4FCUCRZExPz8Hq9WKUEg0gslb3/pnxsJaIiIyBwMLbUmpVArRqApFiWBiYtwYdzgcCARCyOVyxhjDChGR+RhYaEtSVRlPPfUkAMBisaCjoxOiGIbP18WAQkRUghhYaFPTdR3j42NQlAhaW9sQDIoAgGAwBFVVEAqJCAZDqKjwmFwpERGdCgMLbUqJxCwURYaiRDA7OwugsFZlObBUVHhw3XU3mFkiERGdAQYW2lQikZcRiRzF6OiIMWa32+H3ByCKYRMrIyKis8HAQmVN13UIgmB8raoKRkdHIAgC2ts7IIphdHd3w253mFglERGdLQYWKju6rmNycgKqqiAaVfD2t98Ij6cSALB79x60t3ciFBJRWVlpcqVERLReNiSw9PT04DOf+Qyef/55eDweXHvttfjIRz4Ch4Ofemn15ubmEI0qkOUI4vFpY7ynJ4rdu/cCAHy+bvh83SZVSERExVL0wDI7O4t3vetd6Orqwj333IPx8XHcfffdSKfT+OQnP1nsl6dNYGYmjieffALDw0PQdR0AYLPZ0NXVDVEMo729w+QKiYio2IoeWL797W8jmUziX/7lX1BTUwMAyOfzuOuuu/DBD34Qzc3NxS6ByoymaVhYWDAu6bjdFRgbG4Wu62hpaYUkheH3B+F0Ok2ulIiINkrRA8uvfvUr7N+/3wgrAHD11VfjU5/6FJ566in8+Z//ebFLoDIxPX0MshyBqsqoqPDg7W+/EQDgdDpx4MCVaGxshNdbbXKVRERkhqIHllgshuuuu27FmNfrRWNjI2Kx2Fk9t9VqOaufp7O3fAzWeiwWFpJQlMK6lKmpSWNc1zUsLqZRUVEBAJAk8eyL3eTO9ljQ+uGxKB08FqXluJs6z1jRA0sikYDX633NeHV1tbGh11p5ve6z+nlaP2s5Fs888wx+/etfQ9M0AIDH44Lf78eOHTvQ3d0Nm403sa0F50Xp4LEoHTwW5a+s/yIkEink85rZZWxpVqsFXq/7tMdC13WMjAyjurrGWJtis7mRTKbR3LwNkhRGMBiC2114U5mbywDIbMS/wqax2mNBxcdjUTp4LEpLdbUbFsvaznYVPbB4vV7Mzc29Znx2dhbV1We3HiGf15DL8RewFJzsWMTjcaiqDEWRMTeXwPnnX4gLLtgHAGhr68SNN96Cmppa4/t5PM8e50Xp4LEoHTwWpWHpRs81KXpg8fv9r1mrMjc3h8nJSfj9/mK/PJkglUqhpycKRYlgfHzMGC/su/PKb6vNZlsRVoiIiE6m6IHlsssuwxe/+MUVa1kOHz4Mi8WCiy++uNgvTxtM0zR8+9sPIJ1OAQAsFgs6OjohimH4fF2w2+0mV0hEROWo6IHlpptuwje/+U18+MMfxgc/+EGMj4/jc5/7HG666SbuwVLmdF3H6OgoXn55HNu37wFQCCjd3X5MTU1AFAvrUioqPCZXSkRE5a7ogaW6uhpf//rX8elPfxof/vCH4fF48Pa3vx233357sV+aiiSRmIWiyFCUCObmEnC7HWhoaEFdXSMA4JJLLuMdPkREtK425K9KIBDA1772tY14KSqSTCaDWCwKWY5gdHTEGHc4HNixYwdstlcu9TCsEBHReuNfFlqViYlx/PKXjwEABEFAW1s7RDEMUQyiqakW8XiSK/CJiKhoGFhoBV3XMTU1CUWR4Xa7cd555wMA2tra0d7egfb2ToRC4nF7qXD3SCIiKj4GFgIAzM/PQ1VlyHIE8fg0AKCiogJ7954Hi8UCi8WCt771z8wtkoiItiwGli2upyeKl1/+I4aHh6Av7ehjs9ng83VDkiSTqyMiIipgYNliNE2DIAgQljpQjY6OYGhoEADQ0tIKUZQQCITgdDrNLJOIiGgFBpYtYnr6GGQ5AlWVccUVf4K2tnYAwPbtO+ByuSCKErzes2uVQEREVCwMLJvYwkIS0agKRYlgcnLSGO/pUY3AUl/fgPr6BrNKJCIiWhUGlk0onU7j0Ud/jqGhAWha4VZjq9WKzk4fRDGMzk6fyRUSERGdGQaWTUDXdSQSs6iurgEAOJ1OzM7GoWkampu3IRSSEAyG4Ha7zS2UiIhojRhYytjMTHxpi3wZi4sZHDr0HthsNgiCgDe84QAqKjyorWU3ZCIiKn8MLGUmlUqhpycKRYlgfHzMGHc4HJiePoampkJDyeU1KkRERJsBA0sZURQZv/zlo8jn8wAKnZHb2zshSRJ8vm7Y7fbTPAMREVF5YmApUbquY2JiHFarFQ0NhS7IjY1NyOfzaGxshCiGEQyGUFHhMblSIiKi4mNgKTGJRAKqKkNRIpiZmUEgEMKf/MlVAIDa2lq84x3vRE0N16UQEdHWwsBSAjKZDGKxKGQ5gtHREWPcZrPD4XBA13VjZ1qGFSIi2ooYWErAT37yI2MBrSAIaGtrhyiG4ff7Ybc7TK6OiIjIfAwsG0jXdUxNTUJVZVxwwT4jjPj9ASwuLkKSwgiFJFRWVppcKRERUWlhYNkA8/PzUFUFihLB9PQxAEB9fSMkKQwA2L17L/bsOde47ENEREQrMbAUSTabRSzWA0WJYHh4CLquAwBsNht8vm5UV7/SaNBisZhVJhERUVlgYCmSdDqNxx77ufF1S0srRFFCIBCC0+k0sTIiIqLyw8CyDqanj0GWI8hkMnjjGw8AAKqqqhAO70BVVRVEUYLXW32aZyEiIqKTYWBZo1RqwViXMjk5CaBwaefCC/cZm7ldfvkVZpZIRES0aTCwnKHh4SEcOfI8BgcHoGkaAMBqtaKz0wdRDMPh4OUeIiKi9cbAchq6rkPTNFitVgCFDsn9/X0AgKamZmOLfLfbbWKVREREmxsDy0nMzMShqjJkWcbevedh165zAACBQAjJZBKhkITaWu46S0REtBEYWI6TTqcRjapQlIix8ywAxGJRI7C4XC5ceOFFZpVIRES0JTGwoHDZ5xe/eAS9vTHk83kAhQW07e2dkCQJPl+3yRUSERFtbVsysOi6jnh8GnV19QAK/Xuy2Szy+TwaGhohihJCIdG424eIiIjMtaUCSyKRgKrKUJQIZmZmcMsth4z9US688CLs27cf9fUNJldJREREr7bpA8vi4iJisShkOYKRkWFj3GazY2pqyggsDQ2NZpVIREREp7GpA8vo6Ah+/OMfIpfLAShc+mltbYMohuH3B+BwOEyukIiIiFZj0wQWXdcxNTWJxcVFtLW1AwAaGhogCAJqa+sgSWEEgyKqqqpMrpSIiIjOVNkHlmRyHopS2CJ/evoYamvrcOONN0MQBNjtDtx44y2orKyEIAhml0pERERrVLaBZXFxET/60b9jYGAAuq4DAGw2G+rq6pHLZWG3Fy738IwKERFR+SvbwLKwsIDBwUJYaWlphShKCARCcDrZy4eIiGizKdvAUuiMfBECgZBxpw8RERFtTmUbWKqqqnDBBRcil9PMLoWIiIiKzGJ2AWvFRbRERERbR9kGFiIiIto6GFiIiIio5BU9sDz11FP46Ec/ioMHD0KSJPz93/99sV+SiIiINpmiB5Ynn3wSkUgEF1xwAbxeb7FfjoiIiDahot8l9Dd/8ze44447AADPPvtssV+OiIiINqGin2GxWLhMhoiIiM5O2e7DAgDV1W4s7cpPJlm+u5zHwnw8FqWDx6J08FiUFotl7VuSlHVg4dmb0sFjUTp4LEoHj0Xp4LEof2ccWObm5jAxMXHa7+vo6IDD4VhTUURERETHO+PAcvjwYdx5552n/b6HH34YgUBgTUURERERHe+MA8v111+P66+/vhi1EBEREZ0QL+oRERFRySv6otvh4WG8+OKLAIBUKoWBgQEcPnwYAHDVVVcV++WJiIhoExB0vbg3ej300EP4+Mc/fsLHZFku5ksTERHRJlH0wEJERER0triGhYiIiEoeAwsRERGVPAYWIiIiKnkMLERERFTyGFiIiIio5DGwEBERUckr+8Dy1FNP4aMf/SgOHjwISZLw93//92aXtCX09PTg3e9+N/bu3YuLL74Yn/vc57C4uGh2WVtSf38/PvnJT+Laa6/Fjh07cM0115hd0pb005/+FH/5l3+Jyy67DHv37sW1116LBx98ENw5whxPPPEE3vnOd+Kiiy7Crl27cMUVV+Czn/0s5ubmzC5ty0smk7jssssgSZKxsexqFH2n22J78sknEYlEcMEFF2B2dtbscraE2dlZvOtd70JXVxfuuecejI+P4+6770Y6ncYnP/lJs8vbclRVxRNPPIE9e/ZA0zT+gTTJ1772NbS1teGOO+5AbW0tnn76aXziE5/A2NgYbrvtNrPL23JmZmawe/du3HrrraipqYGqqrjnnnugqiruv/9+s8vb0r7whS8gn8+f+Q/qZS6fzxv/fPnll+t33XWXidVsDV/84hf1vXv36vF43Bj79re/rW/fvl0fGxszr7At6vg58LGPfUx/y1veYmI1W9exY8deM3bnnXfq55133opjROb5zne+o4uiyPcpE0WjUX3v3r36t771LV0URf0Pf/jDqn+27C8JWSxl/69Qdn71q19h//79qKmpMcauvvpqaJqGp556yrzCtijOgdJQV1f3mrHt27djfn4eCwsLJlREr7b8npXNZs0tZAv7zGc+g5tuugnd3d1n/LN8p6MzFovF4Pf7V4x5vV40NjYiFouZVBVR6fnd736H5uZmVFZWml3KlpXP55HJZPDSSy/h85//PA4cOID29nazy9qSDh8+DEVR8OEPf3hNP1/2a1ho4yUSCXi93teMV1dXcx0R0ZLnnnsODz/8MD72sY+ZXcqWdvnll2N8fBwAcOmll+J//a//ZXJFW1MqlcLdd9+N22+/fc0BvuQCy9zcHCYmJk77fR0dHXA4HBtQERHRmRkbG8Ptt9+Offv24dChQ2aXs6Xdd999SKVSiEajuPfee/GhD30IX/3qV2G1Ws0ubUu59957UV9fj+uuu27Nz1FygeXw4cO48847T/t9Dz/8MAKBwAZURK/m9XpPeGvg7OwsqqurTaiIqHQkEgm8//3vR01NDe655x6uMTJZOBwGAJx77rk455xzcO211+LnP/85rrrqKpMr2zqGh4dx//334/Of/7zxt2N5XdfCwgKSySQ8Hs9pn6fkAsv111+P66+/3uwy6BT8fv9r1qrMzc1hcnLyNWtbiLaSdDqND37wg5ibm8N3vvMdVFVVmV0SHUeSJNjtdgwMDJhdypYyNDSEbDaLD3zgA6957NChQ9izZw+++93vnvZ5Si6wUOm77LLL8MUvfnHFWpbDhw/DYrHg4osvNrk6InPkcjl85CMfQSwWwwMPPIDm5mazS6JXOXLkCLLZLBfdbrDt27fjG9/4xoqxo0eP4rOf/SzuuusunHPOOat6nrIPLMPDw8ZOealUCgMDAzh8+DAA8JRfkdx000345je/iQ9/+MP44Ac/iPHxcXzuc5/DTTfdxDdpE6RSKTzxxBMACvNhfn7emAMXXnjhCW+3pfV311134fHHH8cdd9yB+fl5vPDCC8ZjO3bs4Jq7DXbbbbdh165dkCQJLpcLkUgEX/nKVyBJEg4ePGh2eVuK1+vFvn37TvjYzp07sXPnzlU9j6Dr5b0t5kMPPYSPf/zjJ3xMluUNrmbr6Onpwac//Wk8//zz8Hg8uPbaa3H77bfzTdkEQ0NDuOKKK0742De+8Y2TvlHQ+jpw4ACGh4dP+Nijjz7KT/Ub7L777sPDDz+MgYEB6LqOtrY2XHnllXjve9/L28xLwLPPPotDhw7hwQcfXPUZlrIPLERERLT5cfk6ERERlTwGFiIiIip5DCxERERU8hhYiIiIqOQxsBAREVHJY2AhIiKiksfAQkRERCWPgYWIiIhKHgMLERERlTwGFiIiIip5DCxERERU8v5/Uw+Slkht6IgAAAAASUVORK5CYII="}}]}}, 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diff --git a/ml-fundamentals/classification/more-classifiers.html b/ml-fundamentals/classification/more-classifiers.html
index af292b5731..dd6a0dbe97 100644
--- a/ml-fundamentals/classification/more-classifiers.html
+++ b/ml-fundamentals/classification/more-classifiers.html
@@ -27,8 +27,8 @@
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     <link rel="stylesheet" type="text/css" href="../../_static/sphinx-thebe.css" />
     <link rel="stylesheet" type="text/css" href="../../_static/drawio.css" />
-    <link rel="stylesheet" type="text/css" href="../../_static/style.css" />
     <link rel="stylesheet" type="text/css" href="../../_static/youtube.css" />
+    <link rel="stylesheet" type="text/css" href="../../_static/style.css" />
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@@ -2184,7 +2184,7 @@ <h2><span class="section-number">13.2.4. </span>Exercise - apply logistic regres
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Accuracy is 0.8031693077564637
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Accuracy is 0.8115095913261051
 </pre></div>
 </div>
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@@ -2207,8 +2207,8 @@ <h2><span class="section-number">13.2.4. </span>Exercise - apply logistic regres
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>ingredients: Index([&#39;black_pepper&#39;, &#39;egg&#39;, &#39;scallion&#39;, &#39;soy_sauce&#39;], dtype=&#39;object&#39;)
-cuisine: korean
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>ingredients: Index([&#39;egg&#39;, &#39;honey&#39;, &#39;milk&#39;, &#39;shrimp&#39;, &#39;vegetable_oil&#39;, &#39;walnut&#39;], dtype=&#39;object&#39;)
+cuisine: chinese
 </pre></div>
 </div>
 </div>
@@ -2259,23 +2259,23 @@ <h2><span class="section-number">13.2.4. </span>Exercise - apply logistic regres
   <tbody>
     <tr>
       <th>chinese</th>
-      <td>0.529949</td>
+      <td>0.374274</td>
     </tr>
     <tr>
-      <th>korean</th>
-      <td>0.294966</td>
+      <th>japanese</th>
+      <td>0.252066</td>
     </tr>
     <tr>
-      <th>japanese</th>
-      <td>0.136638</td>
+      <th>korean</th>
+      <td>0.207289</td>
     </tr>
     <tr>
       <th>thai</th>
-      <td>0.037765</td>
+      <td>0.128032</td>
     </tr>
     <tr>
       <th>indian</th>
-      <td>0.000681</td>
+      <td>0.038340</td>
     </tr>
   </tbody>
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@@ -2296,15 +2296,15 @@ <h2><span class="section-number">13.2.4. </span>Exercise - apply logistic regres
 <div class="cell_output docutils container">
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>              precision    recall  f1-score   support
 
-     chinese       0.75      0.70      0.73       253
-      indian       0.91      0.90      0.90       230
-    japanese       0.73      0.82      0.77       223
-      korean       0.85      0.78      0.82       238
-        thai       0.79      0.82      0.80       255
+     chinese       0.73      0.73      0.73       232
+      indian       0.91      0.90      0.91       231
+    japanese       0.78      0.77      0.78       242
+      korean       0.84      0.79      0.82       243
+        thai       0.79      0.86      0.82       251
 
-    accuracy                           0.80      1199
-   macro avg       0.81      0.81      0.80      1199
-weighted avg       0.81      0.80      0.80      1199
+    accuracy                           0.81      1199
+   macro avg       0.81      0.81      0.81      1199
+weighted avg       0.81      0.81      0.81      1199
 </pre></div>
 </div>
 </div>
diff --git a/ml-fundamentals/classification/yet-other-classifiers.html b/ml-fundamentals/classification/yet-other-classifiers.html
index 65ffba8589..73fe281d2d 100644
--- a/ml-fundamentals/classification/yet-other-classifiers.html
+++ b/ml-fundamentals/classification/yet-other-classifiers.html
@@ -27,8 +27,8 @@
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-    <link rel="stylesheet" type="text/css" href="../../_static/style.css" />
     <link rel="stylesheet" type="text/css" href="../../_static/youtube.css" />
+    <link rel="stylesheet" type="text/css" href="../../_static/style.css" />
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@@ -1839,18 +1839,18 @@ <h3><span class="section-number">13.3.4.1. </span>Exercise - apply a linear SVC<
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Accuracy (train) for Linear SVC: 78.2% 
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Accuracy (train) for Linear SVC: 79.6% 
               precision    recall  f1-score   support
 
-     chinese       0.69      0.72      0.71       240
-      indian       0.88      0.93      0.91       253
-    japanese       0.75      0.75      0.75       240
-      korean       0.80      0.73      0.77       250
-        thai       0.78      0.77      0.78       216
+     chinese       0.72      0.71      0.71       249
+      indian       0.86      0.89      0.88       238
+    japanese       0.84      0.74      0.78       250
+      korean       0.79      0.79      0.79       234
+        thai       0.77      0.87      0.82       228
 
-    accuracy                           0.78      1199
-   macro avg       0.78      0.78      0.78      1199
-weighted avg       0.78      0.78      0.78      1199
+    accuracy                           0.80      1199
+   macro avg       0.80      0.80      0.80      1199
+weighted avg       0.80      0.80      0.80      1199
 </pre></div>
 </div>
 </div>
@@ -1874,32 +1874,30 @@ <h3><span class="section-number">13.3.5.1. </span>Exercise - apply the K-Neighbo
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Accuracy (train) for Linear SVC: 78.2% 
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Accuracy (train) for Linear SVC: 79.6% 
               precision    recall  f1-score   support
 
-     chinese       0.69      0.72      0.71       240
-      indian       0.88      0.93      0.91       253
-    japanese       0.75      0.75      0.75       240
-      korean       0.80      0.73      0.77       250
-        thai       0.78      0.77      0.78       216
+     chinese       0.72      0.71      0.71       249
+      indian       0.86      0.89      0.88       238
+    japanese       0.84      0.74      0.78       250
+      korean       0.79      0.79      0.79       234
+        thai       0.77      0.87      0.82       228
 
-    accuracy                           0.78      1199
-   macro avg       0.78      0.78      0.78      1199
-weighted avg       0.78      0.78      0.78      1199
+    accuracy                           0.80      1199
+   macro avg       0.80      0.80      0.80      1199
+weighted avg       0.80      0.80      0.80      1199
 
-Accuracy (train) for KNN classifier: 72.7% 
-</pre></div>
-</div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>              precision    recall  f1-score   support
+Accuracy (train) for KNN classifier: 73.2% 
+              precision    recall  f1-score   support
 
-     chinese       0.68      0.71      0.70       240
-      indian       0.86      0.84      0.85       253
-    japanese       0.63      0.85      0.72       240
-      korean       0.88      0.52      0.65       250
-        thai       0.67      0.72      0.70       216
+     chinese       0.66      0.64      0.65       249
+      indian       0.86      0.79      0.83       238
+    japanese       0.66      0.86      0.75       250
+      korean       0.86      0.56      0.68       234
+        thai       0.70      0.81      0.75       228
 
     accuracy                           0.73      1199
-   macro avg       0.75      0.73      0.72      1199
+   macro avg       0.75      0.73      0.73      1199
 weighted avg       0.75      0.73      0.73      1199
 </pre></div>
 </div>
@@ -1928,47 +1926,45 @@ <h3><span class="section-number">13.3.6.1. </span>Exercise - apply a Support Vec
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Accuracy (train) for Linear SVC: 78.2% 
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Accuracy (train) for Linear SVC: 79.6% 
               precision    recall  f1-score   support
 
-     chinese       0.69      0.72      0.71       240
-      indian       0.88      0.93      0.91       253
-    japanese       0.75      0.75      0.75       240
-      korean       0.80      0.73      0.77       250
-        thai       0.78      0.77      0.78       216
+     chinese       0.72      0.71      0.71       249
+      indian       0.86      0.89      0.88       238
+    japanese       0.84      0.74      0.78       250
+      korean       0.79      0.79      0.79       234
+        thai       0.77      0.87      0.82       228
 
-    accuracy                           0.78      1199
-   macro avg       0.78      0.78      0.78      1199
-weighted avg       0.78      0.78      0.78      1199
+    accuracy                           0.80      1199
+   macro avg       0.80      0.80      0.80      1199
+weighted avg       0.80      0.80      0.80      1199
 
-Accuracy (train) for KNN classifier: 72.7% 
-</pre></div>
-</div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>              precision    recall  f1-score   support
+Accuracy (train) for KNN classifier: 73.2% 
+              precision    recall  f1-score   support
 
-     chinese       0.68      0.71      0.70       240
-      indian       0.86      0.84      0.85       253
-    japanese       0.63      0.85      0.72       240
-      korean       0.88      0.52      0.65       250
-        thai       0.67      0.72      0.70       216
+     chinese       0.66      0.64      0.65       249
+      indian       0.86      0.79      0.83       238
+    japanese       0.66      0.86      0.75       250
+      korean       0.86      0.56      0.68       234
+        thai       0.70      0.81      0.75       228
 
     accuracy                           0.73      1199
-   macro avg       0.75      0.73      0.72      1199
+   macro avg       0.75      0.73      0.73      1199
 weighted avg       0.75      0.73      0.73      1199
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Accuracy (train) for SVC: 81.2% 
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Accuracy (train) for SVC: 82.0% 
               precision    recall  f1-score   support
 
-     chinese       0.75      0.76      0.75       240
-      indian       0.91      0.91      0.91       253
-    japanese       0.81      0.80      0.81       240
-      korean       0.86      0.77      0.81       250
-        thai       0.74      0.82      0.78       216
+     chinese       0.75      0.72      0.74       249
+      indian       0.90      0.89      0.89       238
+    japanese       0.87      0.80      0.83       250
+      korean       0.82      0.83      0.82       234
+        thai       0.77      0.86      0.81       228
 
-    accuracy                           0.81      1199
-   macro avg       0.81      0.81      0.81      1199
-weighted avg       0.81      0.81      0.81      1199
+    accuracy                           0.82      1199
+   macro avg       0.82      0.82      0.82      1199
+weighted avg       0.82      0.82      0.82      1199
 </pre></div>
 </div>
 </div>
@@ -1993,75 +1989,73 @@ <h2><span class="section-number">13.3.7. </span>Ensemble Classifiers<a class="he
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Accuracy (train) for Linear SVC: 78.2% 
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Accuracy (train) for Linear SVC: 79.6% 
               precision    recall  f1-score   support
 
-     chinese       0.69      0.72      0.71       240
-      indian       0.88      0.93      0.91       253
-    japanese       0.75      0.75      0.75       240
-      korean       0.80      0.73      0.77       250
-        thai       0.78      0.77      0.78       216
+     chinese       0.72      0.71      0.71       249
+      indian       0.86      0.89      0.88       238
+    japanese       0.84      0.74      0.78       250
+      korean       0.79      0.79      0.79       234
+        thai       0.77      0.87      0.82       228
 
-    accuracy                           0.78      1199
-   macro avg       0.78      0.78      0.78      1199
-weighted avg       0.78      0.78      0.78      1199
+    accuracy                           0.80      1199
+   macro avg       0.80      0.80      0.80      1199
+weighted avg       0.80      0.80      0.80      1199
 
-Accuracy (train) for KNN classifier: 72.7% 
-</pre></div>
-</div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>              precision    recall  f1-score   support
+Accuracy (train) for KNN classifier: 73.2% 
+              precision    recall  f1-score   support
 
-     chinese       0.68      0.71      0.70       240
-      indian       0.86      0.84      0.85       253
-    japanese       0.63      0.85      0.72       240
-      korean       0.88      0.52      0.65       250
-        thai       0.67      0.72      0.70       216
+     chinese       0.66      0.64      0.65       249
+      indian       0.86      0.79      0.83       238
+    japanese       0.66      0.86      0.75       250
+      korean       0.86      0.56      0.68       234
+        thai       0.70      0.81      0.75       228
 
     accuracy                           0.73      1199
-   macro avg       0.75      0.73      0.72      1199
+   macro avg       0.75      0.73      0.73      1199
 weighted avg       0.75      0.73      0.73      1199
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Accuracy (train) for SVC: 81.2% 
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Accuracy (train) for SVC: 82.0% 
               precision    recall  f1-score   support
 
-     chinese       0.75      0.76      0.75       240
-      indian       0.91      0.91      0.91       253
-    japanese       0.81      0.80      0.81       240
-      korean       0.86      0.77      0.81       250
-        thai       0.74      0.82      0.78       216
+     chinese       0.75      0.72      0.74       249
+      indian       0.90      0.89      0.89       238
+    japanese       0.87      0.80      0.83       250
+      korean       0.82      0.83      0.82       234
+        thai       0.77      0.86      0.81       228
 
-    accuracy                           0.81      1199
-   macro avg       0.81      0.81      0.81      1199
-weighted avg       0.81      0.81      0.81      1199
+    accuracy                           0.82      1199
+   macro avg       0.82      0.82      0.82      1199
+weighted avg       0.82      0.82      0.82      1199
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Accuracy (train) for RFST: 83.8% 
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Accuracy (train) for RFST: 84.5% 
               precision    recall  f1-score   support
 
-     chinese       0.81      0.78      0.79       240
-      indian       0.89      0.94      0.92       253
-    japanese       0.83      0.82      0.82       240
-      korean       0.84      0.79      0.82       250
-        thai       0.81      0.86      0.83       216
+     chinese       0.82      0.77      0.79       249
+      indian       0.89      0.89      0.89       238
+    japanese       0.90      0.82      0.86       250
+      korean       0.82      0.87      0.84       234
+        thai       0.80      0.88      0.84       228
 
     accuracy                           0.84      1199
-   macro avg       0.84      0.84      0.84      1199
-weighted avg       0.84      0.84      0.84      1199
+   macro avg       0.85      0.85      0.85      1199
+weighted avg       0.85      0.84      0.84      1199
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Accuracy (train) for ADA: 70.2% 
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Accuracy (train) for ADA: 70.7% 
               precision    recall  f1-score   support
 
-     chinese       0.64      0.45      0.53       240
-      indian       0.87      0.86      0.86       253
-    japanese       0.69      0.62      0.65       240
-      korean       0.62      0.79      0.70       250
-        thai       0.69      0.79      0.74       216
+     chinese       0.61      0.46      0.52       249
+      indian       0.87      0.85      0.86       238
+    japanese       0.71      0.58      0.64       250
+      korean       0.62      0.82      0.71       234
+        thai       0.74      0.84      0.79       228
 
-    accuracy                           0.70      1199
-   macro avg       0.70      0.70      0.70      1199
-weighted avg       0.70      0.70      0.70      1199
+    accuracy                           0.71      1199
+   macro avg       0.71      0.71      0.70      1199
+weighted avg       0.71      0.71      0.70      1199
 </pre></div>
 </div>
 </div>
diff --git a/ml-fundamentals/ml-overview.html b/ml-fundamentals/ml-overview.html
index c42f8b4235..1a52d00473 100644
--- a/ml-fundamentals/ml-overview.html
+++ b/ml-fundamentals/ml-overview.html
@@ -27,8 +27,8 @@
     <link rel="stylesheet" type="text/css" href="../_static/mystnb.css" />
     <link rel="stylesheet" type="text/css" href="../_static/sphinx-thebe.css" />
     <link rel="stylesheet" type="text/css" href="../_static/drawio.css" />
-    <link rel="stylesheet" type="text/css" href="../_static/style.css" />
     <link rel="stylesheet" type="text/css" href="../_static/youtube.css" />
+    <link rel="stylesheet" type="text/css" href="../_static/style.css" />
     <link rel="stylesheet" type="text/css" href="../_static/design-style.b7bb847fb20b106c3d81b95245e65545.min.css" />
     
   <!-- Pre-loaded scripts that we'll load fully later -->
diff --git a/ml-fundamentals/regression/linear-and-polynomial-regression.html b/ml-fundamentals/regression/linear-and-polynomial-regression.html
index 0c14459152..99db642e11 100644
--- a/ml-fundamentals/regression/linear-and-polynomial-regression.html
+++ b/ml-fundamentals/regression/linear-and-polynomial-regression.html
@@ -27,8 +27,8 @@
     <link rel="stylesheet" type="text/css" href="../../_static/mystnb.css" />
     <link rel="stylesheet" type="text/css" href="../../_static/sphinx-thebe.css" />
     <link rel="stylesheet" type="text/css" href="../../_static/drawio.css" />
-    <link rel="stylesheet" type="text/css" href="../../_static/style.css" />
     <link rel="stylesheet" type="text/css" href="../../_static/youtube.css" />
+    <link rel="stylesheet" type="text/css" href="../../_static/style.css" />
     <link rel="stylesheet" type="text/css" href="../../_static/design-style.b7bb847fb20b106c3d81b95245e65545.min.css" />
     
   <!-- Pre-loaded scripts that we'll load fully later -->
@@ -2213,7 +2213,7 @@ <h2><span class="section-number">11.3.3. </span>Correlation<a class="headerlink"
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;matplotlib.collections.PathCollection at 0x7f40d3e347c0&gt;
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;matplotlib.collections.PathCollection at 0x7f82242c05b0&gt;
 </pre></div>
 </div>
 <img alt="../../_images/linear-and-polynomial-regression_12_1.png" src="../../_images/linear-and-polynomial-regression_12_1.png" />
@@ -2399,7 +2399,7 @@ <h2><span class="section-number">11.3.5. </span>Simple Linear Regression<a class
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[&lt;matplotlib.lines.Line2D at 0x7f40c9176970&gt;]
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[&lt;matplotlib.lines.Line2D at 0x7f82195fd5b0&gt;]
 </pre></div>
 </div>
 <img alt="../../_images/linear-and-polynomial-regression_34_1.png" src="../../_images/linear-and-polynomial-regression_34_1.png" />
@@ -2644,8 +2644,8 @@ <h2><span class="section-number">11.3.8. </span>Putting it all together<a class=
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Mean error: 2.51 (9.19%)
-Model determination:  0.9554938013125742
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Mean error: 2.6 (9.52%)
+Model determination:  0.9505769161049876
 </pre></div>
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00,104,109,112,124,127,129,131,137,139,140,141,143,146,150,151,152,154,156,157,158,165,167,170,171,172,173,184,188],By:[7,18,29,40,41,46,49,52,53,54,57,59,68,74,77,78,80,99,109,114,117,119,123,124,128,137,139,140,142,145,146,147,148,155,158,162,165,166,167,171,182],FOR:[92,93,171,172],For:[7,19,29,30,31,32,35,36,38,39,40,41,43,45,46,47,48,49,50,51,54,59,60,61,64,66,67,68,69,71,72,74,75,78,84,85,86,88,89,90,91,100,101,102,111,112,113,114,116,117,119,121,122,123,126,129,130,131,133,135,137,139,140,141,142,143,145,146,147,148,150,151,152,155,156,157,158,159,162,163,164,165,166,167,168,170,171,172,173,174,184,188,191,192,194],IN:[25,82,92,93,171,172],IS:[22,45,47,48,54,92,93,97,145,171,172],IT:[54,99,140],If:[1,7,14,16,18,29,33,34,35,39,40,41,43,45,48,49,50,51,52,53,54,57,58,59,60,62,64,66,68,69,74,76,77,78,80,82,92,93,95,100,101,103,104,106,108,109,110,111,112,113,114,116,117,118,119,121,122,123,124,126,128,129,130,131,132,134,136,139,140,141,143,145,147,150,151,152,154,156,157,158,159,162,164,165,166,167,168,170,171,172,173,180,186,188,191,193,194,195],In:[1,3,7,8,9,11,12,13,14,16,18,19,21,24,28,29,30,31,32,33,36,39,40,41,43,45,46,47,48,49,50,52,53,54,57,58,59,60,61,62,64,66,68,69,71,74,75,76,77,78,80,82,83,88,89,90,92,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,121,122,123,124,125,126,127,128,129,130,131,132,133,135,137,139,140,141,142,143,145,146,147,148,150,151,152,153,154,155,156,157,158,159,161,162,163,164,165,166,167,168,169,170,171,172,173,174,177,179,180,181,186,188,189,190,191,192,194,195],Is:[50,93,97,99,103,106,111,112,113,128,140,141,145,159,165,167,168,195],It:[0,1,2,3,4,5,7,8,9,10,11,13,14,15,16,17,18,19,20,23,24,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,46,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,71,72,74,76,77,78,80,82,83,84,85,86,88,89,90,91,99,100,101,102,103,104,106,108,109,110,111,112,113,114,116,117,118,119,121,122,123,127,128,129,130,131,132,133,134,135,136,137,139,140,141,142,143,144,145,146,147,148,150,151,152,153,154,155,156,157,158,159,162,163,164,165,166,168,170,171,172,173,175,176,182,186,187,188,189,191,192,194],Its:[129,150,155,167],NEAR:[61,74],NO:[92,93,171,172],NOT:[82,92,93,119,171,172],Near:[109,178],No:[7,20,29,33,50,54,56,64,82,88,93,98,100,101,110,130,143,147,156,158,163,171,172,175,178],Not:[7,40,43,49,52,54,56,68,80,101,111,117,118,134,151,163,166,167,172,187,194],OF:[22,45,47,48,92,93,171,172],ON:[124,180],ONE:7,OR:[22,45,47,48,92,93,119,171,172],Of:[50,101,102,104,114,158,173,174],On:[49,50,52,57,58,59,60,61,66,68,74,80,101,104,106,141,147,151,154,155,158,159,165,166,170,171,177,187],One:[1,7,11,28,40,43,49,50,52,53,54,55,57,58,59,66,83,98,103,104,106,108,110,114,116,119,127,129,135,141,146,148,150,156,165,166,168,171,172,173,177,178,179,184,186,191,194],Or:[32,40,50,58,74,102,104,119,129,130,141,145,148,165,171,172,187,191,194],Such:[1,7,30,40,43,49,50,54,116,141,142,166,171,193],THAT:82,THE:[92,93,171,172],TO:[54,92,93,136,171,172],That:[31,32,40,43,48,49,50,52,57,61,62,68,74,80,104,109,116,119,124,130,146,148,151,152,158,159,165,167,172,173,191],The:[0,3,5,6,7,8,12,13,14,15,16,18,19,24,25,26,28,29,30,31,32,33,34,35,37,39,40,41,45,46,47,48,49,50,52,54,55,56,57,58,59,60,61,62,63,64,65,66,68,71,74,76,78,80,83,84,90,91,92,93,99,102,103,104,106,108,109,110,112,113,114,115,116,118,119,121,122,123,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,145,146,147,148,150,151,152,153,154,155,156,159,162,163,165,166,167,173,174,176,177,178,182,183,185,186,187,188,189,190,191,192,195],Then:[7,31,45,47,50,56,66,76,77,82,101,104,116,119,127,128,131,134,136,141,142,143,146,147,148,150,151,155,159,166,170,171,172,173,187],There:[0,1,3,7,18,28,29,30,32,34,36,37,39,40,41,43,46,47,49,50,51,54,57,59,60,62,64,68,72,74,80,85,100,101,104,106,108,109,110,113,114,116,117,118,119,121,124,126,127,129,132,134,136,137,139,140,141,142,143,145,146,148,150,151,154,158,159,161,162,164,165,166,167,169,170,171,172,173,191,192,193,194],These:[7,30,31,39,41,45,49,54,56,59,60,74,75,77,78,99,101,104,106,109,112,113,118,119,122,123,132,140,146,150,151,155,157,158,165,171,173,175,177,179,180,182,193],To:[0,1,7,14,18,22,29,33,34,36,40,41,45,46,48,49,50,52,54,58,61,62,66,68,74,75,76,82,92,99,100,101,102,104,109,110,111,112,114,116,117,119,121,123,124,127,128,129,131,132,133,134,137,138,139,140,141,142,143,147,148,151,152,154,155,156,158,159,162,165,166,167,168,170,171,172,173,177,178,180,183,186,187,191,193,194,195],WITH:[92,93,171,172],Will:[143,167,173],With:[7,40,41,43,47,50,56,60,61,62,84,99,103,104,108,109,111,112,113,114,118,119,122,123,124,129,140,141,148,150,151,156,158,165,171,180,187,192],_0:151,_1:148,_2:148,_:[18,29,31,33,37,41,51,56,81,126,127,128,130,131,132,133,134,136,137,148,151,158,171,172,182,188,189,194],____:[3,12,22,24,25,46,92,93,97],_____:[24,92],______:[12,14,25],_______:14,________:14,_________:14,_________________________________________________________________:29,____i:93,__abs__: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ompon:[75,101,102,108,118,126,130,137,139,140,141,142,147,151,154,156,165,173,174],components_:[158,186],compos:[36,37,61,74,82,126,127,131,139,140,151,188],compose_greet_func:171,compose_greet_func_with_closur:171,composit:[119,150],compound:[172,179,194],compound_stmt:171,comprehend:49,comprehens:[93,108,123,141,166],compress:[29,30,31,106,126,129,132],compris:[39,78,101,140],compromis:[7,117],comput:[3,7,18,22,29,32,33,36,40,41,43,46,47,49,50,53,54,58,59,66,75,76,77,81,82,84,99,102,103,106,114,116,117,118,121,122,123,126,127,128,129,131,132,135,136,138,139,140,141,143,147,148,150,151,155,156,157,162,165,167,170,172,174,175,176,187,188,189,191,194,195],computation:[33,36,50,56,119,122,129,132,137,153],computationn:33,compute_reciproc:179,compute_target:[9,100],con:[7,47,56,101,112,159],concat:[22,30,36,38,42,54,56,66,128,132,133,134,135,137,162,171,172],concat_axi:121,concat_index:121,concaten:[34,38,55,76,119,121,128,133,135,168,172,182,188,189,190,194],concatenated_str:171,concav:128,conceiv:[142,171],concentr:145,concept:[3,18,29,31,47,50,59,75,76,77,101,102,114,116,118,119,123,127,131,132,138,139,140,141,142,148,151,155,156,163,167,170,172,181,194],conceptu:151,concern:[7,47,54,58,59,74,106,109,112,117,139,140,143,151,165,166,178,191],concis:[119,147,165,171,172,194],conclud:[56,59,75,102,108,116,141,148],conclus:[24,50,103,112,114,166],concret:[143,165,186],concurr:[84,100,101],conda:0,condens:130,condit:[3,22,31,39,40,45,47,48,50,54,93,102,112,119,128,132,141,148,150,151,166,167,171,172,173,193,194],condition2:54,condorcet:147,conduct:[56,100,112,176],conf:18,conf_conv:135,conf_matrix:[52,57],confer:[104,108,121,139,143],confid:[40,41,48,68,80,111,132,141,142,145,147,151],config:[9,38,50,66,134,137,147,150,186,187],configur:[10,41,45,47,99,101,136,139,140,141,165,168,169,171],confirm:[14,30,45,47,48,59,103,106,112,158,166,176,177],conflict:[93,104,112,123],conform:[113,123,139,141],confus:[7,34,40,50,52,57,60,68,80,83,104,117,119,148,150,155,157,162,171],confusingli:158,confusion_matrix:[34,39,40,51,52,57,59,60,68,80,83,84,163,164,167,189,190],confusion_mtx:34,congratul:[100,101,166,167,170,173],conjug:92,conjunct:114,connect:[6,30,32,33,41,43,45,48,62,82,92,93,102,104,112,116,127,132,133,136,141,157,171,172,182],connectionist:78,connor:139,conquer:151,consciou:7,consecut:[14,32,40,49,155],consent:[112,176],consequ:[28,102,112,130,176],conserv:[109,178],conservationstatu:[109,178],consid:[1,3,7,8,11,14,18,22,24,29,36,39,40,41,45,46,49,50,53,56,62,64,75,76,78,93,101,103,104,106,108,113,114,115,116,117,126,127,130,132,134,137,140,141,142,143,145,147,148,150,151,152,155,156,157,158,159,165,167,170,171,172,184,186,193,194],consider:[59,101,106,112,114,115,123,151,154,158,176,177],consist:[0,1,3,8,15,30,33,34,36,41,45,49,50,52,54,59,88,106,112,117,119,126,132,133,139,140,141,142,143,148,157,165,166,168,172,176,177,180,191],consol:123,consolid:75,conspiraci:62,constant:[50,63,65,75,78,119,127,128,130,132,136,141,151,155],constant_initi:127,constantli:114,constrain:[30,130,145],constraint:[30,101,106,119,132,140,141,145,157,163,186],construct:[30,50,119,123,126,128,132,137,139,145,147,150,151,152,155,171,172,194],constructor:[123,132,171,172,193,194],consult:[7,121],consum:[10,20,41,45,98,100,101,103,108,111,112,113,139,141,143,156,159,165,175],consumpt:[108,159,175,179],cont:54,cont_num_var:54,contact:[118,180,187],contagi:167,contain:[1,3,6,7,12,14,15,17,22,29,31,33,36,37,39,40,41,45,46,47,48,49,50,51,52,54,57,59,60,62,68,74,80,83,86,91,92,93,100,101,103,106,113,116,117,118,121,123,124,129,132,133,136,139,140,141,145,147,150,152,155,158,159,162,165,166,167,168,170,171,172,179,193,194],container:140,content:[1,2,3,4,5,7,8,9,10,11,12,13,14,15,16,17,18,19,20,22,23,24,25,26,27,28,29,30,31,32,34,35,36,37,38,39,40,41,42,43,44,46,49,50,51,52,53,54,55,56,59,62,63,64,65,66,67,68,69,71,72,74,80,82,83,84,85,86,88,89,90,91,99,100,101,102,103,104,106,108,109,110,111,112,113,114,116,117,118,119,121,122,123,124,126,127,128,130,131,132,133,134,135,136,137,138,141,143,145,146,147,148,150,151,152,154,155,159,162,163,164,165,166,167,168,170,171,172,173,179,186,187,188,189,192,194],contest:147,context:[9,28,31,50,59,75,78,100,102,104,106,112,122,123,127,133,141,143,145,146,157,158,159,169,171,172,174,176,177,194],contigu:119,contin:[57,58],continu:[0,1,17,18,31,33,34,40,47,48,50,54,55,58,59,62,75,77,78,99,100,102,109,111,116,119,121,124,128,133,140,141,142,143,145,146,150,151,152,157,159,165,167,173,191],contour:[78,156,158,184],contourf:[150,158,189,190],contract:[92,93,101,140,171,172],contradictori:141,contrari:[122,123,143],contrarili:171,contrast:[7,50,78,90,103,119,127,131,141,150,156,182],contrib:136,contribut:[46,52,53,57,58,66,102,119,121,122,123,138,141,147,148,150,171,172,173,174],contributor:138,control:[7,11,30,43,48,56,59,61,62,63,64,65,99,103,104,106,112,114,117,119,129,130,132,134,139,140,142,150,151,157,172,177,187,194],controlflow:171,conv0:135,conv1:[127,133],conv1_1:127,conv1_2:127,conv1_add_bia:127,conv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class=\"section-number\">37. </span>Self-paced assignments","<span class=\"section-number\">37.22. </span>Analyzing COVID-19 papers","<span class=\"section-number\">37.27. </span>Analyzing data","<span class=\"section-number\">37.9. </span>Analyzing text about Data Science","<span class=\"section-number\">37.13. </span>Apply your skills","<span class=\"section-number\">37.16. </span>Build your own custom vis","<span class=\"section-number\">37.17. </span>Classifying datasets","<span class=\"section-number\">37.26. </span>Data preparation","<span class=\"section-number\">37.24. </span>Data processing in Python","<span class=\"section-number\">37.41. </span>Data Science in the cloud: The \u201cAzure ML SDK\u201d way","<span class=\"section-number\">37.40. </span>Data Science project using Azure ML SDK","<span class=\"section-number\">37.10. </span>Data Science scenarios","<span class=\"section-number\">37.20. </span>Displaying airport data","<span class=\"section-number\">37.15. </span>Dive into the beehive","<span class=\"section-number\">37.23. </span>Estimation of COVID-19 pandemic","<span class=\"section-number\">37.25. </span>Evaluating data from a form","<span class=\"section-number\">37.36. </span>Explore a planetary computer dataset","<span class=\"section-number\">37.37. </span>Exploring for answers","<span class=\"section-number\">37.19. </span>Introduction to probability and statistics","<span class=\"section-number\">37.12. </span>Lines, scatters and bars","<span class=\"section-number\">37.39. </span>Low code/no code Data Science project on Azure ML","<span class=\"section-number\">37.38. </span>Market research","<span class=\"section-number\">37.29. </span>Matplotlib applied","<span class=\"section-number\">37.28. </span>NYC taxi data in winter and summer","<span class=\"section-number\">37.18. </span>Small diabetes study","<span class=\"section-number\">37.21. </span>Soda profits","<span class=\"section-number\">37.35. </span>Tell a story","<span class=\"section-number\">37.14. </span>Try it in Excel","<span class=\"section-number\">37.11. </span>Write a data ethics case study","<span class=\"section-number\">37.101. </span>Intro to Autoencoders","<span class=\"section-number\">37.102. </span>Base/Denoising Autoencoder &amp; Dimension Reduction","<span class=\"section-number\">37.103. </span>Fun with Variational Autoencoders","<span class=\"section-number\">37.90. </span>How to choose cnn architecture mnist","<span class=\"section-number\">37.94. </span>Object Recognition in Images using CNN","<span class=\"section-number\">37.92. </span>Sign Language Digits Classification with CNN","<span class=\"section-number\">37.112. </span>DQN On Foreign Exchange Market","<span class=\"section-number\">37.113. </span>Art by gan","<span class=\"section-number\">37.115. </span>Generative Adversarial Networks (GANs)","<span class=\"section-number\">37.97. </span>Bitcoin LSTM Model with Tweet Volume and Sentiment","<span class=\"section-number\">37.107. </span>NN Classify 15 Fruits Assignment","<span class=\"section-number\">37.106. </span>Neural Networks for Classification with TensorFlow","<span class=\"section-number\">37.116. </span>Basic classification: Classify images of clothing","<span class=\"section-number\">37.99. </span>Google Stock Price Prediction RNN","<span class=\"section-number\">37.95. </span>Intro to TensorFlow for Deep Learning","<span class=\"section-number\">37.104. </span>Time Series Forecasting Assignment","<span class=\"section-number\">37.83. </span>Counterintuitive Challenges in ML Debugging","<span class=\"section-number\">37.82. </span>Data engineering","<span class=\"section-number\">37.84. </span>Case Study: Debugging in Classification","<span class=\"section-number\">37.85. </span>Case Study: Debugging in Regression","<span class=\"section-number\">37.75. </span>Beyond random forests: more ensemble models","<span class=\"section-number\">37.76. </span>Decision trees","<span class=\"section-number\">37.80. </span>Random Forest Classifier with Feature Importance","<span class=\"section-number\">37.74. </span>Random forests for classification","<span class=\"section-number\">37.73. </span>Random forests intro and regression","<span class=\"section-number\">37.79. </span>Boosting with tuning","<span class=\"section-number\">37.78. </span>Gradient boosting","<span class=\"section-number\">37.77. </span>Hyperparameter tuning gradient boosting","<span class=\"section-number\">37.67. </span>Decision Trees - Classification","<span class=\"section-number\">37.66. </span>Decision Trees - Intro and Regression","<span class=\"section-number\">37.63. </span>Kernel method assignment 1","<span class=\"section-number\">37.65. </span>Support Vector Machines (SVM) - Classification","<span class=\"section-number\">37.64. </span>Support Vector Machines (SVM) - Intro and SVM for Regression","<span class=\"section-number\">37.70. </span>Dropout and Batch Normalization","<span class=\"section-number\">37.71. </span>Lasso and Ridge Regression","<span class=\"section-number\">37.69. </span>Learning Curve To Identify Overfit &amp; Underfit","<span class=\"section-number\">37.68. </span>Model selection assignment 1","<span class=\"section-number\">37.72. </span>Regularized Linear Models","<span class=\"section-number\">37.88. </span>Build Classification Model","<span class=\"section-number\">37.87. </span>Build classification models","<span class=\"section-number\">37.57. </span>Create a regression model","<span class=\"section-number\">37.61. </span>Delicious asian and indian cuisines","<span class=\"section-number\">37.62. </span>Explore classification methods","<span class=\"section-number\">37.55. </span>Exploring visualizations","<span class=\"section-number\">37.58. </span>Linear and polynomial regression","<span class=\"section-number\">37.45. </span>Linear regression - California Housing","<span class=\"section-number\">37.48. </span>Gradient descent","<span class=\"section-number\">37.49. </span>Linear Regression Implementation from Scratch","<span class=\"section-number\">37.46. </span>Linear Regression Metrics","<span class=\"section-number\">37.47. </span>Loss Function","<span class=\"section-number\">37.54. </span>Managing data","<span class=\"section-number\">37.50. </span>ML logistic regression - assignment 1","<span class=\"section-number\">37.51. </span>ML logistic regression - assignment 2","<span class=\"section-number\">37.52. </span>ML neural network - Assignment 1","<span class=\"section-number\">37.42. </span>Machine Learning overview - assignment 1","<span class=\"section-number\">37.43. </span>Machine Learning overview - assignment 2","<span class=\"section-number\">37.89. </span>Parameter play","<span class=\"section-number\">37.60. </span>Pumpkin varieties and color","<span class=\"section-number\">37.53. </span>Regression tools","<span class=\"section-number\">37.44. </span>Regression with Scikit-learn","<span class=\"section-number\">37.59. </span>Retrying some regression","<span class=\"section-number\">37.86. </span>Study the solvers","<span class=\"section-number\">37.56. </span>Try a different model","<span class=\"section-number\">37.8. </span>Python programming advanced","<span class=\"section-number\">37.7. </span>Python programming basics","<span class=\"section-number\">37.6. </span>Python programming introduction","<span class=\"section-number\">37.5. </span>Project Plan\u200b Template","<span class=\"section-number\">37.3. </span>First assignment","<span class=\"section-number\">37.4. </span>Second assignment","<span class=\"section-number\">8. </span>Data Science in the cloud","<span class=\"section-number\">8.1. </span>Introduction","<span class=\"section-number\">8.3. </span>Data Science in the cloud: The \u201cAzure ML SDK\u201d way","<span class=\"section-number\">8.2. </span>The \u201clow code/no code\u201d way","<span class=\"section-number\">9. </span>Data Science in the real world","<span class=\"section-number\">7.2. </span>Analyzing","<span class=\"section-number\">7.3. </span>Communication","<span class=\"section-number\">7. </span>Data Science lifecycle","<span class=\"section-number\">7.1. </span>Introduction to the Data Science lifecycle","<span class=\"section-number\">6. </span>Data visualization","<span class=\"section-number\">6.4. </span>Making meaningful visualizations","<span class=\"section-number\">6.1. </span>Visualizing distributions","<span class=\"section-number\">6.2. </span>Visualizing proportions","<span class=\"section-number\">6.3. </span>Visualizing relationships: all about honey \ud83c\udf6f","<span class=\"section-number\">4.2. </span>Data Science ethics","<span class=\"section-number\">4.3. </span>Defining data","<span class=\"section-number\">4.1. </span>Defining data science","<span class=\"section-number\">4. </span>Introduction","<span class=\"section-number\">4.4. </span>Introduction to statistics and probability","<span class=\"section-number\">5.5. </span>Data preparation","<span class=\"section-number\">5.2. </span>Non-relational data","<span class=\"section-number\">5.3. </span>NumPy","<span class=\"section-number\">5.4. </span>Pandas","<span class=\"section-number\">5.4.3. </span>Advanced Pandas Techniques","<span class=\"section-number\">5.4.2. </span>Data Selection","<span class=\"section-number\">5.4.1. </span>Introduction and Data Structures","<span class=\"section-number\">5.1. </span>Relational databases","<span class=\"section-number\">5. </span>Working with data","<span class=\"section-number\">24. </span>Autoencoder","<span class=\"section-number\">21. </span>Convolutional Neural Networks","<span class=\"section-number\">30. </span>Diffusion Model","<span class=\"section-number\">20. </span>Intro to Deep Learning","<span class=\"section-number\">27. </span>Deep Q-learning","<span class=\"section-number\">22. </span>Generative adversarial networks","<span class=\"section-number\">28. </span>Image classification","<span class=\"section-number\">29. </span>Image segmentation","<span class=\"section-number\">25. </span>Long-short term memory","<span class=\"section-number\">31. </span>Object detection","<span class=\"section-number\">23. </span>Recurrent Neural Networks","<span class=\"section-number\">26. </span>Time series","Learn AI together, for free","<span class=\"section-number\">34. </span>Data engineering","<span class=\"section-number\">36. </span>Model deployment","<span class=\"section-number\">35. </span>Model training &amp; evaluation","<span class=\"section-number\">32. </span>Overview","<span class=\"section-number\">33. </span>Problem framing","<span class=\"section-number\">14. </span>Clustering models for Machine Learning","<span class=\"section-number\">14.1. </span>Introduction to clustering","<span class=\"section-number\">14.2. </span>K-Means clustering","<span class=\"section-number\">15.1. </span>Bagging","<span class=\"section-number\">15.3. </span>Feature importance","<span class=\"section-number\">15. </span>Getting started with ensemble learning","<span class=\"section-number\">15.2. </span>Random forest","<span class=\"section-number\">16.1. </span>Gradient Boosting","<span class=\"section-number\">16.2. </span>Gradient boosting example","<span class=\"section-number\">16. </span>Introduction to Gradient Boosting","<span class=\"section-number\">16.3. </span>XGBoost","<span class=\"section-number\">16.4. </span>XGBoost + k-fold CV + Feature Importance","<span class=\"section-number\">18. </span>Kernel method","<span class=\"section-number\">19. </span>Model selection","<span class=\"section-number\">17. </span>Unsupervised learning","<span class=\"section-number\">12. </span>Build a web app to use a Machine Learning model","<span class=\"section-number\">13.4. </span>Applied Machine Learning : build a web app","<span class=\"section-number\">13. </span>Getting started with classification","<span class=\"section-number\">13.1. </span>Introduction to classification","<span class=\"section-number\">13.2. </span>More classifiers","<span class=\"section-number\">13.3. </span>Yet other classifiers","<span class=\"section-number\">10. </span>Machine Learning overview","<span class=\"section-number\">11.3. </span>Linear and polynomial regression","<span class=\"section-number\">11.4. </span>Logistic regression","<span class=\"section-number\">11.2. </span>Managing data","<span class=\"section-number\">11. </span>Regression models for Machine Learning","<span class=\"section-number\">11.1. </span>Tools of the trade","<span class=\"section-number\">3. </span>Python programming advanced","<span class=\"section-number\">2. </span>Python programming basics","<span class=\"section-number\">1. </span>Python programming introduction","<span class=\"section-number\">38.10. </span>Data Science in real world","<span class=\"section-number\">38.9. </span>Data Science in the cloud","<span class=\"section-number\">38.4. </span>Data Science introduction","<span class=\"section-number\">38.8. </span>Data Science lifecycle","<span class=\"section-number\">38.7. </span>Data visualization","<span class=\"section-number\">38.6. </span>NumPy and Pandas","<span class=\"section-number\">38.5. </span>Relational vs. non-relational database","<span class=\"section-number\">38.20. </span>Convolutional Neural Network","<span class=\"section-number\">38.21. </span>Generative Adversarial Network","<span class=\"section-number\">38. </span>Slides","<span class=\"section-number\">38.18. </span>Kernel method","<span class=\"section-number\">38.19. </span>Model Selection","<span class=\"section-number\">38.17. </span>Unsupervised learning","<span class=\"section-number\">38.16. </span>Build an machine learning web application","<span class=\"section-number\">38.12. </span>Linear Regression","<span class=\"section-number\">38.13. </span>Logistic Regression","<span class=\"section-number\">38.14. </span>Logistic Regression","<span class=\"section-number\">38.11. </span>Machine Learning overview","<span class=\"section-number\">38.15. </span>Neural Network","<span class=\"section-number\">38.3. </span>Python programming advanced","<span class=\"section-number\">38.2. </span>Python programming basics","<span class=\"section-number\">38.1. </span>Python programming 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5,76,77,78,80,82,85,88,89,90,92,93,99,100,101,102,104,106,108,109,110,112,113,114,116,117,118,119,121,122,123,124,126,127,128,130,131,132,133,135,136,137,139,140,141,142,144,145,146,147,148,150,151,155,156,158,159,162,165,167,168,170,171,172,173,176,177,179,180,182,184,187,188,191,192,193,194,195],AND:[92,93,108,119,122,171,172],AS:[22,25,45,47,48,92,93,171,172],And:[31,32,40,41,43,48,49,50,52,56,58,62,68,74,76,78,80,92,100,102,104,112,116,119,126,129,130,133,136,139,140,141,142,143,147,156,158,168,172,176,180,184,187,194],As:[1,3,7,8,33,34,36,40,41,43,47,48,49,50,51,52,53,54,56,57,58,59,60,61,68,74,78,80,82,83,99,100,106,109,112,114,116,117,119,123,124,132,133,134,139,140,141,147,150,151,155,156,157,158,162,165,166,167,168,171,172,173,177,179,182,191,193,194],At:[28,40,48,50,56,59,68,75,80,106,116,119,124,130,140,141,143,147,151,152,157,165,168,170,171,172,179,191,192],BE:[92,93,171,172],BUT:[92,93,171,172],BY:[101,141,146],Be:[85,91,104,108,119,159],Being:[43,62,101,104,123],But:[33,38,40,43,48,49,50,52,53,56,57,58,59,61,64,68,74,80,100,104,109,112,124,127,129,131,137,139,140,141,143,146,150,151,152,154,156,157,158,165,167,170,171,172,173,184,188],By:[7,18,29,40,41,46,49,52,53,54,57,59,68,74,76,77,78,80,99,109,114,117,119,123,124,128,137,139,140,142,145,146,147,148,155,158,162,165,166,167,171,182],FOR:[92,93,171,172],For:[7,19,29,30,31,32,35,36,38,39,40,41,43,45,46,47,48,49,50,51,54,59,60,61,64,66,67,68,69,71,72,74,75,84,85,86,88,89,90,91,100,101,102,111,112,113,114,116,117,119,121,122,123,126,129,130,131,133,135,137,139,140,141,142,143,145,146,147,148,150,151,152,155,156,157,158,159,162,163,164,165,166,167,168,170,171,172,173,174,184,188,191,192,194],IN:[25,82,92,93,171,172],IS:[22,45,47,48,54,92,93,97,145,171,172],IT:[54,99,140],If:[1,7,14,16,18,29,33,34,35,39,40,41,43,45,48,49,50,51,52,53,54,57,58,59,60,62,64,66,68,69,74,76,77,80,82,92,93,95,100,101,103,104,106,108,109,110,111,112,113,114,116,117,118,119,121,122,123,124,126,128,129,130,131,132,134,136,139,140,141,143,145,147,150,151,152,154,156,157,158,159,162,164,165,166,167,168,170,171,172,173,180,186,188,191,193,194,195],In:[1,3,7,8,9,11,12,13,14,16,18,19,21,24,28,29,30,31,32,33,36,39,40,41,43,45,46,47,48,49,50,52,53,54,57,58,59,60,61,62,64,66,68,69,71,74,75,76,77,78,80,82,83,88,89,90,92,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,121,122,123,124,125,126,127,128,129,130,131,132,133,135,137,139,140,141,142,143,145,146,147,148,150,151,152,153,154,155,156,157,158,159,161,162,163,164,165,166,167,168,169,170,171,172,173,174,177,179,180,181,186,188,189,190,191,192,194,195],Is:[50,93,97,99,103,106,111,112,113,128,140,141,145,159,165,167,168,195],It:[0,1,2,3,4,5,7,8,9,10,11,13,14,15,16,17,18,19,20,23,24,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,46,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,71,72,74,76,77,78,80,82,83,84,85,86,88,89,90,91,99,100,101,102,103,104,106,108,109,110,111,112,113,114,116,117,118,119,121,122,123,127,128,129,130,131,132,133,134,135,136,137,139,140,141,142,143,144,145,146,147,148,150,151,152,153,154,155,156,157,158,159,162,163,164,165,166,168,170,171,172,173,175,176,182,186,187,188,189,191,192,194],Its:[129,150,155,167],NEAR:[61,74],NO:[92,93,171,172],NOT:[82,92,93,119,171,172],Near:[109,178],No:[7,20,29,33,50,54,56,64,82,88,93,98,100,101,110,130,143,147,156,158,163,171,172,175,178],Not:[7,40,43,49,52,54,56,68,80,101,111,117,118,134,151,163,166,167,172,187,194],OF:[22,45,47,48,92,93,171,172],ON:[124,180],ONE:7,OR:[22,45,47,48,92,93,119,171,172],Of:[50,101,102,104,114,158,173,174],On:[49,50,52,57,58,59,60,61,66,68,74,80,101,104,106,141,147,151,154,155,158,159,165,166,170,171,177,187],One:[1,7,11,28,40,43,49,50,52,53,54,55,57,58,59,66,83,98,103,104,106,108,110,114,116,119,127,129,135,141,146,148,150,156,165,166,168,171,172,173,177,178,179,184,186,191,194],Or:[32,40,50,58,74,102,104,119,129,130,141,145,148,165,171,172,187,191,194],Such:[1,7,30,40,43,49,50,54,116,141,142,166,171,193],THAT:82,THE:[92,93,171,172],TO:[54,92,93,136,171,172],That:[31,32,40,43,48,49,50,52,57,61,62,68,74,80,104,109,116,119,124,130,146,148,151,152,158,159,165,167,172,173,191],The:[0,3,5,6,7,8,12,13,14,15,16,18,19,24,25,26,28,29,30,31,32,33,34,35,37,39,40,41,45,46,47,48,49,50,52,54,55,56,57,58,59,60,61,62,63,64,65,66,68,71,74,76,78,80,83,84,90,91,92,93,99,102,103,104,106,108,109,110,112,113,114,115,116,118,119,121,122,123,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,145,146,147,148,150,151,152,153,154,155,156,159,162,163,165,166,167,173,174,176,177,178,182,183,185,186,187,188,189,190,191,192,195],Then:[7,31,45,47,50,56,66,77,82,101,104,116,119,127,128,131,134,136,141,142,143,146,147,148,150,151,155,159,166,170,171,172,173,187],There:[0,1,3,7,18,28,29,30,32,34,36,37,39,40,41,43,46,47,49,50,51,54,57,59,60,62,64,68,72,74,80,85,100,101,104,106,108,109,110,113,114,116,117,118,119,121,124,126,127,129,132,134,136,137,139,140,141,142,143,145,146,148,150,151,154,158,159,161,162,164,165,166,167,169,170,171,172,173,191,192,193,194],These:[7,30,31,39,41,45,49,54,56,59,60,74,75,77,78,99,101,104,106,109,112,113,118,119,122,123,132,140,146,150,151,155,157,158,165,171,173,175,177,179,180,182,193],To:[0,1,7,14,18,22,29,33,34,36,40,41,45,46,48,49,50,52,54,58,61,62,66,68,74,75,76,82,92,99,100,101,102,104,109,110,111,112,114,116,117,119,121,123,124,127,128,129,131,132,133,134,137,138,139,140,141,142,143,147,148,151,152,154,155,156,158,159,162,165,166,167,168,170,171,172,173,177,178,180,183,186,187,191,193,194,195],WITH:[92,93,171,172],Will:[143,167,173],With:[7,40,41,43,47,50,56,60,61,62,84,99,103,104,108,109,111,112,113,114,118,119,122,123,124,129,140,141,148,150,151,156,158,165,171,180,187,192],_0:151,_1:148,_2:148,_:[18,29,31,33,37,41,51,56,81,126,127,128,130,131,132,133,134,136,137,148,151,158,171,172,182,188,189,194],____:[3,12,22,24,25,46,92,93,97],_____:[24,92],______:[12,14,25],_______:14,________:14,_________:14,_________________________________________________________________:29,____i:93,__abs__:92,__add__:92,__all__:171,__annotations__:[171,193],__builtins__:171,__cached__:171,__call__:[63,65,128,133],__class__:[37,171],__dict__:[189,190],__doc__:[171,193],__eq__:92,__file__:[127,171],__finalize__:[121,122],__future__:[37,127],__get__:121,__getitem__:[122,123],__init__:[29,30,31,33,35,36,37,43,55,63,65,81,82,94,128,130,132,133,134,135,152,171,188,189,193],__iter__:33,__len__:33,__loader__:171,__main__:[35,127,130,159],__mul__:92,__name__:[35,37,127,130,159,171],__operators__:134,__package__:171,__repr__:55,__spec__:171,__str__:92,__sub__:92,__truediv__:92,__version__:[41,158,193],_aspp:133,_attach:[118,180],_bin:54,_branch:133,_build_model:35,_bunch:[57,58],_caller:121,_check_indexing_error:123,_check_params_vs_input:146,_concaten:121,_consolidate_inplac:122,_constructor:122,_conv_block:133,_conv_bn_relu:133,_conv_relu:133,_data:122,_deeplabv3:133,_deprecate_mismatched_index:122,_deprecated_arg:122,_engin:123,_etag:[118,180],_fcn_16:133,_fcn_32:133,_fcn_8:133,_fuse_bn_tensor:132,_get_axi:122,_get_block_manager_axi:122,_get_comb_axi:121,_get_concat_axi:121,_get_join_info:121,_get_list_axi:122,_get_result_dim:121,_get_slice_axi:122,_get_valu:[122,123],_get_values_for_loc:123,_getbool_axi:122,_getitem_axi:122,_getitem_lowerdim:122,_getitem_tupl:122,_i:[77,78,148,158],_identity_block:133,_ilocindex:122,_index:56,_indicator_pre_merg:121,_info_axi:121,_invalid_index:122,_is_copi:122,_is_scalar_access:122,_items_overlap_with_suffix:121,_j:[148,158],_k:130,_kmean:146,_label:57,_left:121,_lib:[121,123],_locationindex:122,_locindex:122,_m:130,_make_concat_multiindex:121,_make_stag:132,_maybe_cast_for_get_loc:122,_maybe_cast_slice_bound:122,_maybe_check_integr:121,_merge_doc:121,_merge_typ:121,_mergeoper:121,_method:179,_mgr:[121,122],_novalu:179,_other:121,_pad_1x1_to_3x3_tensor:132,_pickl:127,_recognized_scalar:122,_reindex_and_concat:121,_rid:[118,180],_right:121,_sec_1:93,_segnet:133,_self:[118,180],_sigmoid:[81,189],_skip:3,_slice:122,_static:[122,123],_subplot:74,_sum:179,_t:[118,180],_t_sne:186,_take:122,_take_with_is_copi:122,_takeabl:122,_valid_typ:122,_validate_integ:122,_validate_kei:122,_validate_tuple_index:122,_valu:123,a0958ad901d7:118,a0:[121,179],a10:123,a1:[119,121,179],a1gkdhua8we2lilmxcctgfiycqfttwx6tljchvsbz6sfau8wquo8541xaz2myyziork:59,a21453:172,a23:[171,193],a2:[119,121,179],a3:121,a3z5kdkfn3tbq:59,a4:121,a5:121,a7yia1n5fo6efhugqfis3dhueyjsa:59,a_:82,a_dict:172,a_i:[82,147],a_list:172,a_n:150,aaaaaa:[156,184],aafter:157,aaron:[29,50,78,131],ab:[50,63,65,77,92,93,122,127,128,135,158,171,172,194],abadi:131,abbeel:128,abbrevi:[124,128],abc:[93,122,123,172,179,195],abcd:[7,117,122,179],abcdef:122,abcmous:[112,176],abhinav:[139,143],abil:[43,52,54,68,76,77,80,108,129,139,140,150,156,159,165,171,173,184,191],abl:[3,7,10,11,14,16,20,31,40,49,50,52,53,54,57,61,62,74,76,101,104,110,112,114,118,119,123,129,137,140,142,145,151,154,157,159,162,165,166,167,168,170,176,186,189,190,193],abnorm:29,abnorml:66,abo:38,aboslut:157,about:[1,4,7,11,12,13,15,16,17,18,19,22,23,26,28,29,31,40,41,43,46,47,48,49,50,52,53,54,57,58,59,60,61,62,66,68,77,78,80,83,84,90,99,100,101,102,103,104,106,108,109,110,112,114,115,116,117,118,119,121,123,124,125,129,130,133,135,137,138,139,140,141,142,143,144,145,146,147,150,151,152,154,155,157,158,159,161,163,164,165,166,168,169,170,171,172,173,174,176,177,180,188,191,193,195],abov:[0,1,7,11,14,19,26,29,32,36,40,43,45,46,47,48,49,50,51,52,53,54,57,58,59,60,64,66,68,74,80,92,93,104,108,110,114,116,119,121,122,123,124,127,128,129,130,132,133,135,137,139,140,141,142,145,146,147,148,150,154,155,157,158,162,165,166,167,168,169,170,171,172,173,178,187,188],above_cutoff:158,abracadabra:172,abraham:195,abs_vector:[172,194],absenc:[54,186],absent:122,absolut:[47,76,78,83,92,116,119,141,150,154,157,171,172,173,194],absolute_error:77,absolute_import:127,absolute_percentage_error:77,abspath:127,abund:[110,178],ac:158,academ:[112,115,138,176],academi:193,acc:[33,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163,171],compat:[15,55,101,119,122,130,131,134,136,138],compatible_format:193,compel:78,compens:[155,158],compet:151,competit:[129,137,143,151,154,155],compexifi:50,compil:[1,7,29,30,32,34,35,38,39,42,44,45,47,48,62,133,140,173,179,182],compilaton:40,complaint:[104,112,176],compleletli:[68,80],complementari:137,complet:[1,8,21,24,34,40,41,49,50,52,53,56,57,58,62,68,69,75,80,101,106,108,112,114,116,119,121,123,127,128,132,135,136,137,139,141,143,155,158,165,166,167,168,171,172,173,175,186,193,194],complex32:119,complex:[0,1,31,32,33,49,57,58,60,61,63,64,65,66,68,76,78,80,111,114,119,124,129,132,135,137,138,139,140,141,143,147,153,156,157,158,162,165,171,173,181,182,184,185,192,195],complex_numb:171,complex_number_1:[172,194],complex_number_2:[172,194],complex_number__1:194,complexnumb:171,complexnumberwithconstructor:171,compli:112,complianc:[22,45,47,48,112,176],compliant:112,complic:[36,50,82,114,119,139,140,141,151,154,157,158,168,182],compon:[75,76,101,102,108,118,126,130,137,139,140,141,142,147,151,154,156,165,173,174],components_:[158,186],compos:[36,37,61,74,82,126,127,131,139,140,151,188],compose_greet_func:171,compose_greet_func_with_closur:171,composit:[119,150],compound:[172,179,194],compound_stmt:171,comprehend:49,comprehens:[93,108,123,141,166],compress:[29,30,31,106,126,129,132],compris:[39,101,140],compromis:[7,117],comput:[3,7,18,22,29,32,33,36,40,41,43,46,47,49,50,53,54,58,59,66,75,76,77,81,82,84,99,102,103,106,114,116,117,118,121,122,123,126,127,128,129,131,132,135,136,138,139,140,141,143,147,148,150,151,155,156,157,162,165,167,170,172,174,175,176,187,188,189,191,194,195],computation:[33,36,50,56,119,122,129,132,137,153],computationn:33,compute_reciproc:179,compute_target:[9,100],con:[7,47,56,101,112,159],concat:[22,30,36,38,42,54,56,66,128,132,133,134,135,137,162,171,172],concat_axi:121,concat_index:121,concaten:[34,38,55,76,119,121,128,133,135,168,172,182,188,189,190,194],concatenated_str:171,concav:128,conceiv:[142,171],concentr:145,concept:[3,18,29,31,47,50,59,75,76,77,101,102,114,116,118,119,123,127,131,132,138,139,140,141,142,148,151,155,156,163,167,170,172,181,194],conceptu:151,concern:[7,47,54,58,59,74,106,109,112,117,139,140,143,151,165,166,178,191],concis:[119,147,165,171,172,194],conclud:[56,59,75,102,108,116,141,148],conclus:[24,50,103,112,114,166],concret:[143,165,186],concurr:[84,100,101],conda:0,condens:130,condit:[3,22,31,39,40,45,47,48,50,54,93,102,112,119,128,132,141,148,150,151,166,167,171,172,173,193,194],condition2:54,condorcet:147,conduct:[56,100,112,176],conf:18,conf_conv:135,conf_matrix:[52,57],confer:[104,108,121,139,143],confid:[40,41,48,68,80,111,132,141,142,145,147,151],config:[9,38,50,66,134,137,147,150,186,187],configur:[10,41,45,47,99,101,136,139,140,141,165,168,169,171],confirm:[14,30,45,47,48,59,103,106,112,158,166,176,177],conflict:[93,104,112,123],conform:[113,123,139,141],confus:[7,34,40,50,52,57,60,68,80,83,104,117,119,148,150,155,157,162,171],confusingli:158,confusion_matrix:[34,39,40,51,52,57,59,60,68,80,83,84,163,164,167,189,190],confusion_mtx:34,congratul:[100,101,166,167,170,173],conjug:92,conjunct:114,connect:[6,30,32,33,41,43,45,48,62,82,92,93,102,104,112,116,127,132,133,136,141,157,171,172,182],connectionist:78,connor:139,conquer:151,consciou:7,consecut:[14,32,40,49,155],consent:[112,176],consequ:[28,102,112,130,176],conserv:[109,178],conservationstatu:[109,178],consid:[1,3,7,8,11,14,18,22,24,29,36,39,40,41,45,46,49,50,53,56,62,64,75,76,93,101,103,104,106,108,113,114,115,116,117,126,127,130,132,134,137,140,141,142,143,145,147,148,150,151,152,155,156,157,158,159,165,167,170,171,172,184,186,193,194],consider:[59,101,106,112,114,115,123,151,154,158,176,177],consist:[0,1,3,8,15,30,33,34,36,41,45,49,50,52,54,59,88,106,112,117,119,126,132,133,139,140,141,142,143,148,157,165,166,168,172,176,177,180,191],consol:123,consolid:75,conspiraci:62,constant:[50,63,65,75,78,119,127,128,130,132,136,141,151,155],constant_initi:127,constantli:114,constrain:[30,130,145],constraint:[30,101,106,119,132,140,141,145,157,163,186],construct:[30,50,119,123,126,128,132,137,139,145,147,150,151,152,155,171,172,194],constructor:[123,132,171,172,193,194],consult:[7,121],consum:[10,20,41,45,98,100,101,103,108,111,112,113,139,141,143,156,159,165,175],consumpt:[108,159,175,179],cont:54,cont_num_var:54,contact:[118,180,187],contagi:167,contain:[1,3,6,7,12,14,15,17,22,29,31,33,36,37,39,40,41,45,46,47,48,49,50,51,52,54,57,59,60,62,68,74,80,83,86,91,92,93,100,101,103,106,113,116,117,118,121,123,124,129,132,133,136,139,140,141,145,147,150,152,155,158,159,162,165,166,167,168,170,171,172,179,193,194],container:140,content:[1,2,3,4,5,7,8,9,10,11,12,13,14,15,16,17,18,19,20,22,23,24,25,26,27,28,29,30,31,32,34,35,36,37,38,39,40,41,42,43,44,46,49,50,51,52,53,54,55,56,59,62,63,64,65,66,67,68,69,71,72,74,80,82,83,84,85,86,88,89,90,91,99,100,101,102,103,104,106,108,109,110,111,112,113,114,116,117,118,119,121,122,123,124,126,127,128,130,131,132,133,134,135,136,137,138,141,143,145,146,147,148,150,1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class=\"section-number\">37. </span>Self-paced assignments","<span class=\"section-number\">37.22. </span>Analyzing COVID-19 papers","<span class=\"section-number\">37.27. </span>Analyzing data","<span class=\"section-number\">37.9. </span>Analyzing text about Data Science","<span class=\"section-number\">37.13. </span>Apply your skills","<span class=\"section-number\">37.16. </span>Build your own custom vis","<span class=\"section-number\">37.17. </span>Classifying datasets","<span class=\"section-number\">37.26. </span>Data preparation","<span class=\"section-number\">37.24. </span>Data processing in Python","<span class=\"section-number\">37.41. </span>Data Science in the cloud: The \u201cAzure ML SDK\u201d way","<span class=\"section-number\">37.40. </span>Data Science project using Azure ML SDK","<span class=\"section-number\">37.10. </span>Data Science scenarios","<span class=\"section-number\">37.20. </span>Displaying airport data","<span class=\"section-number\">37.15. </span>Dive into the beehive","<span class=\"section-number\">37.23. </span>Estimation of COVID-19 pandemic","<span class=\"section-number\">37.25. </span>Evaluating data from a form","<span class=\"section-number\">37.36. </span>Explore a planetary computer dataset","<span class=\"section-number\">37.37. </span>Exploring for answers","<span class=\"section-number\">37.19. </span>Introduction to probability and statistics","<span class=\"section-number\">37.12. </span>Lines, scatters and bars","<span class=\"section-number\">37.39. </span>Low code/no code Data Science project on Azure ML","<span class=\"section-number\">37.38. </span>Market research","<span class=\"section-number\">37.29. </span>Matplotlib applied","<span class=\"section-number\">37.28. </span>NYC taxi data in winter and summer","<span class=\"section-number\">37.18. </span>Small diabetes study","<span class=\"section-number\">37.21. </span>Soda profits","<span class=\"section-number\">37.35. </span>Tell a story","<span class=\"section-number\">37.14. </span>Try it in Excel","<span class=\"section-number\">37.11. </span>Write a data ethics case study","<span class=\"section-number\">37.101. </span>Intro to Autoencoders","<span class=\"section-number\">37.102. </span>Base/Denoising Autoencoder &amp; Dimension Reduction","<span class=\"section-number\">37.103. </span>Fun with Variational Autoencoders","<span class=\"section-number\">37.90. </span>How to choose cnn architecture mnist","<span class=\"section-number\">37.94. </span>Object Recognition in Images using CNN","<span class=\"section-number\">37.92. </span>Sign Language Digits Classification with CNN","<span class=\"section-number\">37.112. </span>DQN On Foreign Exchange Market","<span class=\"section-number\">37.113. </span>Art by gan","<span class=\"section-number\">37.115. </span>Generative Adversarial Networks (GANs)","<span class=\"section-number\">37.97. </span>Bitcoin LSTM Model with Tweet Volume and Sentiment","<span class=\"section-number\">37.107. </span>NN Classify 15 Fruits Assignment","<span class=\"section-number\">37.106. </span>Neural Networks for Classification with TensorFlow","<span class=\"section-number\">37.116. </span>Basic classification: Classify images of clothing","<span class=\"section-number\">37.99. </span>Google Stock Price Prediction RNN","<span class=\"section-number\">37.95. </span>Intro to TensorFlow for Deep Learning","<span class=\"section-number\">37.104. </span>Time Series Forecasting Assignment","<span class=\"section-number\">37.83. </span>Counterintuitive Challenges in ML Debugging","<span class=\"section-number\">37.82. </span>Data engineering","<span class=\"section-number\">37.84. </span>Case Study: Debugging in Classification","<span class=\"section-number\">37.85. </span>Case Study: Debugging in Regression","<span class=\"section-number\">37.75. </span>Beyond random forests: more ensemble models","<span class=\"section-number\">37.76. </span>Decision trees","<span class=\"section-number\">37.80. </span>Random Forest Classifier with Feature Importance","<span class=\"section-number\">37.74. </span>Random forests for classification","<span class=\"section-number\">37.73. </span>Random forests intro and regression","<span class=\"section-number\">37.79. </span>Boosting with tuning","<span class=\"section-number\">37.78. </span>Gradient boosting","<span class=\"section-number\">37.77. </span>Hyperparameter tuning gradient boosting","<span class=\"section-number\">37.67. </span>Decision Trees - Classification","<span class=\"section-number\">37.66. </span>Decision Trees - Intro and Regression","<span class=\"section-number\">37.63. </span>Kernel method assignment 1","<span class=\"section-number\">37.65. </span>Support Vector Machines (SVM) - Classification","<span class=\"section-number\">37.64. </span>Support Vector Machines (SVM) - Intro and SVM for Regression","<span class=\"section-number\">37.70. </span>Dropout and Batch Normalization","<span class=\"section-number\">37.71. </span>Lasso and Ridge Regression","<span class=\"section-number\">37.69. </span>Learning Curve To Identify Overfit &amp; Underfit","<span class=\"section-number\">37.68. </span>Model selection assignment 1","<span class=\"section-number\">37.72. </span>Regularized Linear Models","<span class=\"section-number\">37.88. </span>Build Classification Model","<span class=\"section-number\">37.87. </span>Build classification models","<span class=\"section-number\">37.57. </span>Create a regression model","<span class=\"section-number\">37.61. </span>Delicious asian and indian cuisines","<span class=\"section-number\">37.62. </span>Explore classification methods","<span class=\"section-number\">37.55. </span>Exploring visualizations","<span class=\"section-number\">37.58. </span>Linear and polynomial regression","<span class=\"section-number\">37.45. </span>Linear regression - California Housing","<span class=\"section-number\">37.48. </span>Gradient descent","<span class=\"section-number\">37.49. </span>Linear Regression Implementation from Scratch","<span class=\"section-number\">37.46. </span>Linear Regression Metrics","<span class=\"section-number\">37.47. </span>Loss Function","<span class=\"section-number\">37.54. </span>Managing data","<span class=\"section-number\">37.50. </span>ML logistic regression - assignment 1","<span class=\"section-number\">37.51. </span>ML logistic regression - assignment 2","<span class=\"section-number\">37.52. </span>ML neural network - Assignment 1","<span class=\"section-number\">37.42. </span>Machine Learning overview - assignment 1","<span class=\"section-number\">37.43. </span>Machine Learning overview - assignment 2","<span class=\"section-number\">37.89. </span>Parameter play","<span class=\"section-number\">37.60. </span>Pumpkin varieties and color","<span class=\"section-number\">37.53. </span>Regression tools","<span class=\"section-number\">37.44. </span>Regression with Scikit-learn","<span class=\"section-number\">37.59. </span>Retrying some regression","<span class=\"section-number\">37.86. </span>Study the solvers","<span class=\"section-number\">37.56. </span>Try a different model","<span class=\"section-number\">37.8. </span>Python programming advanced","<span class=\"section-number\">37.7. </span>Python programming basics","<span class=\"section-number\">37.6. </span>Python programming introduction","<span class=\"section-number\">37.5. </span>Project Plan\u200b Template","<span class=\"section-number\">37.3. </span>First assignment","<span class=\"section-number\">37.4. </span>Second assignment","<span class=\"section-number\">8. </span>Data Science in the cloud","<span class=\"section-number\">8.1. </span>Introduction","<span class=\"section-number\">8.3. </span>Data Science in the cloud: The \u201cAzure ML SDK\u201d way","<span class=\"section-number\">8.2. </span>The \u201clow code/no code\u201d way","<span class=\"section-number\">9. </span>Data Science in the real world","<span class=\"section-number\">7.2. </span>Analyzing","<span class=\"section-number\">7.3. </span>Communication","<span class=\"section-number\">7. </span>Data Science lifecycle","<span class=\"section-number\">7.1. </span>Introduction to the Data Science lifecycle","<span class=\"section-number\">6. </span>Data visualization","<span class=\"section-number\">6.4. </span>Making meaningful visualizations","<span class=\"section-number\">6.1. </span>Visualizing distributions","<span class=\"section-number\">6.2. </span>Visualizing proportions","<span class=\"section-number\">6.3. </span>Visualizing relationships: all about honey \ud83c\udf6f","<span class=\"section-number\">4.2. </span>Data Science ethics","<span class=\"section-number\">4.3. </span>Defining data","<span class=\"section-number\">4.1. </span>Defining data science","<span class=\"section-number\">4. </span>Introduction","<span class=\"section-number\">4.4. </span>Introduction to statistics and probability","<span class=\"section-number\">5.5. </span>Data preparation","<span class=\"section-number\">5.2. </span>Non-relational data","<span class=\"section-number\">5.3. </span>NumPy","<span class=\"section-number\">5.4. </span>Pandas","<span class=\"section-number\">5.4.3. </span>Advanced Pandas Techniques","<span class=\"section-number\">5.4.2. </span>Data Selection","<span class=\"section-number\">5.4.1. </span>Introduction and Data Structures","<span class=\"section-number\">5.1. </span>Relational databases","<span class=\"section-number\">5. </span>Working with data","<span class=\"section-number\">24. </span>Autoencoder","<span class=\"section-number\">21. </span>Convolutional Neural Networks","<span class=\"section-number\">30. </span>Diffusion Model","<span class=\"section-number\">20. </span>Intro to Deep Learning","<span class=\"section-number\">27. </span>Deep Q-learning","<span class=\"section-number\">22. </span>Generative adversarial networks","<span class=\"section-number\">28. </span>Image classification","<span class=\"section-number\">29. </span>Image segmentation","<span class=\"section-number\">25. </span>Long-short term memory","<span class=\"section-number\">31. </span>Object detection","<span class=\"section-number\">23. </span>Recurrent Neural Networks","<span class=\"section-number\">26. </span>Time series","Learn AI together, for free","<span class=\"section-number\">34. </span>Data engineering","<span class=\"section-number\">36. </span>Model deployment","<span class=\"section-number\">35. </span>Model training &amp; evaluation","<span class=\"section-number\">32. </span>Overview","<span class=\"section-number\">33. </span>Problem framing","<span class=\"section-number\">14. </span>Clustering models for Machine Learning","<span class=\"section-number\">14.1. </span>Introduction to clustering","<span class=\"section-number\">14.2. </span>K-Means clustering","<span class=\"section-number\">15.1. </span>Bagging","<span class=\"section-number\">15.3. </span>Feature importance","<span class=\"section-number\">15. </span>Getting started with ensemble learning","<span class=\"section-number\">15.2. </span>Random forest","<span class=\"section-number\">16.1. </span>Gradient Boosting","<span class=\"section-number\">16.2. </span>Gradient boosting example","<span class=\"section-number\">16. </span>Introduction to Gradient Boosting","<span class=\"section-number\">16.3. </span>XGBoost","<span class=\"section-number\">16.4. </span>XGBoost + k-fold CV + Feature Importance","<span class=\"section-number\">18. </span>Kernel method","<span class=\"section-number\">19. </span>Model selection","<span class=\"section-number\">17. </span>Unsupervised learning","<span class=\"section-number\">12. </span>Build a web app to use a Machine Learning model","<span class=\"section-number\">13.4. </span>Applied Machine Learning : build a web app","<span class=\"section-number\">13. </span>Getting started with classification","<span class=\"section-number\">13.1. </span>Introduction to classification","<span class=\"section-number\">13.2. </span>More classifiers","<span class=\"section-number\">13.3. </span>Yet other classifiers","<span class=\"section-number\">10. </span>Machine Learning overview","<span class=\"section-number\">11.3. </span>Linear and polynomial regression","<span class=\"section-number\">11.4. </span>Logistic regression","<span class=\"section-number\">11.2. </span>Managing data","<span class=\"section-number\">11. </span>Regression models for Machine Learning","<span class=\"section-number\">11.1. </span>Tools of the trade","<span class=\"section-number\">3. </span>Python programming advanced","<span class=\"section-number\">2. </span>Python programming basics","<span class=\"section-number\">1. </span>Python programming introduction","<span class=\"section-number\">38.10. </span>Data Science in real world","<span class=\"section-number\">38.9. </span>Data Science in the cloud","<span class=\"section-number\">38.4. </span>Data Science introduction","<span class=\"section-number\">38.8. </span>Data Science lifecycle","<span class=\"section-number\">38.7. </span>Data visualization","<span class=\"section-number\">38.6. </span>NumPy and Pandas","<span class=\"section-number\">38.5. </span>Relational vs. non-relational database","<span class=\"section-number\">38.20. </span>Convolutional Neural Network","<span class=\"section-number\">38.21. </span>Generative Adversarial Network","<span class=\"section-number\">38. </span>Slides","<span class=\"section-number\">38.18. </span>Kernel method","<span class=\"section-number\">38.19. </span>Model Selection","<span class=\"section-number\">38.17. </span>Unsupervised learning","<span class=\"section-number\">38.16. </span>Build an machine learning web application","<span class=\"section-number\">38.12. </span>Linear Regression","<span class=\"section-number\">38.13. </span>Logistic Regression","<span class=\"section-number\">38.14. </span>Logistic Regression","<span class=\"section-number\">38.11. </span>Machine Learning overview","<span class=\"section-number\">38.15. </span>Neural Network","<span class=\"section-number\">38.3. </span>Python programming advanced","<span class=\"section-number\">38.2. </span>Python programming basics","<span class=\"section-number\">38.1. </span>Python programming introduction"],titleterms:{"0":59,"1":[3,24,30,32,43,49,50,52,53,54,56,57,58,59,60,61,65,68,74,75,80,82,83,104,113,119,132,174,175,176,177,178,179,180,181,182,184,188,190,194,195],"10":[40,56,127,132],"100":[51,59,132],"1000":[59,132],"11":56,"12":56,"13":56,"15":39,"19":[1,8,14],"1d":119,"2":[3,24,30,32,43,44,49,50,51,52,53,54,56,57,58,60,61,68,75,80,81,84,104,113,119,174,175,176,177,178,179,180,181,182,184,188,190,194,195],"2d":[30,119,184],"3":[3,24,32,39,43,49,50,52,53,54,56,57,58,60,61,68,74,80,104,119,174,175,176,177,178,179,181,182,188,194,195],"3d":[30,83,108,184],"4":[3,24,32,43,49,50,51,52,53,54,56,57,58,60,61,68,80,104,174,175,176,177,178,179,180,194,195],"5":[24,32,43,49,50,53,54,56,57,58,60,61,68,80,83,104,132,174,175,176,177,178,180,194,195],"50":56,"500":56,"6":[43,50,52,53,54,56,57,58,60,61,68,80,174,175,176,177,178],"7":[43,50,52,53,56,57,58,60,61,174,177,178],"8":[52,56,177],"9":56,"boolean":[119,172,173,194,195],"break":[92,171,193],"case":[28,45,47,48,50,104,112,168,184],"class":[35,39,47,50,51,63,65,92,171,193],"default":[51,59,171],"do":[47,114,167,168,172,176,184],"final":[49,74,75],"float":[7,172,194],"function":[43,51,59,75,76,78,82,92,93,119,129,130,151,171,173,179,189,190,192,193],"import":[9,29,33,37,41,44,49,51,52,53,54,56,57,58,59,60,61,64,66,83,117,128,141,148,155,170,171,188,189,190,192],"long":134,"new":[56,94,119,123,189,190],"null":[7,59,179],"public":37,"return":[94,130,171],"short":134,"true":59,"try":[0,27,48,68,75,80,91,171,193],"while":[92,171,193],A:[31,129,157,163,164,166],And:165,At:46,But:168,By:151,For:92,Is:143,It:[124,167,180],NOT:167,Not:165,On:35,One:[74,75,192],That:189,The:[9,36,43,51,53,75,77,82,100,101,117,124,157,158,164,168,170,171,172,175,179,180,181,184,193,194],There:168,To:[64,157],With:[34,137],about:[3,33,111,162,167,178],absolut:77,acceler:158,access:[93,119,123],accuraci:[32,41,47,59,132,155,192],acknowledg:[1,2,3,4,5,7,8,9,10,11,12,13,14,15,16,17,18,19,20,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,46,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,71,72,74,80,82,83,84,85,86,88,89,90,91,92,93,99,100,101,102,103,104,106,108,109,110,111,112,113,114,116,117,118,119,121,122,123,124,126,127,128,130,131,132,133,134,135,136,137,138,141,145,146,147,148,150,151,152,154,155,156,159,162,163,164,165,166,167,168,170,171,172,173,181,184,186,187,188,189,192],ackowledg:182,action:130,activ:129,actual:104,ad:[30,31,47,62,66,119,157],adaboost:49,adam:192,add:[48,119,141],addit:123,advanc:[32,92,119,121,171,193],adversari:[37,131,182],after:[30,157,185],ag:24,against:48,agent:[35,130],agglom:158,aggreg:[119,121,176,179],ahead:82,ai:[36,41,138,186],airport:12,aka:64,algorithm:[50,54,130,142,145,150,151,155,158],align:123,all:[24,47,74,111,124,166,178,180],alpha:128,an:[9,31,64,66,100,101,104,118,119,159,181,187],anagram:172,analysi:[8,38,49,51,52,53,54,56,57,58,59,60,61,74,77,99,103,165,186,191],analyz:[1,2,3,18,103,119,168,177],anchor:[39,51],anim:108,ann:44,annot:[171,193],anomal:47,anomali:29,anoth:37,answer:17,api:[34,43],app:[159,160],append:[93,179],appli:[4,22,47,112,160,163,164,176],applic:[50,126,165,187,191],approach:[51,104,124,163,180],ar:172,arbitrari:171,architectur:[32,127,129,181,192],argument:[171,193],arithmet:[123,172,194],arrai:[119,123,179],art:36,artifici:[165,191],artwork:36,ascend:[93,94],ascent:131,asian:70,ask:168,assert:51,assign:[0,12,25,39,44,59,65,80,81,82,83,84,96,97,119,121,123,190],assist:133,attribut:[59,119,123,179],auc:[56,59],audienc:104,augment:[32,34,128],author:170,autoencod:[29,30,31,126],automl:[9,100,101,141],avail:119,averag:[92,168],avoid:[108,157],axi:119,azur:[9,10,20,100,101,118],b:75,babylonian:93,background:128,backprop:82,bag:[49,147,150,155],balanc:162,bar:[19,22,178],base:[30,92,122,155],basebal:18,baselin:[47,48,165,191],basi:59,basic:[29,32,41,43,93,112,119,123,130,172,173,176,179,194,195],batch:[33,62,158],beehiv:13,begin:104,behind:55,best:[9,32,37,100,143],beta:128,better:[163,167],between:[24,54,56,68,74,80,119,150,176,179],beyond:[40,49,156,184],bi:54,bia:[64,150,157,185],bibliographi:25,big:192,binai:54,binari:[40,167],binder:0,bird:[109,178],bit:[31,119],bitcoin:38,blend:54,bmi:24,boost:[49,54,55,56,151,152,153,155],bootstrap:147,bound:128,boundari:[156,158,184],boxplot:[24,83],bp:24,brain:181,broadcast:[119,179],bug:48,build:[5,29,31,32,36,41,50,51,67,68,80,108,146,159,160,166,167,168,178,187],c:59,cach:187,calcul:[47,92,93,172],california:74,call:193,callabl:122,callback:40,can:[50,56,114,176],candid:56,capac:157,captur:[106,177],cardin:51,cast:[172,194],catalog:139,catboost:54,categor:[7,51,54,57,68,74,80,166,188],categori:93,categorical_crossentropi:192,caus:45,central:[18,116],centroid:158,chain:123,challeng:[1,14,22,45,112,119,129,132,135,176],chang:[34,54],changin:34,channel:104,chart:[108,111,178,187],check:[30,47,48,51,53,57,59,68,74,80,141,155,168,172,195],checkbox:187,checklist:112,choic:[123,143],choos:[32,50,74,99,101,108,163,175],churn:50,cifar:[127,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