diff --git a/_images/gradient-descent_19_1.png b/_images/gradient-descent_19_1.png
index 36cd0b2bae..eeea6f4f3e 100644
Binary files a/_images/gradient-descent_19_1.png and b/_images/gradient-descent_19_1.png differ
diff --git a/_images/kernel-method_32_0.png b/_images/kernel-method_32_0.png
index 9620e2f567..f014020bf0 100644
Binary files a/_images/kernel-method_32_0.png and b/_images/kernel-method_32_0.png differ
diff --git a/_images/kernel-method_39_1.png b/_images/kernel-method_39_1.png
index 52ec2b26d0..2909af98e7 100644
Binary files a/_images/kernel-method_39_1.png and b/_images/kernel-method_39_1.png differ
diff --git a/_images/tools-of-the-trade_29_0.png b/_images/tools-of-the-trade_29_0.png
index 07bada16db..8fb2c5fcc8 100644
Binary files a/_images/tools-of-the-trade_29_0.png and b/_images/tools-of-the-trade_29_0.png differ
diff --git a/_images/visualization-relationships_12_0.png b/_images/visualization-relationships_12_0.png
index 7a6cde5af7..aab7339178 100644
Binary files a/_images/visualization-relationships_12_0.png and b/_images/visualization-relationships_12_0.png differ
diff --git a/_images/visualization-relationships_16_0.png b/_images/visualization-relationships_16_0.png
index 95c0e7590b..cccad5bbd4 100644
Binary files a/_images/visualization-relationships_16_0.png and b/_images/visualization-relationships_16_0.png differ
diff --git a/_images/visualization-relationships_18_1.png b/_images/visualization-relationships_18_1.png
index b56a1cc81b..89a19a5f8b 100644
Binary files a/_images/visualization-relationships_18_1.png and b/_images/visualization-relationships_18_1.png differ
diff --git a/_images/visualization-relationships_20_0.png b/_images/visualization-relationships_20_0.png
index b2c3086f63..17ce1cb7fc 100644
Binary files a/_images/visualization-relationships_20_0.png and b/_images/visualization-relationships_20_0.png differ
diff --git a/_sources/assignments/data-science/analyzing-COVID-19-papers.ipynb b/_sources/assignments/data-science/analyzing-COVID-19-papers.ipynb
index 36b55df157..4b9a6dbfd3 100644
--- a/_sources/assignments/data-science/analyzing-COVID-19-papers.ipynb
+++ b/_sources/assignments/data-science/analyzing-COVID-19-papers.ipynb
@@ -43,7 +43,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "df = pd.read_csv(\"FILE_PATH_TO_metadata.csv\")\n",
+    "df = pd.read_csv(\n",
+    "    \"https://static-1300131294.cos.ap-shanghai.myqcloud.com/data/data-science/metadata.csv\"\n",
+    ")\n",
     "df.head()"
    ]
   },
@@ -402,7 +404,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.7"
+   "version": "3.7.16"
   },
   "vscode": {
    "interpreter": {
diff --git a/_sources/assignments/data-science/analyzing-data.ipynb b/_sources/assignments/data-science/analyzing-data.ipynb
index e248754798..0c2e324d92 100644
--- a/_sources/assignments/data-science/analyzing-data.ipynb
+++ b/_sources/assignments/data-science/analyzing-data.ipynb
@@ -17,8 +17,8 @@
     "import pandas as pd\n",
     "import glob\n",
     "\n",
-    "#Loading the dataset\n",
-    "path = '../../data/emails.csv'\n",
+    "# Loading the dataset\n",
+    "path = \"https://static-1300131294.cos.ap-shanghai.myqcloud.com/data/data-science/emails.csv\"\n",
     "email_df = pd.read_csv(path)"
    ]
   },
diff --git a/_sources/assignments/deep-learning/tensorflow/intro_to_tensorflow_for_deeplearning.ipynb b/_sources/assignments/deep-learning/tensorflow/intro_to_tensorflow_for_deeplearning.ipynb
index a596cf5226..3f9701952c 100644
--- a/_sources/assignments/deep-learning/tensorflow/intro_to_tensorflow_for_deeplearning.ipynb
+++ b/_sources/assignments/deep-learning/tensorflow/intro_to_tensorflow_for_deeplearning.ipynb
@@ -8,7 +8,6 @@
     "id": "304ad719"
    },
    "source": [
-    "<a name='0'></a>\n",
     "# Intro to TensorFlow for Deep Learning"
    ]
   },
diff --git a/_sources/assignments/machine-learning-productionization/debugging-in-classification.ipynb b/_sources/assignments/machine-learning-productionization/debugging-in-classification.ipynb
index 1032e8b2ec..c78ae0e880 100644
--- a/_sources/assignments/machine-learning-productionization/debugging-in-classification.ipynb
+++ b/_sources/assignments/machine-learning-productionization/debugging-in-classification.ipynb
@@ -93,7 +93,7 @@
         "\n",
         "# Load data\n",
         "mnistDf_backup = pd.read_csv(\n",
-        "  \"https://download.mlcc.google.com/mledu-datasets/mnist_train_small.csv\",\n",
+        "  \"https://static-1300131294.cos.ap-shanghai.myqcloud.com/data/mnist_train_small.csv\",\n",
         "  sep=\",\",\n",
         "  header=None)\n",
         "# Shuffle data\n",
diff --git a/_sources/assignments/machine-learning-productionization/debugging-in-regression.ipynb b/_sources/assignments/machine-learning-productionization/debugging-in-regression.ipynb
index 3dfb5f6621..57b6410fe2 100644
--- a/_sources/assignments/machine-learning-productionization/debugging-in-regression.ipynb
+++ b/_sources/assignments/machine-learning-productionization/debugging-in-regression.ipynb
@@ -92,7 +92,7 @@
         "pd.options.display.float_format = '{:.1f}'.format\n",
         "\n",
         "wineDf = pd.read_csv(\n",
-        "  \"https://download.mlcc.google.com/mledu-datasets/winequality.csv\",\n",
+        "  \"https://static-1300131294.cos.ap-shanghai.myqcloud.com/data/winequality.csv\",\n",
         "  encoding='latin-1')\n",
         "wineDf.columns = ['fixed acidity','volatile acidity','citric acid',\n",
         "                     'residual sugar','chlorides','free sulfur dioxide',\n",
diff --git a/_sources/assignments/ml-fundamentals/create-a-regression-model.ipynb b/_sources/assignments/ml-fundamentals/create-a-regression-model.ipynb
index 4356f6dc77..20ac9dfa66 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": "7f8c5d9c",
+   "id": "ffe91a20",
    "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 28b6e7d612..84528f3f83 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": "54030063",
+   "id": "f38f7776",
    "metadata": {},
    "source": [
     "# Exploring visualizations\n",
diff --git a/_sources/assignments/ml-fundamentals/parameter-play.ipynb b/_sources/assignments/ml-fundamentals/parameter-play.ipynb
index df47a8e2fd..4b98510e42 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": "1407fd89",
+   "id": "1798d5b8",
    "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 9ea709ddd9..0e11347d46 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": "6f86010f",
+   "id": "00646e5e",
    "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 a8a8404cd2..fcfe85775e 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": "63285526",
+   "id": "6a8e2a7e",
    "metadata": {},
    "source": [
     "# Retrying some regression\n",
diff --git a/_sources/data-science/working-with-data/numpy.ipynb b/_sources/data-science/working-with-data/numpy.ipynb
index 3b38e3b086..2bc65be175 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": "b5888482",
+   "id": "afd632b9",
    "metadata": {},
    "source": [
     "# NumPy\n",
@@ -19,7 +19,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "785cda3a",
+   "id": "c0e30daf",
    "metadata": {},
    "outputs": [
     {
@@ -40,7 +40,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4497576e",
+   "id": "57e00754",
    "metadata": {},
    "source": [
     "### Create a basic array\n",
@@ -53,7 +53,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "a102ea7f",
+   "id": "53d4f97b",
    "metadata": {},
    "outputs": [
     {
@@ -75,7 +75,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "45e91322",
+   "id": "96b27b37",
    "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": "dfe4c7a0",
+   "id": "90cf63b7",
    "metadata": {},
    "outputs": [
     {
@@ -104,7 +104,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "03a6c41b",
+   "id": "1308eae0",
    "metadata": {},
    "source": [
     "Or an array filled with 1’s:"
@@ -113,7 +113,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "30524d08",
+   "id": "4a492738",
    "metadata": {},
    "outputs": [
     {
@@ -133,7 +133,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ebf9858c",
+   "id": "3f580af4",
    "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": "4e02ccfd",
+   "id": "9db1f151",
    "metadata": {},
    "outputs": [
     {
@@ -162,7 +162,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3dbd97c7",
+   "id": "6b3857db",
    "metadata": {},
    "source": [
     "You can create an array with a range of elements:"
@@ -171,7 +171,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "a0448381",
+   "id": "09ae9658",
    "metadata": {},
    "outputs": [
     {
@@ -191,7 +191,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "47cada4e",
+   "id": "a24a8fce",
    "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": "05de6608",
+   "id": "c771bd4e",
    "metadata": {},
    "outputs": [
     {
@@ -220,7 +220,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "69708ef5",
+   "id": "f82cbadc",
    "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": "73c75e6d",
+   "id": "8b00a93d",
    "metadata": {},
    "outputs": [
     {
@@ -249,7 +249,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d4ed5418",
+   "id": "4eac2b2c",
    "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": "03b5f43a",
+   "id": "fa5ec2d3",
    "metadata": {},
    "outputs": [
     {
@@ -278,7 +278,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e5709a38",
+   "id": "d0818c5e",
    "metadata": {},
    "source": [
     "### Adding, removing, and sorting elements\n",
@@ -291,7 +291,7 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "049e020a",
+   "id": "1bea9026",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -300,7 +300,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "be71fbe1",
+   "id": "627bdbdc",
    "metadata": {},
    "source": [
     "You can quickly sort the numbers in ascending order with:"
@@ -309,7 +309,7 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "id": "de2aa8cf",
+   "id": "8833b143",
    "metadata": {},
    "outputs": [
     {
@@ -329,7 +329,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d6849175",
+   "id": "0fb253ea",
    "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": "76837ee3",
+   "id": "e26b89ca",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -355,7 +355,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "28bb0702",
+   "id": "2c76cd24",
    "metadata": {},
    "source": [
     "You can concatenate them with `np.concatenate()`."
@@ -364,7 +364,7 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "id": "0f172ac3",
+   "id": "34f3e25f",
    "metadata": {},
    "outputs": [
     {
@@ -384,7 +384,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ad8a9627",
+   "id": "91c6a0aa",
    "metadata": {},
    "source": [
     "Or, if you start with these arrays:"
@@ -393,7 +393,7 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "id": "b3d5e201",
+   "id": "221bb0f1",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -403,7 +403,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "38230d50",
+   "id": "e88c5796",
    "metadata": {},
    "source": [
     "You can concatenate them with:"
@@ -412,7 +412,7 @@
   {
    "cell_type": "code",
    "execution_count": 15,
-   "id": "dcc748c4",
+   "id": "7cfe1084",
    "metadata": {},
    "outputs": [
     {
@@ -434,7 +434,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b9aed6b6",
+   "id": "135cf70b",
    "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": "930d5c5a",
+   "id": "f6507c6a",
    "metadata": {},
    "outputs": [
     {
@@ -475,7 +475,7 @@
   {
    "cell_type": "code",
    "execution_count": 17,
-   "id": "6de83562",
+   "id": "2e5a343a",
    "metadata": {},
    "outputs": [
     {
@@ -495,7 +495,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "910fa2fa",
+   "id": "d58138bd",
    "metadata": {},
    "source": [
     "- ndarray.shape\n",
@@ -505,7 +505,7 @@
   {
    "cell_type": "code",
    "execution_count": 18,
-   "id": "4df0897a",
+   "id": "1321a855",
    "metadata": {},
    "outputs": [
     {
@@ -525,7 +525,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a8b7089e",
+   "id": "df14a749",
    "metadata": {},
    "source": [
     "- ndarray.size\n",
@@ -535,7 +535,7 @@
   {
    "cell_type": "code",
    "execution_count": 19,
-   "id": "4b82bdeb",
+   "id": "92317290",
    "metadata": {},
    "outputs": [
     {
@@ -555,7 +555,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c46827ca",
+   "id": "94e57081",
    "metadata": {},
    "source": [
     "- ndarray.dtype\n",
@@ -565,7 +565,7 @@
   {
    "cell_type": "code",
    "execution_count": 20,
-   "id": "1a4fdec5",
+   "id": "d765a8dc",
    "metadata": {},
    "outputs": [
     {
@@ -586,7 +586,7 @@
   {
    "cell_type": "code",
    "execution_count": 21,
-   "id": "46cb5a92",
+   "id": "32a250c9",
    "metadata": {},
    "outputs": [
     {
@@ -606,7 +606,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "63c6eebf",
+   "id": "e897d24c",
    "metadata": {},
    "source": [
     "- ndarray.itemsize\n",
@@ -616,7 +616,7 @@
   {
    "cell_type": "code",
    "execution_count": 22,
-   "id": "2d8c0e24",
+   "id": "53bbe240",
    "metadata": {},
    "outputs": [
     {
@@ -636,7 +636,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6560f96f",
+   "id": "18d7a014",
    "metadata": {},
    "source": [
     "- ndarray.data\n",
@@ -646,13 +646,13 @@
   {
    "cell_type": "code",
    "execution_count": 23,
-   "id": "88e890a3",
+   "id": "684a1fdc",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<memory at 0x7f806c85fd40>"
+       "<memory at 0x7f12d2318d40>"
       ]
      },
      "execution_count": 23,
@@ -666,7 +666,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c3608ecd",
+   "id": "84baa9ec",
    "metadata": {},
    "source": [
     "### Reshape an array\n",
@@ -679,7 +679,7 @@
   {
    "cell_type": "code",
    "execution_count": 24,
-   "id": "283a3027",
+   "id": "476ccc9d",
    "metadata": {},
    "outputs": [
     {
@@ -700,7 +700,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f77a9c2e",
+   "id": "18d73378",
    "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": "27b6a6f4",
+   "id": "fd56c9d1",
    "metadata": {},
    "outputs": [
     {
@@ -732,7 +732,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4f9818e1",
+   "id": "aacbe93f",
    "metadata": {},
    "source": [
     "With `np.reshape`, you can specify a few optional parameters:"
@@ -741,7 +741,7 @@
   {
    "cell_type": "code",
    "execution_count": 26,
-   "id": "2b0ab3e9",
+   "id": "efbbeaa3",
    "metadata": {},
    "outputs": [
     {
@@ -761,7 +761,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ec4b3d99",
+   "id": "57483977",
    "metadata": {},
    "source": [
     "`a` is the array to be reshaped.\n",
@@ -782,7 +782,7 @@
   {
    "cell_type": "code",
    "execution_count": 27,
-   "id": "c83a127f",
+   "id": "24927b0b",
    "metadata": {},
    "outputs": [
     {
@@ -803,7 +803,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fb4550e1",
+   "id": "a86da884",
    "metadata": {},
    "source": [
     "You can use `np.newaxis` to add a new axis:"
@@ -812,7 +812,7 @@
   {
    "cell_type": "code",
    "execution_count": 28,
-   "id": "343b677a",
+   "id": "dcca9748",
    "metadata": {},
    "outputs": [
     {
@@ -833,7 +833,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0b886e81",
+   "id": "a6d1a5e4",
    "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": "2396425f",
+   "id": "b671df03",
    "metadata": {},
    "outputs": [
     {
@@ -863,7 +863,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e88e5084",
+   "id": "8a03741d",
    "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": "f86eaec4",
+   "id": "69c62c9c",
    "metadata": {},
    "outputs": [
     {
@@ -893,7 +893,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c2ceeaa3",
+   "id": "deeed2bc",
    "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": "feaaf5f5",
+   "id": "1154f985",
    "metadata": {},
    "outputs": [
     {
@@ -925,7 +925,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8179a047",
+   "id": "bb445792",
    "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": "f2395964",
+   "id": "64df29e0",
    "metadata": {},
    "outputs": [
     {
@@ -955,7 +955,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e731906e",
+   "id": "6dcef3f0",
    "metadata": {},
    "source": [
     "You can add an axis at index position 0 with:"
@@ -964,7 +964,7 @@
   {
    "cell_type": "code",
    "execution_count": 33,
-   "id": "1e831b21",
+   "id": "ccc24c7a",
    "metadata": {},
    "outputs": [
     {
@@ -985,7 +985,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "32c57fe6",
+   "id": "d640e522",
    "metadata": {},
    "source": [
     "### Indexing and slicing\n",
@@ -996,7 +996,7 @@
   {
    "cell_type": "code",
    "execution_count": 34,
-   "id": "e6828924",
+   "id": "c91dbaea",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1006,7 +1006,7 @@
   {
    "cell_type": "code",
    "execution_count": 35,
-   "id": "fcf6182e",
+   "id": "f1a45f5b",
    "metadata": {},
    "outputs": [
     {
@@ -1027,7 +1027,7 @@
   {
    "cell_type": "code",
    "execution_count": 36,
-   "id": "db06cad3",
+   "id": "a276b0ac",
    "metadata": {},
    "outputs": [
     {
@@ -1048,7 +1048,7 @@
   {
    "cell_type": "code",
    "execution_count": 37,
-   "id": "77460fae",
+   "id": "ff2a4b3e",
    "metadata": {},
    "outputs": [
     {
@@ -1069,7 +1069,7 @@
   {
    "cell_type": "code",
    "execution_count": 38,
-   "id": "50d3d7d5",
+   "id": "8c44de1c",
    "metadata": {},
    "outputs": [
     {
@@ -1089,7 +1089,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "390f7286",
+   "id": "793dc947",
    "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": "bfabe582",
+   "id": "6b0d7201",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1111,7 +1111,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "98d7b447",
+   "id": "66dc4c1a",
    "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": "7b481bbb",
+   "id": "466f59e3",
    "metadata": {},
    "outputs": [
     {
@@ -1140,7 +1140,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a1f20041",
+   "id": "60fd21ac",
    "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": "abbbef5c",
+   "id": "57caa999",
    "metadata": {},
    "outputs": [
     {
@@ -1170,7 +1170,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5f9efe6e",
+   "id": "f7f8b40a",
    "metadata": {},
    "source": [
     "You can select elements that are divisible by 2:"
@@ -1179,7 +1179,7 @@
   {
    "cell_type": "code",
    "execution_count": 42,
-   "id": "6fcf8c82",
+   "id": "b2b5e8a7",
    "metadata": {},
    "outputs": [
     {
@@ -1200,7 +1200,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ac07b8bf",
+   "id": "aeb7292c",
    "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": "a6c02448",
+   "id": "1fd64587",
    "metadata": {},
    "outputs": [
     {
@@ -1230,7 +1230,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9aea32e3",
+   "id": "29f3f751",
    "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": "52e1a2a9",
+   "id": "73677cf5",
    "metadata": {},
    "outputs": [
     {
@@ -1262,7 +1262,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6196ee04",
+   "id": "4a44e4eb",
    "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": "84de3fc8",
+   "id": "74838c5e",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1282,7 +1282,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "62b27c24",
+   "id": "15fba768",
    "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": "bf9b79b9",
+   "id": "ee023e21",
    "metadata": {},
    "outputs": [
     {
@@ -1312,7 +1312,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "be13baec",
+   "id": "13a0f028",
    "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": "6e50ff11",
+   "id": "1d487b02",
    "metadata": {},
    "outputs": [
     {
@@ -1345,7 +1345,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2eecc043",
+   "id": "c95ed28e",
    "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": "e6439bbf",
+   "id": "e428d9da",
    "metadata": {},
    "outputs": [
     {
@@ -1374,7 +1374,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "85a29814",
+   "id": "38065886",
    "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": "454088ea",
+   "id": "3a5553af",
    "metadata": {},
    "outputs": [
     {
@@ -1404,7 +1404,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8be2d548",
+   "id": "b20e0feb",
    "metadata": {},
    "source": [
     "### Create an array from existing data\n",
@@ -1417,7 +1417,7 @@
   {
    "cell_type": "code",
    "execution_count": 50,
-   "id": "62890d3d",
+   "id": "bf17f95a",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1426,7 +1426,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e7bdf8cb",
+   "id": "eef16df9",
    "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": "cf1bb50c",
+   "id": "5e32a71d",
    "metadata": {},
    "outputs": [
     {
@@ -1456,7 +1456,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b4a69e71",
+   "id": "046dc652",
    "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": "e8d3491c",
+   "id": "d1ff94b9",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1479,7 +1479,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a1fd82bb",
+   "id": "21ff4cea",
    "metadata": {},
    "source": [
     "You can stack them vertically with `vstack`:"
@@ -1488,7 +1488,7 @@
   {
    "cell_type": "code",
    "execution_count": 53,
-   "id": "2b133e0b",
+   "id": "f19ecc4a",
    "metadata": {},
    "outputs": [
     {
@@ -1511,7 +1511,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cb12b0c2",
+   "id": "83f58573",
    "metadata": {},
    "source": [
     "Or stack them horizontally with hstack:"
@@ -1520,7 +1520,7 @@
   {
    "cell_type": "code",
    "execution_count": 54,
-   "id": "bce35ecd",
+   "id": "17e3f7ac",
    "metadata": {},
    "outputs": [
     {
@@ -1541,7 +1541,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e28a6f04",
+   "id": "5051bc67",
    "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": "f99bd667",
+   "id": "050776e1",
    "metadata": {},
    "outputs": [
     {
@@ -1574,7 +1574,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cbc08091",
+   "id": "a050b004",
    "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": "14c2c436",
+   "id": "33a4af0d",
    "metadata": {},
    "outputs": [
     {
@@ -1608,7 +1608,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "35b81ae5",
+   "id": "6b8ba061",
    "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": "dcaf2edb",
+   "id": "82cbb781",
    "metadata": {},
    "outputs": [
     {
@@ -1642,7 +1642,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c81861d9",
+   "id": "f9d630fb",
    "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": "234f0412",
+   "id": "5263cc58",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1664,7 +1664,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "af503dcd",
+   "id": "3d2ebdeb",
    "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": "845c5d61",
+   "id": "b165b69e",
    "metadata": {},
    "outputs": [
     {
@@ -1695,7 +1695,7 @@
   {
    "cell_type": "code",
    "execution_count": 60,
-   "id": "106bbd4b",
+   "id": "47c9a96d",
    "metadata": {},
    "outputs": [
     {
@@ -1717,7 +1717,7 @@
   {
    "cell_type": "code",
    "execution_count": 61,
-   "id": "a3cea45b",
+   "id": "b3f713e5",
    "metadata": {},
    "outputs": [
     {
@@ -1739,7 +1739,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "09b0efa2",
+   "id": "767927aa",
    "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": "e97bbc37",
+   "id": "c20fcc4b",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1757,7 +1757,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "840ff748",
+   "id": "4d9ff5df",
    "metadata": {},
    "source": [
     "## Array operations\n",
@@ -1770,7 +1770,7 @@
   {
    "cell_type": "code",
    "execution_count": 63,
-   "id": "d34c3785",
+   "id": "7020198e",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1780,7 +1780,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "354b812c",
+   "id": "c31a33cd",
    "metadata": {},
    "source": [
     "You can add the arrays together with the plus sign."
@@ -1789,7 +1789,7 @@
   {
    "cell_type": "code",
    "execution_count": 64,
-   "id": "044a9d38",
+   "id": "246ef529",
    "metadata": {},
    "outputs": [
     {
@@ -1809,7 +1809,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ca6b8eee",
+   "id": "972acd1b",
    "metadata": {},
    "source": [
     "You can, of course, do more than just addition!"
@@ -1818,7 +1818,7 @@
   {
    "cell_type": "code",
    "execution_count": 65,
-   "id": "02080dd2",
+   "id": "16c2a890",
    "metadata": {},
    "outputs": [
     {
@@ -1839,7 +1839,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "06df329f",
+   "id": "a16dadd6",
    "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": "d5942646",
+   "id": "ed0c6159",
    "metadata": {},
    "outputs": [
     {
@@ -1869,7 +1869,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "de4ae5f1",
+   "id": "8965e1d3",
    "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": "797ef075",
+   "id": "0b73d411",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1889,7 +1889,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d6326b81",
+   "id": "5c022264",
    "metadata": {},
    "source": [
     "You can sum over the axis of rows with:"
@@ -1898,7 +1898,7 @@
   {
    "cell_type": "code",
    "execution_count": 68,
-   "id": "879ed4b6",
+   "id": "e91c6613",
    "metadata": {},
    "outputs": [
     {
@@ -1918,7 +1918,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1d1d6798",
+   "id": "995174be",
    "metadata": {},
    "source": [
     "You can sum over the axis of columns with:"
@@ -1927,7 +1927,7 @@
   {
    "cell_type": "code",
    "execution_count": 69,
-   "id": "89742bc3",
+   "id": "d28ea010",
    "metadata": {},
    "outputs": [
     {
@@ -1947,7 +1947,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e05aea65",
+   "id": "5e161355",
    "metadata": {},
    "source": [
     "### Universal functions(ufunc)\n",
@@ -2038,7 +2038,7 @@
   {
    "cell_type": "code",
    "execution_count": 70,
-   "id": "2e7a2827",
+   "id": "32b883ba",
    "metadata": {},
    "outputs": [
     {
@@ -2060,7 +2060,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e14d53f4",
+   "id": "9ffb0ade",
    "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": "3034ee65",
+   "id": "e795ee7c",
    "metadata": {},
    "outputs": [
     {
@@ -2091,7 +2091,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b32494fc",
+   "id": "d0dc4f1d",
    "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": "dfec7dfa",
+   "id": "9622f23e",
    "metadata": {},
    "outputs": [
     {
@@ -2154,7 +2154,7 @@
   {
    "cell_type": "code",
    "execution_count": 73,
-   "id": "b109968d",
+   "id": "376131a7",
    "metadata": {},
    "outputs": [
     {
@@ -2175,7 +2175,7 @@
   {
    "cell_type": "code",
    "execution_count": 74,
-   "id": "14573901",
+   "id": "f9b036f6",
    "metadata": {},
    "outputs": [
     {
@@ -2195,7 +2195,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "827494ec",
+   "id": "50959069",
    "metadata": {},
    "source": [
     "Let’s start with this array, called “a”."
@@ -2204,7 +2204,7 @@
   {
    "cell_type": "code",
    "execution_count": 75,
-   "id": "8fbcc6fd",
+   "id": "f2bba32b",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2215,7 +2215,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9bb857c3",
+   "id": "f62ca103",
    "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": "1365f7c7",
+   "id": "9e4268f9",
    "metadata": {},
    "outputs": [
     {
@@ -2244,7 +2244,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7d979114",
+   "id": "b6ab4168",
    "metadata": {},
    "source": [
     "Or:"
@@ -2253,7 +2253,7 @@
   {
    "cell_type": "code",
    "execution_count": 77,
-   "id": "d01f3f34",
+   "id": "ba76b255",
    "metadata": {},
    "outputs": [
     {
@@ -2273,7 +2273,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6b75981d",
+   "id": "26f0a9aa",
    "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": "b877a6d2",
+   "id": "ed234ccc",
    "metadata": {},
    "outputs": [
     {
@@ -2302,7 +2302,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0f014088",
+   "id": "c53293e7",
    "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": "11a0d9b8",
+   "id": "91383c9a",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2335,7 +2335,7 @@
   {
    "cell_type": "code",
    "execution_count": 80,
-   "id": "e5904a16",
+   "id": "ddb83fe6",
    "metadata": {},
    "outputs": [
     {
@@ -2356,7 +2356,7 @@
   {
    "cell_type": "code",
    "execution_count": 81,
-   "id": "b1f20faa",
+   "id": "bf560db2",
    "metadata": {},
    "outputs": [
     {
@@ -2376,7 +2376,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "78347e54",
+   "id": "cd1802ce",
    "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": "38396974",
+   "id": "6db67042",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2395,7 +2395,7 @@
   {
    "cell_type": "code",
    "execution_count": 83,
-   "id": "14246c83",
+   "id": "8d33f82a",
    "metadata": {},
    "outputs": [
     {
@@ -2416,7 +2416,7 @@
   {
    "cell_type": "code",
    "execution_count": 84,
-   "id": "d964c396",
+   "id": "eac24599",
    "metadata": {},
    "outputs": [
     {
@@ -2436,7 +2436,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f4db7183",
+   "id": "d72b521a",
    "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": "b3063d7f",
+   "id": "be877af4",
    "metadata": {},
    "outputs": [
     {
@@ -2465,7 +2465,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "302f6ed0",
+   "id": "64842e26",
    "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": "b61a9fd7",
+   "id": "012d21ab",
    "metadata": {},
    "outputs": [
     {
@@ -2494,7 +2494,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "35a67fd4",
+   "id": "cb1d7b6d",
    "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": "5d86e072",
+   "id": "4de2cbd9",
    "metadata": {},
    "outputs": [
     {
@@ -2542,7 +2542,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c642e2bd",
+   "id": "efb0fe8c",
    "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": "c98b0c5a",
+   "id": "93896f51",
    "metadata": {},
    "outputs": [
     {
@@ -2572,7 +2572,7 @@
   {
    "cell_type": "code",
    "execution_count": 89,
-   "id": "3a57ee72",
+   "id": "abab0c27",
    "metadata": {},
    "outputs": [
     {
@@ -2592,7 +2592,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fbf154df",
+   "id": "bcfac7a9",
    "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": "54fa2284",
+   "id": "20f1a2bc",
    "metadata": {},
    "outputs": [
     {
@@ -2621,7 +2621,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8cacdc50",
+   "id": "df45a1c8",
    "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": "a6a38346",
+   "id": "8fbec975",
    "metadata": {},
    "outputs": [
     {
@@ -2652,7 +2652,7 @@
   {
    "cell_type": "code",
    "execution_count": 92,
-   "id": "45fd34c7",
+   "id": "592a1d4d",
    "metadata": {},
    "outputs": [
     {
@@ -2674,7 +2674,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bd80540c",
+   "id": "4dc1edac",
    "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": "cf487e2a",
+   "id": "70b88522",
    "metadata": {},
    "outputs": [
     {
@@ -2720,7 +2720,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cea60560",
+   "id": "fc28c761",
    "metadata": {},
    "source": [
     "This is equivalent to:"
@@ -2729,7 +2729,7 @@
   {
    "cell_type": "code",
    "execution_count": 94,
-   "id": "256b0b53",
+   "id": "d8be64d9",
    "metadata": {},
    "outputs": [
     {
@@ -2750,7 +2750,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f0852ec8",
+   "id": "68ae62cf",
    "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": "f3949020",
+   "id": "e907ef93",
    "metadata": {},
    "outputs": [
     {
@@ -2780,7 +2780,7 @@
   {
    "cell_type": "code",
    "execution_count": 96,
-   "id": "9c51768b",
+   "id": "dfc40517",
    "metadata": {},
    "outputs": [
     {
@@ -2800,7 +2800,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d342e127",
+   "id": "4c485aaf",
    "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": "adbe9075",
+   "id": "1fd64ba6",
    "metadata": {},
    "outputs": [
     {
@@ -2834,7 +2834,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b17566e0",
+   "id": "9bb6ec4c",
    "metadata": {},
    "source": [
     "### Advanced indexing\n",
@@ -2857,7 +2857,7 @@
   {
    "cell_type": "code",
    "execution_count": 98,
-   "id": "02932a10",
+   "id": "063462f6",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2867,7 +2867,7 @@
   {
    "cell_type": "code",
    "execution_count": 99,
-   "id": "7f8fdde1",
+   "id": "7a3e12f0",
    "metadata": {},
    "outputs": [
     {
@@ -2888,7 +2888,7 @@
   {
    "cell_type": "code",
    "execution_count": 100,
-   "id": "a8a7ea68",
+   "id": "744d9514",
    "metadata": {},
    "outputs": [
     {
@@ -2909,7 +2909,7 @@
   {
    "cell_type": "code",
    "execution_count": 101,
-   "id": "5ccb1438",
+   "id": "b28b3408",
    "metadata": {},
    "outputs": [
     {
@@ -2929,7 +2929,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "96d8e32b",
+   "id": "f8a9a31d",
    "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": "43a4fb4a",
+   "id": "2ad82631",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2948,7 +2948,7 @@
   {
    "cell_type": "code",
    "execution_count": 103,
-   "id": "f065ee6a",
+   "id": "0a5c9ed5",
    "metadata": {},
    "outputs": [
     {
@@ -2969,7 +2969,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e7c13101",
+   "id": "89ac205a",
    "metadata": {},
    "source": [
     "```py\n",
@@ -2999,7 +2999,7 @@
   {
    "cell_type": "code",
    "execution_count": 104,
-   "id": "063101bc",
+   "id": "875bf1de",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3009,7 +3009,7 @@
   {
    "cell_type": "code",
    "execution_count": 105,
-   "id": "e4a0954b",
+   "id": "60517bde",
    "metadata": {},
    "outputs": [
     {
@@ -3034,7 +3034,7 @@
   {
    "cell_type": "code",
    "execution_count": 106,
-   "id": "f5399375",
+   "id": "bd10806d",
    "metadata": {},
    "outputs": [
     {
@@ -3054,7 +3054,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "54a9e869",
+   "id": "1555bcc5",
    "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": "b5f16305",
+   "id": "98938b31",
    "metadata": {},
    "outputs": [
     {
@@ -3097,7 +3097,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "31c71ff4",
+   "id": "d8d6cd40",
    "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": "c844ded4",
+   "id": "cb5a9daf",
    "metadata": {},
    "outputs": [
     {
@@ -3128,7 +3128,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ac086385",
+   "id": "2477d757",
    "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": "0570bd1c",
+   "id": "0c0c1d55",
    "metadata": {},
    "outputs": [
     {
@@ -3164,7 +3164,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a3dcf6fe",
+   "id": "b413f935",
    "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": "9bafc89a",
+   "id": "2b7d821f",
    "metadata": {},
    "outputs": [
     {
@@ -3206,7 +3206,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e2c55b65",
+   "id": "4175b1f5",
    "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": "7c316750",
+   "id": "9c76050f",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3226,7 +3226,7 @@
   {
    "cell_type": "code",
    "execution_count": 112,
-   "id": "15afb73a",
+   "id": "5b1e4f8b",
    "metadata": {},
    "outputs": [
     {
@@ -3248,7 +3248,7 @@
   {
    "cell_type": "code",
    "execution_count": 113,
-   "id": "30c1524d",
+   "id": "0dc54de3",
    "metadata": {},
    "outputs": [
     {
@@ -3269,7 +3269,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "90a1e37e",
+   "id": "e371c00b",
    "metadata": {},
    "source": [
     "This broadcasting can also be achieved using the function `ix_`:"
@@ -3278,7 +3278,7 @@
   {
    "cell_type": "code",
    "execution_count": 114,
-   "id": "c1cc2872",
+   "id": "4efe4cd1",
    "metadata": {},
    "outputs": [
     {
@@ -3299,7 +3299,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4e044e8a",
+   "id": "4d2d1040",
    "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": "f13887ba",
+   "id": "0fab075b",
    "metadata": {},
    "outputs": [
     {
@@ -3328,7 +3328,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cb1e8cf2",
+   "id": "5470b9dd",
    "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": "05271311",
+   "id": "fd9f1a9c",
    "metadata": {},
    "outputs": [
     {
@@ -3370,7 +3370,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ec5b9a35",
+   "id": "38a15ef6",
    "metadata": {},
    "source": [
     "Or wish to add a constant to all negative elements:"
@@ -3379,7 +3379,7 @@
   {
    "cell_type": "code",
    "execution_count": 117,
-   "id": "291fff1d",
+   "id": "e6ff83aa",
    "metadata": {},
    "outputs": [
     {
@@ -3401,7 +3401,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "034c1975",
+   "id": "1377eac0",
    "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": "c58aa8b3",
+   "id": "46569ba2",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3425,7 +3425,7 @@
   {
    "cell_type": "code",
    "execution_count": 119,
-   "id": "21849360",
+   "id": "36b2b148",
    "metadata": {},
    "outputs": [
     {
@@ -3446,7 +3446,7 @@
   {
    "cell_type": "code",
    "execution_count": 120,
-   "id": "e6924d97",
+   "id": "21f9558e",
    "metadata": {},
    "outputs": [
     {
@@ -3467,7 +3467,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2cbb48f6",
+   "id": "6eea78ca",
    "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": "612560aa",
+   "id": "deb7f9b0",
    "metadata": {},
    "outputs": [
     {
@@ -3503,7 +3503,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4c8ed0c7",
+   "id": "f32a04a6",
    "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": "c8309bd2",
+   "id": "00b1054c",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3530,7 +3530,7 @@
   {
    "cell_type": "code",
    "execution_count": 123,
-   "id": "6f96cb8a",
+   "id": "9e0b2885",
    "metadata": {},
    "outputs": [
     {
@@ -3551,7 +3551,7 @@
   {
    "cell_type": "code",
    "execution_count": 124,
-   "id": "11741b5c",
+   "id": "c68d1ca5",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3561,7 +3561,7 @@
   {
    "cell_type": "code",
    "execution_count": 125,
-   "id": "fd39a729",
+   "id": "e1b69b0e",
    "metadata": {},
    "outputs": [
     {
@@ -3582,7 +3582,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7ddc6b28",
+   "id": "5ebd2940",
    "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": "7ccf9464",
+   "id": "3f958897",
    "metadata": {},
    "outputs": [
     {
@@ -3615,7 +3615,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "564a7af0",
+   "id": "c30bafa8",
    "metadata": {},
    "source": [
     "##### Example 3\n",
@@ -3626,7 +3626,7 @@
   {
    "cell_type": "code",
    "execution_count": 127,
-   "id": "dce8c929",
+   "id": "bbd9ffb3",
    "metadata": {},
    "outputs": [
     {
@@ -3654,7 +3654,7 @@
   {
    "cell_type": "code",
    "execution_count": 128,
-   "id": "bcd7a12b",
+   "id": "123eb6b5",
    "metadata": {},
    "outputs": [
     {
@@ -3678,7 +3678,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a7da394e",
+   "id": "c941c59b",
    "metadata": {},
    "source": [
     "#### Combining advanced and basic indexing\n",
@@ -3691,7 +3691,7 @@
   {
    "cell_type": "code",
    "execution_count": 129,
-   "id": "dc2df71d",
+   "id": "4794a87c",
    "metadata": {},
    "outputs": [
     {
@@ -3714,7 +3714,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "28f3afe5",
+   "id": "38d17db1",
    "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": "d61f25a8",
+   "id": "a36fe89e",
    "metadata": {},
    "outputs": [
     {
@@ -3745,7 +3745,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "907b0b9d",
+   "id": "b2e31803",
    "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": "9a400503",
+   "id": "9690f141",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3767,7 +3767,7 @@
   {
    "cell_type": "code",
    "execution_count": 132,
-   "id": "95b3f42f",
+   "id": "0056a333",
    "metadata": {},
    "outputs": [
     {
@@ -3788,7 +3788,7 @@
   {
    "cell_type": "code",
    "execution_count": 133,
-   "id": "0dbd6221",
+   "id": "f68d47d7",
    "metadata": {},
    "outputs": [
     {
@@ -3808,7 +3808,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "718eb852",
+   "id": "bd9276eb",
    "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": "b840bdb4",
+   "id": "b9a9e721",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3845,7 +3845,7 @@
   {
    "cell_type": "code",
    "execution_count": 135,
-   "id": "4031f15a",
+   "id": "d18af53c",
    "metadata": {},
    "outputs": [
     {
@@ -3870,7 +3870,7 @@
   {
    "cell_type": "code",
    "execution_count": 136,
-   "id": "e16ef04d",
+   "id": "a46c61a7",
    "metadata": {},
    "outputs": [
     {
@@ -3891,7 +3891,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "022fb178",
+   "id": "90461200",
    "metadata": {},
    "source": [
     "### Field access\n",
@@ -3908,7 +3908,7 @@
   {
    "cell_type": "code",
    "execution_count": 137,
-   "id": "dc4dfd32",
+   "id": "d35521cb",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3918,7 +3918,7 @@
   {
    "cell_type": "code",
    "execution_count": 138,
-   "id": "437605af",
+   "id": "4409ef93",
    "metadata": {},
    "outputs": [
     {
@@ -3939,7 +3939,7 @@
   {
    "cell_type": "code",
    "execution_count": 139,
-   "id": "1e9f160b",
+   "id": "cb258d6f",
    "metadata": {},
    "outputs": [
     {
@@ -3960,7 +3960,7 @@
   {
    "cell_type": "code",
    "execution_count": 140,
-   "id": "e209d12a",
+   "id": "5edf3085",
    "metadata": {},
    "outputs": [
     {
@@ -3981,7 +3981,7 @@
   {
    "cell_type": "code",
    "execution_count": 141,
-   "id": "8f574141",
+   "id": "e207b95f",
    "metadata": {},
    "outputs": [
     {
@@ -4001,7 +4001,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c1eed605",
+   "id": "7919d5ec",
    "metadata": {},
    "source": [
     "### Flat Iterator indexing\n",
@@ -4016,7 +4016,7 @@
   {
    "cell_type": "code",
    "execution_count": 142,
-   "id": "5ac958b9",
+   "id": "26837104",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -4026,7 +4026,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e5fa14f1",
+   "id": "a6660ba8",
    "metadata": {},
    "source": [
     "Or an array of the right size:"
@@ -4035,7 +4035,7 @@
   {
    "cell_type": "code",
    "execution_count": 143,
-   "id": "03e4fa34",
+   "id": "db928126",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -4044,7 +4044,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "48b8957e",
+   "id": "1b27d3e5",
    "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": "78392572",
+   "id": "e9f98d73",
    "metadata": {},
    "outputs": [
     {
@@ -4074,7 +4074,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2d6aec14",
+   "id": "67debb07",
    "metadata": {},
    "source": [
     "```py\n",
@@ -4093,7 +4093,7 @@
   {
    "cell_type": "code",
    "execution_count": 145,
-   "id": "817f4e56",
+   "id": "8e27fdb0",
    "metadata": {},
    "outputs": [
     {
@@ -4115,7 +4115,7 @@
   {
    "cell_type": "code",
    "execution_count": 146,
-   "id": "8d24f864",
+   "id": "a0c9e4ed",
    "metadata": {},
    "outputs": [
     {
@@ -4136,7 +4136,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fc9b6b02",
+   "id": "d445872a",
    "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": "fcf9c20a",
+   "id": "f2286f20",
    "metadata": {},
    "outputs": [
     {
@@ -4171,7 +4171,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "86f6e843",
+   "id": "dfc6a6e1",
    "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": "13b4d1e0",
+   "id": "f4effb63",
    "metadata": {},
    "outputs": [
     {
@@ -4203,7 +4203,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "159319b6",
+   "id": "21bb89e9",
    "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": "9ca410fa",
+   "id": "365d65f5",
    "metadata": {},
    "outputs": [
     {
@@ -4235,7 +4235,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "767a18dd",
+   "id": "de8faa4d",
    "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": "2580ea87",
+   "id": "315eda2d",
    "metadata": {},
    "outputs": [
     {
@@ -4316,7 +4316,7 @@
   {
    "cell_type": "code",
    "execution_count": 151,
-   "id": "a9876d42",
+   "id": "47005c0a",
    "metadata": {},
    "outputs": [
     {
@@ -4336,7 +4336,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e878b8e8",
+   "id": "090bddf7",
    "metadata": {},
    "source": [
     "## Structured arrays\n",
@@ -4349,7 +4349,7 @@
   {
    "cell_type": "code",
    "execution_count": 152,
-   "id": "4c4c0611",
+   "id": "63b3f381",
    "metadata": {},
    "outputs": [
     {
@@ -4372,7 +4372,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6df07549",
+   "id": "d485f421",
    "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": "842831f0",
+   "id": "90aa50c5",
    "metadata": {},
    "outputs": [
     {
@@ -4403,7 +4403,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "72ddac9c",
+   "id": "6dd26439",
    "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": "e34d9aa7",
+   "id": "92220f87",
    "metadata": {},
    "outputs": [
     {
@@ -4433,7 +4433,7 @@
   {
    "cell_type": "code",
    "execution_count": 155,
-   "id": "126b416f",
+   "id": "f1d5effa",
    "metadata": {},
    "outputs": [
     {
@@ -4454,7 +4454,7 @@
   {
    "cell_type": "code",
    "execution_count": 156,
-   "id": "912cad13",
+   "id": "3e347096",
    "metadata": {},
    "outputs": [
     {
@@ -4475,7 +4475,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ca9d77da",
+   "id": "ef86ad92",
    "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": "a651d2ac",
+   "id": "deaa4b6f",
    "metadata": {},
    "outputs": [
     {
@@ -4518,7 +4518,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "85665077",
+   "id": "e4013774",
    "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": "5bfb5622",
+   "id": "290e5a71",
    "metadata": {},
    "outputs": [
     {
@@ -4547,7 +4547,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7d7bda10",
+   "id": "9c79739f",
    "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": "dbfc5653",
+   "id": "1785e5e8",
    "metadata": {},
    "outputs": [
     {
@@ -4581,7 +4581,7 @@
   {
    "cell_type": "code",
    "execution_count": 160,
-   "id": "9215972d",
+   "id": "b21eec02",
    "metadata": {},
    "outputs": [
     {
@@ -4601,7 +4601,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f22664b6",
+   "id": "5b908d92",
    "metadata": {},
    "source": [
     "- A dictionary of field parameter arrays\n",
@@ -4614,7 +4614,7 @@
   {
    "cell_type": "code",
    "execution_count": 161,
-   "id": "584c9e57",
+   "id": "d39c3007",
    "metadata": {},
    "outputs": [
     {
@@ -4635,7 +4635,7 @@
   {
    "cell_type": "code",
    "execution_count": 162,
-   "id": "96debf43",
+   "id": "e9ee8fa1",
    "metadata": {},
    "outputs": [
     {
@@ -4658,7 +4658,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "87a73b3e",
+   "id": "4b88bdf7",
    "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": "bb4ad3d2",
+   "id": "d635ab63",
    "metadata": {},
    "outputs": [
     {
@@ -4693,7 +4693,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "394a0bce",
+   "id": "b14afd80",
    "metadata": {},
    "source": [
     "#### Manipulating and Displaying Structured Datatypes\n",
@@ -4704,7 +4704,7 @@
   {
    "cell_type": "code",
    "execution_count": 164,
-   "id": "8da55622",
+   "id": "0ef3750a",
    "metadata": {},
    "outputs": [
     {
@@ -4725,7 +4725,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ef61254c",
+   "id": "558d5321",
    "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": "35e677f7",
+   "id": "7b0f8b3c",
    "metadata": {},
    "outputs": [
     {
@@ -4756,7 +4756,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5b0965e6",
+   "id": "a9b44ff7",
    "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": "808b8e57",
+   "id": "9a4995a3",
    "metadata": {},
    "outputs": [
     {
@@ -4793,7 +4793,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "102ab5a9",
+   "id": "dc735227",
    "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": "5e2bdf5e",
+   "id": "f0e75f91",
    "metadata": {},
    "outputs": [
     {
@@ -4822,7 +4822,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5dc79104",
+   "id": "67b0c4ea",
    "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": "8a797059",
+   "id": "3456dc79",
    "metadata": {},
    "outputs": [
     {
@@ -4850,7 +4850,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "16de7203",
+   "id": "afd441be",
    "metadata": {},
    "source": [
     "### Indexing and Assignment to Structured arrays\n",
@@ -4867,7 +4867,7 @@
   {
    "cell_type": "code",
    "execution_count": 169,
-   "id": "b3c0653e",
+   "id": "38586c54",
    "metadata": {},
    "outputs": [
     {
@@ -4890,7 +4890,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e3161555",
+   "id": "93d6a23e",
    "metadata": {},
    "source": [
     "##### Assignment from Scalars\n",
@@ -4901,7 +4901,7 @@
   {
    "cell_type": "code",
    "execution_count": 170,
-   "id": "3a8d4e40",
+   "id": "6360f54c",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -4911,7 +4911,7 @@
   {
    "cell_type": "code",
    "execution_count": 171,
-   "id": "48873091",
+   "id": "302c9869",
    "metadata": {},
    "outputs": [
     {
@@ -4934,7 +4934,7 @@
   {
    "cell_type": "code",
    "execution_count": 172,
-   "id": "0f6129f7",
+   "id": "974623bd",
    "metadata": {},
    "outputs": [
     {
@@ -4956,7 +4956,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2e114197",
+   "id": "58967ba4",
    "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": "1e817a2d",
+   "id": "e76df064",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -4976,7 +4976,7 @@
   {
    "cell_type": "code",
    "execution_count": 174,
-   "id": "b276510e",
+   "id": "83e60957",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -4985,7 +4985,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d6cb1be7",
+   "id": "e6cc9b94",
    "metadata": {},
    "source": [
     "```py\n",
@@ -5006,7 +5006,7 @@
   {
    "cell_type": "code",
    "execution_count": 175,
-   "id": "86c62c21",
+   "id": "6ab587e4",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -5017,7 +5017,7 @@
   {
    "cell_type": "code",
    "execution_count": 176,
-   "id": "c00582b0",
+   "id": "bef52248",
    "metadata": {},
    "outputs": [
     {
@@ -5039,7 +5039,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "66dbb2d7",
+   "id": "b284d616",
    "metadata": {},
    "source": [
     "##### Assignment involving subarrays\n",
@@ -5056,7 +5056,7 @@
   {
    "cell_type": "code",
    "execution_count": 177,
-   "id": "7ab77ab8",
+   "id": "2d9bd35c",
    "metadata": {},
    "outputs": [
     {
@@ -5078,7 +5078,7 @@
   {
    "cell_type": "code",
    "execution_count": 178,
-   "id": "1512b7c1",
+   "id": "62c3b75c",
    "metadata": {},
    "outputs": [
     {
@@ -5099,7 +5099,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1f9b14bd",
+   "id": "254c5b6f",
    "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": "7408fddd",
+   "id": "204a8591",
    "metadata": {},
    "outputs": [
     {
@@ -5130,7 +5130,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5857f0c1",
+   "id": "2d3f5eb5",
    "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": "5aa16f19",
+   "id": "ebc639ef",
    "metadata": {},
    "outputs": [
     {
@@ -5159,7 +5159,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3e10749c",
+   "id": "c69aa084",
    "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": "62e622e5",
+   "id": "c00e0059",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -5178,7 +5178,7 @@
   {
    "cell_type": "code",
    "execution_count": 182,
-   "id": "7a7ff9ff",
+   "id": "50654920",
    "metadata": {},
    "outputs": [
     {
@@ -5199,7 +5199,7 @@
   {
    "cell_type": "code",
    "execution_count": 183,
-   "id": "c81fc737",
+   "id": "9e250a2b",
    "metadata": {},
    "outputs": [
     {
@@ -5219,7 +5219,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0c7fbe27",
+   "id": "320450d5",
    "metadata": {},
    "source": [
     "##### Accessing multiple fields\n",
@@ -5232,7 +5232,7 @@
   {
    "cell_type": "code",
    "execution_count": 184,
-   "id": "ae1f0721",
+   "id": "38283ceb",
    "metadata": {},
    "outputs": [
     {
@@ -5254,7 +5254,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "dc1fdde2",
+   "id": "7394672a",
    "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": "85fe6386",
+   "id": "8e5e8715",
    "metadata": {},
    "outputs": [
     {
@@ -5287,7 +5287,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "83513e05",
+   "id": "087b4865",
    "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": "d6698eb0",
+   "id": "4bc94f52",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -5305,7 +5305,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cd8e9380",
+   "id": "15b0fb17",
    "metadata": {},
    "source": [
     "##### Indexing with an integer to get a structured scalar\n",
@@ -5316,7 +5316,7 @@
   {
    "cell_type": "code",
    "execution_count": 187,
-   "id": "dd10ae37",
+   "id": "c3d7971d",
    "metadata": {},
    "outputs": [
     {
@@ -5339,7 +5339,7 @@
   {
    "cell_type": "code",
    "execution_count": 188,
-   "id": "2dd9edfd",
+   "id": "98c6761c",
    "metadata": {},
    "outputs": [
     {
@@ -5359,7 +5359,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7021926f",
+   "id": "fe0a55b0",
    "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": "38025152",
+   "id": "c5548f9d",
    "metadata": {},
    "outputs": [
     {
@@ -5391,7 +5391,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "267858ca",
+   "id": "a69274e5",
    "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": "df9a2bb5",
+   "id": "b071db04",
    "metadata": {},
    "outputs": [
     {
@@ -5422,7 +5422,7 @@
   {
    "cell_type": "code",
    "execution_count": 191,
-   "id": "a4d18340",
+   "id": "d6cf56dd",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -5431,7 +5431,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c04f3aa0",
+   "id": "72236f47",
    "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": "44d4f5b2",
+   "id": "7a94231e",
    "metadata": {},
    "outputs": [
     {
@@ -5460,7 +5460,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f30dc4b7",
+   "id": "97d3b3c8",
    "metadata": {},
    "source": [
     "#### Viewing structured arrays containing objects\n",
@@ -5475,7 +5475,7 @@
   {
    "cell_type": "code",
    "execution_count": 193,
-   "id": "4fd31f79",
+   "id": "8096d806",
    "metadata": {},
    "outputs": [
     {
@@ -5497,7 +5497,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "424d1ba7",
+   "id": "fe929ae9",
    "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": "baaa67b7",
+   "id": "90707423",
    "metadata": {},
    "outputs": [
     {
@@ -5527,7 +5527,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "80b3e916",
+   "id": "a0b2b907",
    "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": "84e4108a",
+   "id": "e0c738b6",
    "metadata": {},
    "outputs": [
     {
@@ -5557,7 +5557,7 @@
   {
    "cell_type": "code",
    "execution_count": 196,
-   "id": "c3f17d88",
+   "id": "5c326de2",
    "metadata": {},
    "outputs": [
     {
@@ -5577,7 +5577,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cb87b09c",
+   "id": "2a07d070",
    "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": "20a61efb",
+   "id": "cc5390b9",
    "metadata": {},
    "outputs": [
     {
@@ -5608,7 +5608,7 @@
   {
    "cell_type": "code",
    "execution_count": 198,
-   "id": "cf3b4857",
+   "id": "dc517995",
    "metadata": {},
    "outputs": [
     {
@@ -5628,7 +5628,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9f4e3590",
+   "id": "abad5718",
    "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": "6363970b",
+   "id": "3e506071",
    "metadata": {},
    "outputs": [
     {
@@ -5659,7 +5659,7 @@
   {
    "cell_type": "code",
    "execution_count": 200,
-   "id": "e000dc13",
+   "id": "ebd6df4b",
    "metadata": {},
    "outputs": [
     {
@@ -5680,7 +5680,7 @@
   {
    "cell_type": "code",
    "execution_count": 201,
-   "id": "b47c2d91",
+   "id": "bc3de171",
    "metadata": {},
    "outputs": [
     {
@@ -5700,7 +5700,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fad22d96",
+   "id": "634fd8e1",
    "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": "12393ade",
+   "id": "39180642",
    "metadata": {},
    "outputs": [
     {
@@ -5729,7 +5729,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4a7da9ec",
+   "id": "7ac9811b",
    "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/assignments/data-science/analyzing-COVID-19-papers.html b/assignments/data-science/analyzing-COVID-19-papers.html
index eb891ff99b..a4fb7d64eb 100644
--- a/assignments/data-science/analyzing-COVID-19-papers.html
+++ b/assignments/data-science/analyzing-COVID-19-papers.html
@@ -1697,7 +1697,9 @@ <h2><span class="section-number">40.22.1. </span>Getting the data<a class="heade
 </div></blockquote>
 <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">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;FILE_PATH_TO_metadata.csv&quot;</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">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/data-science/metadata.csv&quot;</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>
diff --git a/assignments/data-science/analyzing-data.html b/assignments/data-science/analyzing-data.html
index df347ea5f1..32f4602e14 100644
--- a/assignments/data-science/analyzing-data.html
+++ b/assignments/data-science/analyzing-data.html
@@ -1620,8 +1620,8 @@ <h1><span class="section-number">40.27. </span>Analyzing data<a class="headerlin
 <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="kn">import</span> <span class="nn">glob</span>
 
-<span class="c1">#Loading the dataset</span>
-<span class="n">path</span> <span class="o">=</span> <span class="s1">&#39;../../data/emails.csv&#39;</span>
+<span class="c1"># Loading the dataset</span>
+<span class="n">path</span> <span class="o">=</span> <span class="s2">&quot;https://static-1300131294.cos.ap-shanghai.myqcloud.com/data/data-science/emails.csv&quot;</span>
 <span class="n">email_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="n">path</span><span class="p">)</span>
 </pre></div>
 </div>
diff --git a/assignments/deep-learning/tensorflow/intro_to_tensorflow_for_deeplearning.html b/assignments/deep-learning/tensorflow/intro_to_tensorflow_for_deeplearning.html
index 578fbdf460..640e3c68f6 100644
--- a/assignments/deep-learning/tensorflow/intro_to_tensorflow_for_deeplearning.html
+++ b/assignments/deep-learning/tensorflow/intro_to_tensorflow_for_deeplearning.html
@@ -1764,8 +1764,7 @@ <h2> Contents </h2>
                 
               <div>
                 
-  <p><a name='0'></a></p>
-<section class="tex2jax_ignore mathjax_ignore" id="intro-to-tensorflow-for-deep-learning">
+  <section class="tex2jax_ignore mathjax_ignore" id="intro-to-tensorflow-for-deep-learning">
 <h1><span class="section-number">40.95. </span>Intro to TensorFlow for Deep Learning<a class="headerlink" href="#intro-to-tensorflow-for-deep-learning" title="Permalink to this headline">#</a></h1>
 <section id="what-is-tensorflow">
 <h2><span class="section-number">40.95.1. </span>1. What is TensorFlow<a class="headerlink" href="#what-is-tensorflow" title="Permalink to this headline">#</a></h2>
diff --git a/assignments/machine-learning-productionization/debugging-in-classification.html b/assignments/machine-learning-productionization/debugging-in-classification.html
index faf586dc37..316f2714c8 100644
--- a/assignments/machine-learning-productionization/debugging-in-classification.html
+++ b/assignments/machine-learning-productionization/debugging-in-classification.html
@@ -1845,7 +1845,7 @@ <h2><span class="section-number">40.84.1. </span>Load MNIST Data<a class="header
 
 <span class="c1"># Load data</span>
 <span class="n">mnistDf_backup</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://download.mlcc.google.com/mledu-datasets/mnist_train_small.csv&quot;</span><span class="p">,</span>
+  <span class="s2">&quot;https://static-1300131294.cos.ap-shanghai.myqcloud.com/data/mnist_train_small.csv&quot;</span><span class="p">,</span>
   <span class="n">sep</span><span class="o">=</span><span class="s2">&quot;,&quot;</span><span class="p">,</span>
   <span class="n">header</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
 <span class="c1"># Shuffle data</span>
diff --git a/assignments/machine-learning-productionization/debugging-in-regression.html b/assignments/machine-learning-productionization/debugging-in-regression.html
index 1729b639ed..9020c6e4d1 100644
--- a/assignments/machine-learning-productionization/debugging-in-regression.html
+++ b/assignments/machine-learning-productionization/debugging-in-regression.html
@@ -1907,7 +1907,7 @@ <h2><span class="section-number">40.85.1. </span>Setup<a class="headerlink" href
 <span class="n">pd</span><span class="o">.</span><span class="n">options</span><span class="o">.</span><span class="n">display</span><span class="o">.</span><span class="n">float_format</span> <span class="o">=</span> <span class="s1">&#39;</span><span class="si">{:.1f}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span>
 
 <span class="n">wineDf</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://download.mlcc.google.com/mledu-datasets/winequality.csv&quot;</span><span class="p">,</span>
+  <span class="s2">&quot;https://static-1300131294.cos.ap-shanghai.myqcloud.com/data/winequality.csv&quot;</span><span class="p">,</span>
   <span class="n">encoding</span><span class="o">=</span><span class="s1">&#39;latin-1&#39;</span><span class="p">)</span>
 <span class="n">wineDf</span><span class="o">.</span><span class="n">columns</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;fixed acidity&#39;</span><span class="p">,</span><span class="s1">&#39;volatile acidity&#39;</span><span class="p">,</span><span class="s1">&#39;citric acid&#39;</span><span class="p">,</span>
                      <span class="s1">&#39;residual sugar&#39;</span><span class="p">,</span><span class="s1">&#39;chlorides&#39;</span><span class="p">,</span><span class="s1">&#39;free sulfur dioxide&#39;</span><span class="p">,</span>
diff --git a/data-science/data-visualization/visualization-relationships.html b/data-science/data-visualization/visualization-relationships.html
index 8b28f6742e..bd0372b3ee 100644
--- a/data-science/data-visualization/visualization-relationships.html
+++ b/data-science/data-visualization/visualization-relationships.html
@@ -2029,7 +2029,7 @@ <h2><span class="section-number">6.3.3. </span>Facet grids<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;seaborn.axisgrid.FacetGrid at 0x7fe404455940&gt;
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;seaborn.axisgrid.FacetGrid at 0x7ff0c05b58b0&gt;
 </pre></div>
 </div>
 <img alt="../../_images/visualization-relationships_18_1.png" src="../../_images/visualization-relationships_18_1.png" />
diff --git a/data-science/introduction/introduction-to-statistics-and-probability.html b/data-science/introduction/introduction-to-statistics-and-probability.html
index d8eff0f8cc..8e7d83f702 100644
--- a/data-science/introduction/introduction-to-statistics-and-probability.html
+++ b/data-science/introduction/introduction-to-statistics-and-probability.html
@@ -1912,10 +1912,10 @@ <h2><span class="section-number">4.4.6. </span>Normal distribution<a class="head
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>array([11.28811917,  9.17295329,  6.56371529,  9.84241983,  7.27205865,
-        8.01697559, 12.43056531,  9.00755935, 10.91226239,  8.97359615,
-       10.02707036, 10.79299852, 11.5942073 ,  7.83968173,  7.2937034 ,
-       12.07886138, 13.57946497, 10.92063479,  8.77085797, 10.20937492])
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>array([10.24742603,  9.18504561,  6.94582554, 12.90321193, 10.31306506,
+       13.29698784, 10.99021735,  7.11176853,  9.49297535, 10.57585147,
+       12.36956393, 10.47596834, 12.64111873,  9.62806819, 11.70137462,
+        7.26499903,  9.51998705,  6.31077483, 10.7870969 , 11.51963014])
 </pre></div>
 </div>
 </div>
diff --git a/data-science/working-with-data/numpy.html b/data-science/working-with-data/numpy.html
index 2225a65060..bcf4566e04 100644
--- a/data-science/working-with-data/numpy.html
+++ b/data-science/working-with-data/numpy.html
@@ -2670,7 +2670,7 @@ <h3><span class="section-number">5.3.1.3. </span>NumPy array attributes<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;memory at 0x7f806c85fd40&gt;
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;memory at 0x7f12d2318d40&gt;
 </pre></div>
 </div>
 </div>
diff --git a/data-science/working-with-data/pandas/data-selection.html b/data-science/working-with-data/pandas/data-selection.html
index bd10b561c9..cd6dc9bc74 100644
--- a/data-science/working-with-data/pandas/data-selection.html
+++ b/data-science/working-with-data/pandas/data-selection.html
@@ -1834,24 +1834,24 @@ <h2><span class="section-number">5.4.2.2. </span>Selection by label<a class="hea
   <tbody>
     <tr>
       <th>2013-01-02</th>
-      <td>-0.456435</td>
-      <td>-0.830210</td>
-      <td>-0.891633</td>
-      <td>0.485772</td>
+      <td>-0.261520</td>
+      <td>-1.015650</td>
+      <td>1.644667</td>
+      <td>1.013260</td>
     </tr>
     <tr>
       <th>2013-01-03</th>
-      <td>-1.859359</td>
-      <td>-0.539319</td>
-      <td>0.474526</td>
-      <td>-0.253166</td>
+      <td>0.429661</td>
+      <td>1.416328</td>
+      <td>1.339036</td>
+      <td>0.498855</td>
     </tr>
     <tr>
       <th>2013-01-04</th>
-      <td>0.699300</td>
-      <td>0.948732</td>
-      <td>0.784142</td>
-      <td>0.845687</td>
+      <td>-0.205649</td>
+      <td>-0.139832</td>
+      <td>-1.897922</td>
+      <td>-0.426337</td>
     </tr>
   </tbody>
 </table>
@@ -1919,10 +1919,10 @@ <h2><span class="section-number">5.4.2.2. </span>Selection by label<a class="hea
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>c    0.038589
-d   -0.197195
-e   -0.130250
-f    0.067106
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>c    1.284624
+d   -0.508137
+e    1.510474
+f    0.757249
 dtype: float64
 </pre></div>
 </div>
@@ -1978,7 +1978,7 @@ <h2><span class="section-number">5.4.2.2. </span>Selection by label<a class="hea
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>-0.9518306057687721
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>1.64675837211757
 </pre></div>
 </div>
 </div>
@@ -2035,8 +2035,8 @@ <h2><span class="section-number">5.4.2.2. </span>Selection by label<a class="hea
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>a    0.004982
-b   -0.951831
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>a   -0.579541
+b    1.646758
 c    0.000000
 d    0.000000
 e    0.000000
@@ -2123,24 +2123,24 @@ <h2><span class="section-number">5.4.2.2. </span>Selection by label<a class="hea
   <tbody>
     <tr>
       <th>a</th>
-      <td>1.699244</td>
-      <td>-1.280140</td>
-      <td>0.683763</td>
-      <td>-0.078884</td>
+      <td>-0.261255</td>
+      <td>0.563448</td>
+      <td>-0.587615</td>
+      <td>0.125141</td>
     </tr>
     <tr>
       <th>b</th>
-      <td>0.210853</td>
-      <td>-1.048199</td>
-      <td>-0.302834</td>
-      <td>-2.402504</td>
+      <td>1.574199</td>
+      <td>-1.116184</td>
+      <td>-1.666234</td>
+      <td>1.314941</td>
     </tr>
     <tr>
       <th>d</th>
-      <td>-0.639539</td>
-      <td>0.850331</td>
-      <td>1.754627</td>
-      <td>-0.189413</td>
+      <td>0.135674</td>
+      <td>-2.411222</td>
+      <td>-1.157352</td>
+      <td>0.860725</td>
     </tr>
   </tbody>
 </table>
@@ -2221,21 +2221,21 @@ <h2><span class="section-number">5.4.2.2. </span>Selection by label<a class="hea
   <tbody>
     <tr>
       <th>d</th>
-      <td>-0.639539</td>
-      <td>0.850331</td>
-      <td>1.754627</td>
+      <td>0.135674</td>
+      <td>-2.411222</td>
+      <td>-1.157352</td>
     </tr>
     <tr>
       <th>e</th>
-      <td>2.519738</td>
-      <td>1.375126</td>
-      <td>0.338291</td>
+      <td>-0.151435</td>
+      <td>-1.138838</td>
+      <td>0.437103</td>
     </tr>
     <tr>
       <th>f</th>
-      <td>1.627156</td>
-      <td>0.865174</td>
-      <td>-0.872900</td>
+      <td>0.741851</td>
+      <td>-0.202684</td>
+      <td>-1.822522</td>
     </tr>
   </tbody>
 </table>
@@ -2249,10 +2249,10 @@ <h2><span class="section-number">5.4.2.2. </span>Selection by label<a class="hea
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>A    1.699244
-B   -1.280140
-C    0.683763
-D   -0.078884
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>A   -0.261255
+B    0.563448
+C   -0.587615
+D    0.125141
 Name: a, dtype: float64
 </pre></div>
 </div>
@@ -2307,10 +2307,10 @@ <h2><span class="section-number">5.4.2.2. </span>Selection by label<a class="hea
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>A     True
-B    False
-C     True
-D    False
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>A    False
+B     True
+C    False
+D     True
 Name: a, dtype: bool
 </pre></div>
 </div>
@@ -2380,40 +2380,40 @@ <h2><span class="section-number">5.4.2.2. </span>Selection by label<a class="hea
   <thead>
     <tr style="text-align: right;">
       <th></th>
-      <th>A</th>
-      <th>C</th>
+      <th>B</th>
+      <th>D</th>
     </tr>
   </thead>
   <tbody>
     <tr>
       <th>a</th>
-      <td>1.699244</td>
-      <td>0.683763</td>
+      <td>0.563448</td>
+      <td>0.125141</td>
     </tr>
     <tr>
       <th>b</th>
-      <td>0.210853</td>
-      <td>-0.302834</td>
+      <td>-1.116184</td>
+      <td>1.314941</td>
     </tr>
     <tr>
       <th>c</th>
-      <td>-2.148546</td>
-      <td>-0.813316</td>
+      <td>1.297326</td>
+      <td>-0.802559</td>
     </tr>
     <tr>
       <th>d</th>
-      <td>-0.639539</td>
-      <td>1.754627</td>
+      <td>-2.411222</td>
+      <td>0.860725</td>
     </tr>
     <tr>
       <th>e</th>
-      <td>2.519738</td>
-      <td>0.338291</td>
+      <td>-1.138838</td>
+      <td>0.100947</td>
     </tr>
     <tr>
       <th>f</th>
-      <td>1.627156</td>
-      <td>-0.872900</td>
+      <td>-0.202684</td>
+      <td>0.818771</td>
     </tr>
   </tbody>
 </table>
@@ -2512,17 +2512,17 @@ <h2><span class="section-number">5.4.2.2. </span>Selection by label<a class="hea
   <tbody>
     <tr>
       <th>a</th>
-      <td>1.699244</td>
-      <td>-1.280140</td>
-      <td>0.683763</td>
-      <td>-0.078884</td>
+      <td>-0.261255</td>
+      <td>0.563448</td>
+      <td>-0.587615</td>
+      <td>0.125141</td>
     </tr>
     <tr>
       <th>c</th>
-      <td>-2.148546</td>
-      <td>-1.361452</td>
-      <td>-0.813316</td>
-      <td>0.347396</td>
+      <td>-0.763426</td>
+      <td>1.297326</td>
+      <td>0.834685</td>
+      <td>-0.802559</td>
     </tr>
   </tbody>
 </table>
@@ -2579,7 +2579,7 @@ <h2><span class="section-number">5.4.2.2. </span>Selection by label<a class="hea
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>1.699243786401637
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>-0.2612545763775011
 </pre></div>
 </div>
 </div>
@@ -2903,9 +2903,9 @@ <h2><span class="section-number">5.4.2.4. </span>Selection by position<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>0    0.608692
-2    1.039676
-4    0.704550
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0    0.547894
+2   -1.356373
+4    2.255724
 dtype: float64
 </pre></div>
 </div>
@@ -2957,7 +2957,7 @@ <h2><span class="section-number">5.4.2.4. </span>Selection by position<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>1.6168817892052105
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.8768241394770284
 </pre></div>
 </div>
 </div>
@@ -3013,8 +3013,8 @@ <h2><span class="section-number">5.4.2.4. </span>Selection by position<a class="
 <div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0    0.000000
 2    0.000000
 4    0.000000
-6    1.616882
-8   -0.946230
+6    0.876824
+8    0.568045
 dtype: float64
 </pre></div>
 </div>
@@ -3098,24 +3098,24 @@ <h2><span class="section-number">5.4.2.4. </span>Selection by position<a class="
   <tbody>
     <tr>
       <th>0</th>
-      <td>0.951573</td>
-      <td>1.408150</td>
-      <td>1.863327</td>
-      <td>-1.520441</td>
+      <td>0.735253</td>
+      <td>-0.586162</td>
+      <td>0.721773</td>
+      <td>-0.972412</td>
     </tr>
     <tr>
       <th>2</th>
-      <td>0.336735</td>
-      <td>1.495843</td>
-      <td>-0.695248</td>
-      <td>0.940683</td>
+      <td>1.270549</td>
+      <td>2.973469</td>
+      <td>3.005324</td>
+      <td>2.231575</td>
     </tr>
     <tr>
       <th>4</th>
-      <td>0.228899</td>
-      <td>0.356077</td>
-      <td>-0.637129</td>
-      <td>1.176355</td>
+      <td>0.822420</td>
+      <td>-1.235388</td>
+      <td>-0.538499</td>
+      <td>-0.325967</td>
     </tr>
   </tbody>
 </table>
@@ -3190,23 +3190,23 @@ <h2><span class="section-number">5.4.2.4. </span>Selection by position<a class="
   <tbody>
     <tr>
       <th>2</th>
-      <td>-0.695248</td>
-      <td>0.940683</td>
+      <td>3.005324</td>
+      <td>2.231575</td>
     </tr>
     <tr>
       <th>4</th>
-      <td>-0.637129</td>
-      <td>1.176355</td>
+      <td>-0.538499</td>
+      <td>-0.325967</td>
     </tr>
     <tr>
       <th>6</th>
-      <td>0.229872</td>
-      <td>0.658689</td>
+      <td>1.623229</td>
+      <td>-2.134257</td>
     </tr>
     <tr>
       <th>8</th>
-      <td>-0.349288</td>
-      <td>-0.935375</td>
+      <td>-0.388910</td>
+      <td>-0.628673</td>
     </tr>
   </tbody>
 </table>
@@ -3245,18 +3245,18 @@ <h2><span class="section-number">5.4.2.4. </span>Selection by position<a class="
   <tbody>
     <tr>
       <th>2</th>
-      <td>1.495843</td>
-      <td>0.940683</td>
+      <td>2.973469</td>
+      <td>2.231575</td>
     </tr>
     <tr>
       <th>6</th>
-      <td>0.027112</td>
-      <td>0.658689</td>
+      <td>1.554299</td>
+      <td>-2.134257</td>
     </tr>
     <tr>
       <th>10</th>
-      <td>-0.322177</td>
-      <td>1.171140</td>
+      <td>0.323439</td>
+      <td>-0.032338</td>
     </tr>
   </tbody>
 </table>
@@ -3333,17 +3333,17 @@ <h2><span class="section-number">5.4.2.4. </span>Selection by position<a class="
   <tbody>
     <tr>
       <th>2</th>
-      <td>0.336735</td>
-      <td>1.495843</td>
-      <td>-0.695248</td>
-      <td>0.940683</td>
+      <td>1.270549</td>
+      <td>2.973469</td>
+      <td>3.005324</td>
+      <td>2.231575</td>
     </tr>
     <tr>
       <th>4</th>
-      <td>0.228899</td>
-      <td>0.356077</td>
-      <td>-0.637129</td>
-      <td>1.176355</td>
+      <td>0.822420</td>
+      <td>-1.235388</td>
+      <td>-0.538499</td>
+      <td>-0.325967</td>
     </tr>
   </tbody>
 </table>
@@ -3424,33 +3424,33 @@ <h2><span class="section-number">5.4.2.4. </span>Selection by position<a class="
   <tbody>
     <tr>
       <th>0</th>
-      <td>1.408150</td>
-      <td>1.863327</td>
+      <td>-0.586162</td>
+      <td>0.721773</td>
     </tr>
     <tr>
       <th>2</th>
-      <td>1.495843</td>
-      <td>-0.695248</td>
+      <td>2.973469</td>
+      <td>3.005324</td>
     </tr>
     <tr>
       <th>4</th>
-      <td>0.356077</td>
-      <td>-0.637129</td>
+      <td>-1.235388</td>
+      <td>-0.538499</td>
     </tr>
     <tr>
       <th>6</th>
-      <td>0.027112</td>
-      <td>0.229872</td>
+      <td>1.554299</td>
+      <td>1.623229</td>
     </tr>
     <tr>
       <th>8</th>
-      <td>2.044324</td>
-      <td>-0.349288</td>
+      <td>0.549486</td>
+      <td>-0.388910</td>
     </tr>
     <tr>
       <th>10</th>
-      <td>-0.322177</td>
-      <td>-0.441221</td>
+      <td>0.323439</td>
+      <td>-1.472784</td>
     </tr>
   </tbody>
 </table>
@@ -3504,7 +3504,7 @@ <h2><span class="section-number">5.4.2.4. </span>Selection by position<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>1.495843419947373
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2.9734688264008327
 </pre></div>
 </div>
 </div>
@@ -3558,10 +3558,10 @@ <h2><span class="section-number">5.4.2.4. </span>Selection by position<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>0    0.336735
-2    1.495843
-4   -0.695248
-6    0.940683
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0    1.270549
+2    2.973469
+4    3.005324
+6    2.231575
 Name: 2, dtype: float64
 </pre></div>
 </div>
@@ -3853,23 +3853,23 @@ <h2><span class="section-number">5.4.2.4. </span>Selection by position<a class="
   <tbody>
     <tr>
       <th>0</th>
-      <td>-0.778377</td>
+      <td>0.399147</td>
     </tr>
     <tr>
       <th>1</th>
-      <td>-0.217388</td>
+      <td>-1.580793</td>
     </tr>
     <tr>
       <th>2</th>
-      <td>0.302417</td>
+      <td>0.262044</td>
     </tr>
     <tr>
       <th>3</th>
-      <td>-0.109041</td>
+      <td>-0.000525</td>
     </tr>
     <tr>
       <th>4</th>
-      <td>2.060042</td>
+      <td>-2.450391</td>
     </tr>
   </tbody>
 </table>
@@ -3946,8 +3946,8 @@ <h2><span class="section-number">5.4.2.4. </span>Selection by position<a class="
   <tbody>
     <tr>
       <th>4</th>
-      <td>1.555099</td>
-      <td>2.060042</td>
+      <td>0.727753</td>
+      <td>-2.450391</td>
     </tr>
   </tbody>
 </table>
@@ -4166,39 +4166,32 @@ <h2><span class="section-number">5.4.2.5. </span>Selection by callable<a class="
   </thead>
   <tbody>
     <tr>
-      <th>b</th>
-      <td>0.920658</td>
-      <td>0.709170</td>
-      <td>0.323474</td>
-      <td>-0.541892</td>
+      <th>a</th>
+      <td>1.026795</td>
+      <td>-1.619999</td>
+      <td>1.388878</td>
+      <td>0.187921</td>
     </tr>
     <tr>
-      <th>c</th>
-      <td>0.945481</td>
-      <td>-2.714789</td>
-      <td>0.798567</td>
-      <td>0.015424</td>
+      <th>b</th>
+      <td>1.602264</td>
+      <td>0.621702</td>
+      <td>-0.477629</td>
+      <td>1.482073</td>
     </tr>
     <tr>
-      <th>d</th>
-      <td>0.259017</td>
-      <td>-0.601825</td>
-      <td>1.002502</td>
-      <td>-0.181562</td>
+      <th>c</th>
+      <td>0.648559</td>
+      <td>-0.173165</td>
+      <td>0.641570</td>
+      <td>-0.912225</td>
     </tr>
     <tr>
       <th>e</th>
-      <td>2.184927</td>
-      <td>0.048558</td>
-      <td>0.255997</td>
-      <td>-2.783334</td>
-    </tr>
-    <tr>
-      <th>f</th>
-      <td>0.153795</td>
-      <td>0.786828</td>
-      <td>-0.450318</td>
-      <td>-1.326000</td>
+      <td>0.171712</td>
+      <td>-0.927714</td>
+      <td>-0.307219</td>
+      <td>0.331830</td>
     </tr>
   </tbody>
 </table>
@@ -4273,33 +4266,33 @@ <h2><span class="section-number">5.4.2.5. </span>Selection by callable<a class="
   <tbody>
     <tr>
       <th>a</th>
-      <td>-0.248610</td>
-      <td>0.161584</td>
+      <td>1.026795</td>
+      <td>-1.619999</td>
     </tr>
     <tr>
       <th>b</th>
-      <td>0.920658</td>
-      <td>0.709170</td>
+      <td>1.602264</td>
+      <td>0.621702</td>
     </tr>
     <tr>
       <th>c</th>
-      <td>0.945481</td>
-      <td>-2.714789</td>
+      <td>0.648559</td>
+      <td>-0.173165</td>
     </tr>
     <tr>
       <th>d</th>
-      <td>0.259017</td>
-      <td>-0.601825</td>
+      <td>-1.229180</td>
+      <td>-0.622595</td>
     </tr>
     <tr>
       <th>e</th>
-      <td>2.184927</td>
-      <td>0.048558</td>
+      <td>0.171712</td>
+      <td>-0.927714</td>
     </tr>
     <tr>
       <th>f</th>
-      <td>0.153795</td>
-      <td>0.786828</td>
+      <td>-0.419989</td>
+      <td>-0.777766</td>
     </tr>
   </tbody>
 </table>
@@ -4380,33 +4373,33 @@ <h2><span class="section-number">5.4.2.5. </span>Selection by callable<a class="
   <tbody>
     <tr>
       <th>a</th>
-      <td>-0.248610</td>
-      <td>0.161584</td>
+      <td>1.026795</td>
+      <td>-1.619999</td>
     </tr>
     <tr>
       <th>b</th>
-      <td>0.920658</td>
-      <td>0.709170</td>
+      <td>1.602264</td>
+      <td>0.621702</td>
     </tr>
     <tr>
       <th>c</th>
-      <td>0.945481</td>
-      <td>-2.714789</td>
+      <td>0.648559</td>
+      <td>-0.173165</td>
     </tr>
     <tr>
       <th>d</th>
-      <td>0.259017</td>
-      <td>-0.601825</td>
+      <td>-1.229180</td>
+      <td>-0.622595</td>
     </tr>
     <tr>
       <th>e</th>
-      <td>2.184927</td>
-      <td>0.048558</td>
+      <td>0.171712</td>
+      <td>-0.927714</td>
     </tr>
     <tr>
       <th>f</th>
-      <td>0.153795</td>
-      <td>0.786828</td>
+      <td>-0.419989</td>
+      <td>-0.777766</td>
     </tr>
   </tbody>
 </table>
@@ -4460,12 +4453,12 @@ <h2><span class="section-number">5.4.2.5. </span>Selection by callable<a class="
 </div>
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--- a/data-science/working-with-data/pandas/introduction-and-data-structures.html
+++ b/data-science/working-with-data/pandas/introduction-and-data-structures.html
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-      <td>0.712584</td>
-      <td>2.792353</td>
-      <td>0.617499</td>
-      <td>-0.342805</td>
+      <td>-5.919119</td>
+      <td>-23.711214</td>
+      <td>-0.737310</td>
+      <td>-0.412435</td>
     </tr>
     <tr>
       <th>3</th>
-      <td>7.577377</td>
-      <td>1.874346</td>
-      <td>3.177611</td>
-      <td>-6.396355</td>
+      <td>1.054199</td>
+      <td>-42.121185</td>
+      <td>0.546683</td>
+      <td>1.041005</td>
     </tr>
     <tr>
       <th>4</th>
-      <td>0.982243</td>
-      <td>1.677348</td>
-      <td>-1.781578</td>
-      <td>1.073788</td>
+      <td>0.606016</td>
+      <td>2.565555</td>
+      <td>-4.923781</td>
+      <td>-0.549147</td>
     </tr>
     <tr>
       <th>5</th>
-      <td>-7.517254</td>
-      <td>0.513049</td>
-      <td>-0.918334</td>
-      <td>-0.806585</td>
+      <td>-2.465164</td>
+      <td>-2.365436</td>
+      <td>-3.728700</td>
+      <td>0.382503</td>
     </tr>
     <tr>
       <th>6</th>
-      <td>-1.048699</td>
-      <td>-2.292212</td>
-      <td>2.575790</td>
-      <td>-0.840010</td>
+      <td>1.249326</td>
+      <td>-0.966944</td>
+      <td>5.519267</td>
+      <td>1.937077</td>
     </tr>
     <tr>
       <th>7</th>
-      <td>-0.721554</td>
-      <td>-1.375442</td>
-      <td>0.700960</td>
-      <td>-1.936885</td>
+      <td>2.236756</td>
+      <td>1.683613</td>
+      <td>-16.613452</td>
+      <td>-3.707162</td>
     </tr>
     <tr>
       <th>8</th>
-      <td>17.233678</td>
-      <td>8.803186</td>
-      <td>0.588881</td>
-      <td>-12.737492</td>
+      <td>0.845677</td>
+      <td>0.908069</td>
+      <td>-8.588758</td>
+      <td>0.812430</td>
     </tr>
     <tr>
       <th>9</th>
-      <td>2.470156</td>
-      <td>-1.482055</td>
-      <td>-0.566505</td>
-      <td>-0.971177</td>
+      <td>-1.598558</td>
+      <td>0.718948</td>
+      <td>2.024870</td>
+      <td>12.325266</td>
     </tr>
   </tbody>
 </table>
@@ -4949,73 +4949,73 @@ <h4><span class="section-number">5.4.1.2.2.5. </span>Data alignment and arithmet
   <tbody>
     <tr>
       <th>0</th>
-      <td>8.322860</td>
-      <td>0.031969</td>
-      <td>0.138454</td>
-      <td>0.112183</td>
+      <td>0.000149</td>
+      <td>3.332793e+00</td>
+      <td>0.146968</td>
+      <td>15.820974</td>
     </tr>
     <tr>
       <th>1</th>
-      <td>0.001196</td>
-      <td>0.110356</td>
-      <td>0.182311</td>
-      <td>0.001514</td>
+      <td>0.000098</td>
+      <td>6.453023e-03</td>
+      <td>0.040139</td>
+      <td>1.233219</td>
     </tr>
     <tr>
       <th>2</th>
-      <td>3.878422</td>
-      <td>0.016448</td>
-      <td>6.877897</td>
-      <td>72.411995</td>
+      <td>0.000815</td>
+      <td>3.163624e-06</td>
+      <td>3.383760</td>
+      <td>34.560378</td>
     </tr>
     <tr>
       <th>3</th>
-      <td>0.000303</td>
-      <td>0.081022</td>
-      <td>0.009808</td>
-      <td>0.000597</td>
+      <td>0.809674</td>
+      <td>3.176857e-07</td>
+      <td>11.195903</td>
+      <td>0.851507</td>
     </tr>
     <tr>
       <th>4</th>
-      <td>1.074297</td>
-      <td>0.126330</td>
-      <td>0.099261</td>
-      <td>0.752186</td>
+      <td>7.414182</td>
+      <td>2.308205e-02</td>
+      <td>0.001701</td>
+      <td>10.996238</td>
     </tr>
     <tr>
       <th>5</th>
-      <td>0.000313</td>
-      <td>14.433281</td>
-      <td>1.406037</td>
-      <td>2.362645</td>
+      <td>0.027078</td>
+      <td>3.194147e-02</td>
+      <td>0.005173</td>
+      <td>46.715265</td>
     </tr>
     <tr>
       <th>6</th>
-      <td>0.826793</td>
-      <td>0.036223</td>
-      <td>0.022717</td>
-      <td>2.008453</td>
+      <td>0.410485</td>
+      <td>1.143917e+00</td>
+      <td>0.001078</td>
+      <td>0.071025</td>
     </tr>
     <tr>
       <th>7</th>
-      <td>3.689128</td>
-      <td>0.279403</td>
-      <td>4.142158</td>
-      <td>0.071053</td>
+      <td>0.039951</td>
+      <td>1.244604e-01</td>
+      <td>0.000013</td>
+      <td>0.005295</td>
     </tr>
     <tr>
       <th>8</th>
-      <td>0.000011</td>
-      <td>0.000167</td>
-      <td>8.315544</td>
-      <td>0.000038</td>
+      <td>1.955158</td>
+      <td>1.470702e+00</td>
+      <td>0.000184</td>
+      <td>2.295388</td>
     </tr>
     <tr>
       <th>9</th>
-      <td>0.026860</td>
-      <td>0.207273</td>
-      <td>9.709205</td>
-      <td>1.124105</td>
+      <td>0.153139</td>
+      <td>3.742910e+00</td>
+      <td>0.059486</td>
+      <td>0.000043</td>
     </tr>
   </tbody>
 </table>
@@ -5271,35 +5271,35 @@ <h4><span class="section-number">5.4.1.2.2.6. </span>Transposing<a class="header
   <tbody>
     <tr>
       <th>A</th>
-      <td>-1.698510</td>
-      <td>0.185949</td>
-      <td>1.403343</td>
-      <td>0.131972</td>
-      <td>1.018078</td>
+      <td>-0.110484</td>
+      <td>0.099472</td>
+      <td>-0.168944</td>
+      <td>0.948588</td>
+      <td>1.650121</td>
     </tr>
     <tr>
       <th>B</th>
-      <td>-0.422846</td>
-      <td>-0.576366</td>
-      <td>0.358121</td>
-      <td>0.533519</td>
-      <td>0.596179</td>
+      <td>1.351145</td>
+      <td>0.283427</td>
+      <td>-0.042174</td>
+      <td>-0.023741</td>
+      <td>0.389779</td>
     </tr>
     <tr>
       <th>C</th>
-      <td>0.609995</td>
-      <td>0.653436</td>
-      <td>1.619436</td>
-      <td>0.314702</td>
-      <td>-0.561300</td>
+      <td>-0.619164</td>
+      <td>-0.447600</td>
+      <td>-1.356282</td>
+      <td>1.829215</td>
+      <td>-0.203096</td>
     </tr>
     <tr>
       <th>D</th>
-      <td>0.578738</td>
-      <td>0.197269</td>
-      <td>-2.917109</td>
-      <td>-0.156339</td>
-      <td>0.931282</td>
+      <td>1.994382</td>
+      <td>1.053805</td>
+      <td>-2.424625</td>
+      <td>0.960610</td>
+      <td>-1.821005</td>
     </tr>
   </tbody>
 </table>
@@ -5422,59 +5422,59 @@ <h3><span class="section-number">5.4.1.3.2. </span>Basics<a class="headerlink" h
   <tbody>
     <tr>
       <th>2000-01-01</th>
-      <td>0.801076</td>
-      <td>1.465342</td>
-      <td>-0.166177</td>
-      <td>-0.788715</td>
+      <td>0.266217</td>
+      <td>1.228785</td>
+      <td>1.031277</td>
+      <td>-0.095949</td>
     </tr>
     <tr>
       <th>2000-01-02</th>
-      <td>0.348102</td>
-      <td>-1.058147</td>
-      <td>-1.531875</td>
-      <td>-1.316167</td>
+      <td>1.488999</td>
+      <td>0.037730</td>
+      <td>-0.392898</td>
+      <td>0.215211</td>
     </tr>
     <tr>
       <th>2000-01-03</th>
-      <td>1.736169</td>
-      <td>-0.839949</td>
-      <td>1.481805</td>
-      <td>-0.264777</td>
+      <td>0.038978</td>
+      <td>-2.140597</td>
+      <td>0.568707</td>
+      <td>1.023702</td>
     </tr>
     <tr>
       <th>2000-01-04</th>
-      <td>-0.435348</td>
-      <td>0.694244</td>
-      <td>1.922322</td>
-      <td>-0.349232</td>
+      <td>-0.079610</td>
+      <td>-0.033805</td>
+      <td>0.101702</td>
+      <td>0.303200</td>
     </tr>
     <tr>
       <th>2000-01-05</th>
-      <td>-0.745863</td>
-      <td>-0.692943</td>
-      <td>-0.828683</td>
-      <td>2.427089</td>
+      <td>-2.045461</td>
+      <td>-0.369304</td>
+      <td>-0.452429</td>
+      <td>2.246294</td>
     </tr>
     <tr>
       <th>2000-01-06</th>
-      <td>0.738183</td>
-      <td>0.224374</td>
-      <td>-0.953017</td>
-      <td>-0.067219</td>
+      <td>0.301222</td>
+      <td>-0.225712</td>
+      <td>1.916028</td>
+      <td>0.982501</td>
     </tr>
     <tr>
       <th>2000-01-07</th>
-      <td>0.387631</td>
-      <td>-0.026705</td>
-      <td>-0.647102</td>
-      <td>-0.489077</td>
+      <td>-0.566633</td>
+      <td>0.042583</td>
+      <td>-2.632710</td>
+      <td>0.073967</td>
     </tr>
     <tr>
       <th>2000-01-08</th>
-      <td>-1.247095</td>
-      <td>-0.649027</td>
-      <td>-0.993315</td>
-      <td>0.407961</td>
+      <td>-0.859050</td>
+      <td>-0.604785</td>
+      <td>-1.571164</td>
+      <td>-1.762992</td>
     </tr>
   </tbody>
 </table>
@@ -5494,7 +5494,7 @@ <h3><span class="section-number">5.4.1.3.2. </span>Basics<a class="headerlink" h
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.7381831630668674
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.30122197945169477
 </pre></div>
 </div>
 </div>
@@ -5534,59 +5534,59 @@ <h3><span class="section-number">5.4.1.3.2. </span>Basics<a class="headerlink" h
   <tbody>
     <tr>
       <th>2000-01-01</th>
-      <td>0.801076</td>
-      <td>1.465342</td>
-      <td>-0.166177</td>
-      <td>-0.788715</td>
+      <td>0.266217</td>
+      <td>1.228785</td>
+      <td>1.031277</td>
+      <td>-0.095949</td>
     </tr>
     <tr>
       <th>2000-01-02</th>
-      <td>0.348102</td>
-      <td>-1.058147</td>
-      <td>-1.531875</td>
-      <td>-1.316167</td>
+      <td>1.488999</td>
+      <td>0.037730</td>
+      <td>-0.392898</td>
+      <td>0.215211</td>
     </tr>
     <tr>
       <th>2000-01-03</th>
-      <td>1.736169</td>
-      <td>-0.839949</td>
-      <td>1.481805</td>
-      <td>-0.264777</td>
+      <td>0.038978</td>
+      <td>-2.140597</td>
+      <td>0.568707</td>
+      <td>1.023702</td>
     </tr>
     <tr>
       <th>2000-01-04</th>
-      <td>-0.435348</td>
-      <td>0.694244</td>
-      <td>1.922322</td>
-      <td>-0.349232</td>
+      <td>-0.079610</td>
+      <td>-0.033805</td>
+      <td>0.101702</td>
+      <td>0.303200</td>
     </tr>
     <tr>
       <th>2000-01-05</th>
-      <td>-0.745863</td>
-      <td>-0.692943</td>
-      <td>-0.828683</td>
-      <td>2.427089</td>
+      <td>-2.045461</td>
+      <td>-0.369304</td>
+      <td>-0.452429</td>
+      <td>2.246294</td>
     </tr>
     <tr>
       <th>2000-01-06</th>
-      <td>0.738183</td>
-      <td>0.224374</td>
-      <td>-0.953017</td>
-      <td>-0.067219</td>
+      <td>0.301222</td>
+      <td>-0.225712</td>
+      <td>1.916028</td>
+      <td>0.982501</td>
     </tr>
     <tr>
       <th>2000-01-07</th>
-      <td>0.387631</td>
-      <td>-0.026705</td>
-      <td>-0.647102</td>
-      <td>-0.489077</td>
+      <td>-0.566633</td>
+      <td>0.042583</td>
+      <td>-2.632710</td>
+      <td>0.073967</td>
     </tr>
     <tr>
       <th>2000-01-08</th>
-      <td>-1.247095</td>
-      <td>-0.649027</td>
-      <td>-0.993315</td>
-      <td>0.407961</td>
+      <td>-0.859050</td>
+      <td>-0.604785</td>
+      <td>-1.571164</td>
+      <td>-1.762992</td>
     </tr>
   </tbody>
 </table>
@@ -5627,59 +5627,59 @@ <h3><span class="section-number">5.4.1.3.2. </span>Basics<a class="headerlink" h
   <tbody>
     <tr>
       <th>2000-01-01</th>
-      <td>1.465342</td>
-      <td>0.801076</td>
-      <td>-0.166177</td>
-      <td>-0.788715</td>
+      <td>1.228785</td>
+      <td>0.266217</td>
+      <td>1.031277</td>
+      <td>-0.095949</td>
     </tr>
     <tr>
       <th>2000-01-02</th>
-      <td>-1.058147</td>
-      <td>0.348102</td>
-      <td>-1.531875</td>
-      <td>-1.316167</td>
+      <td>0.037730</td>
+      <td>1.488999</td>
+      <td>-0.392898</td>
+      <td>0.215211</td>
     </tr>
     <tr>
       <th>2000-01-03</th>
-      <td>-0.839949</td>
-      <td>1.736169</td>
-      <td>1.481805</td>
-      <td>-0.264777</td>
+      <td>-2.140597</td>
+      <td>0.038978</td>
+      <td>0.568707</td>
+      <td>1.023702</td>
     </tr>
     <tr>
       <th>2000-01-04</th>
-      <td>0.694244</td>
-      <td>-0.435348</td>
-      <td>1.922322</td>
-      <td>-0.349232</td>
+      <td>-0.033805</td>
+      <td>-0.079610</td>
+      <td>0.101702</td>
+      <td>0.303200</td>
     </tr>
     <tr>
       <th>2000-01-05</th>
-      <td>-0.692943</td>
-      <td>-0.745863</td>
-      <td>-0.828683</td>
-      <td>2.427089</td>
+      <td>-0.369304</td>
+      <td>-2.045461</td>
+      <td>-0.452429</td>
+      <td>2.246294</td>
     </tr>
     <tr>
       <th>2000-01-06</th>
-      <td>0.224374</td>
-      <td>0.738183</td>
-      <td>-0.953017</td>
-      <td>-0.067219</td>
+      <td>-0.225712</td>
+      <td>0.301222</td>
+      <td>1.916028</td>
+      <td>0.982501</td>
     </tr>
     <tr>
       <th>2000-01-07</th>
-      <td>-0.026705</td>
-      <td>0.387631</td>
-      <td>-0.647102</td>
-      <td>-0.489077</td>
+      <td>0.042583</td>
+      <td>-0.566633</td>
+      <td>-2.632710</td>
+      <td>0.073967</td>
     </tr>
     <tr>
       <th>2000-01-08</th>
-      <td>-0.649027</td>
-      <td>-1.247095</td>
-      <td>-0.993315</td>
-      <td>0.407961</td>
+      <td>-0.604785</td>
+      <td>-0.859050</td>
+      <td>-1.571164</td>
+      <td>-1.762992</td>
     </tr>
   </tbody>
 </table>
@@ -5720,43 +5720,43 @@ <h3><span class="section-number">5.4.1.3.2. </span>Basics<a class="headerlink" h
   <tbody>
     <tr>
       <th>2000-01-01</th>
-      <td>1.465342</td>
-      <td>0.801076</td>
+      <td>1.228785</td>
+      <td>0.266217</td>
     </tr>
     <tr>
       <th>2000-01-02</th>
-      <td>-1.058147</td>
-      <td>0.348102</td>
+      <td>0.037730</td>
+      <td>1.488999</td>
     </tr>
     <tr>
       <th>2000-01-03</th>
-      <td>-0.839949</td>
-      <td>1.736169</td>
+      <td>-2.140597</td>
+      <td>0.038978</td>
     </tr>
     <tr>
       <th>2000-01-04</th>
-      <td>0.694244</td>
-      <td>-0.435348</td>
+      <td>-0.033805</td>
+      <td>-0.079610</td>
     </tr>
     <tr>
       <th>2000-01-05</th>
-      <td>-0.692943</td>
-      <td>-0.745863</td>
+      <td>-0.369304</td>
+      <td>-2.045461</td>
     </tr>
     <tr>
       <th>2000-01-06</th>
-      <td>0.224374</td>
-      <td>0.738183</td>
+      <td>-0.225712</td>
+      <td>0.301222</td>
     </tr>
     <tr>
       <th>2000-01-07</th>
-      <td>-0.026705</td>
-      <td>0.387631</td>
+      <td>0.042583</td>
+      <td>-0.566633</td>
     </tr>
     <tr>
       <th>2000-01-08</th>
-      <td>-0.649027</td>
-      <td>-1.247095</td>
+      <td>-0.604785</td>
+      <td>-0.859050</td>
     </tr>
   </tbody>
 </table>
@@ -5795,43 +5795,43 @@ <h3><span class="section-number">5.4.1.3.2. </span>Basics<a class="headerlink" h
   <tbody>
     <tr>
       <th>2000-01-01</th>
-      <td>1.465342</td>
-      <td>0.801076</td>
+      <td>1.228785</td>
+      <td>0.266217</td>
     </tr>
     <tr>
       <th>2000-01-02</th>
-      <td>-1.058147</td>
-      <td>0.348102</td>
+      <td>0.037730</td>
+      <td>1.488999</td>
     </tr>
     <tr>
       <th>2000-01-03</th>
-      <td>-0.839949</td>
-      <td>1.736169</td>
+      <td>-2.140597</td>
+      <td>0.038978</td>
     </tr>
     <tr>
       <th>2000-01-04</th>
-      <td>0.694244</td>
-      <td>-0.435348</td>
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-       [-0.11608994,  0.09930851],
-       [ 1.01964571, -0.2813047 ],
-       [-0.25504546,  0.04543063],
-       [ 0.10152314,  0.08272095],
-       [-0.87898966, -0.48385257],
-       [-0.79378229, -0.47716821],
-       [ 0.38252217,  0.68679926],
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-       [-0.19008824, -0.86332183],
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-       [-0.95197068,  0.00879897],
-       [ 0.04477681,  0.08715884],
-       [ 0.85692156, -0.2580265 ],
-       [-0.18163865,  0.18257323],
-       [ 0.56121819, -0.89225434],
-       [-0.14069707,  0.06850504],
-       [ 0.08698948, -0.00401784],
-       [-1.01465911,  0.3396538 ],
-       [-0.88714939,  0.63621048],
-       [-0.155571  , -0.03980251],
-       [ 0.13891479, -0.19425922],
-       [ 1.09765538, -0.02026493]]), y=array([1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0,
-       0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0,
-       1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1,
-       1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1,
-       0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0]))&gt;
+{"version_major": 2, "version_minor": 0, "model_id": "b58222c37c0848848417a6aec4ffa1b6"}
+</script><div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;function __main__.plot_3D(elev=30, azim=30, X=array([[-0.14848999,  0.17733417],
+       [ 0.170089  , -0.16879484],
+       [-0.12331707, -0.92372479],
+       [ 0.14978865, -0.03596801],
+       [ 0.54440817,  0.66649003],
+       [ 0.91876559, -0.79579236],
+       [ 0.99706526, -0.1212961 ],
+       [ 0.2554739 ,  1.05198953],
+       [ 0.08519487, -0.21523882],
+       [-0.99504126,  0.48991494],
+       [ 0.47009906,  0.81918994],
+       [-0.05628975, -0.19705435],
+       [-0.06148206,  0.15341799],
+       [-0.40885543,  0.92457735],
+       [-0.85972615,  0.8295671 ],
+       [ 0.01869547,  0.00222574],
+       [-0.16620431,  0.18986843],
+       [-0.53846659, -0.6694117 ],
+       [-0.19470724, -0.0193433 ],
+       [ 0.1165102 ,  0.2054175 ],
+       [-0.00995049, -0.07288576],
+       [ 0.81917904,  0.39846134],
+       [-0.10683682, -0.07620444],
+       [-0.18794649, -1.05788462],
+       [ 0.01217664,  0.16336323],
+       [ 0.13078778,  0.17426385],
+       [-0.09784592,  0.17249158],
+       [ 0.77522849, -0.17453352],
+       [ 0.04382313,  1.01406812],
+       [-0.14758293,  0.01950532],
+       [ 0.02052251,  0.15664383],
+       [ 0.84580961,  0.30452167],
+       [ 0.02375428,  0.05969836],
+       [-0.87961142, -0.52095595],
+       [-0.03625464, -0.37240364],
+       [ 0.21597874,  0.11993992],
+       [-0.10742728, -0.11155046],
+       [-0.97920397, -0.07369176],
+       [ 0.01243166, -0.00583928],
+       [ 0.59043542, -0.62203419],
+       [ 0.69960372, -0.83404985],
+       [ 0.14727722, -0.02386957],
+       [ 0.0060676 , -0.00954399],
+       [ 0.21559742,  0.05595726],
+       [-0.35348823,  0.90852496],
+       [ 0.41841879,  0.82843148],
+       [-0.16938064,  0.93768222],
+       [-0.04690438,  0.01380554],
+       [-0.07632558,  0.12291919],
+       [ 0.93200641, -0.44580722],
+       [-0.98188384, -0.22927463],
+       [-0.97751166,  0.16962453],
+       [-0.57525185,  0.60186491],
+       [-0.14739084, -0.04949411],
+       [ 0.09804627,  0.18031459],
+       [ 0.03500074, -0.02634081],
+       [ 0.50786978, -1.04925824],
+       [-0.08731882, -0.03156234],
+       [ 0.18300793, -0.07047682],
+       [-0.10543128, -0.17762792],
+       [ 1.02388585,  0.26583684],
+       [-0.0898721 , -0.95238612],
+       [ 0.96755745,  0.50877145],
+       [-0.07744817,  0.16905845],
+       [ 0.84004275, -0.51798938],
+       [ 0.41532714, -0.83113785],
+       [ 0.14532358,  1.07332987],
+       [-0.89788068, -0.26664917],
+       [-0.03302014,  0.82516437],
+       [-0.55799765, -0.84728473],
+       [ 0.19168024,  0.02961706],
+       [-0.5789342 , -0.80691691],
+       [-0.05418754, -0.04537884],
+       [ 0.07885114,  0.0731486 ],
+       [-0.13009751,  0.01559291],
+       [ 0.09425421, -0.06187374],
+       [-0.14579523,  0.00605812],
+       [ 0.02699234, -0.08634388],
+       [-0.93930447, -0.62691627],
+       [-0.08485438, -0.13741474],
+       [-0.77118415, -0.61881126],
+       [ 1.03394629, -0.09440484],
+       [-0.91165711,  0.20099994],
+       [ 0.19663177,  0.25672968],
+       [ 0.95074758,  0.61759966],
+       [ 0.16717129, -0.04242818],
+       [-0.22838073,  0.96231377],
+       [-0.26222688, -1.01146794],
+       [ 0.72553862, -0.39029111],
+       [-0.06584067,  0.05428221],
+       [ 0.225225  ,  0.04883436],
+       [-0.10351476,  0.067387  ],
+       [-0.51261137, -0.98922493],
+       [-1.01819662,  0.1151869 ],
+       [ 0.26803518, -1.04187376],
+       [-0.49242864,  0.882162  ],
+       [ 0.80821987,  0.79829679],
+       [ 0.07523394, -0.13732133],
+       [ 0.03547556, -0.03405817],
+       [-0.00290804, -0.18180785]]), y=array([1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0,
+       1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1,
+       0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0,
+       0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0,
+       0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1]))&gt;
 </pre></div>
 </div>
 </div>
@@ -2239,7 +2239,7 @@ <h2><span class="section-number">20.4. </span>Beyond linear boundaries: Kernel S
 </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 0x7f2c37ed18e0&gt;
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;matplotlib.collections.PathCollection at 0x7fa8202478b0&gt;
 </pre></div>
 </div>
 <img alt="../_images/kernel-method_39_1.png" src="../_images/kernel-method_39_1.png" />
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"}}]}}, 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"}}]}}, 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q9WWtNWiNWrXjpMcLIypCFhuhU0PIlGmmntvmoDBSPWejlKZcrgbqucg49Zx/H0WxWGqpnpsuIpdjybMOAsdBR5p/eYtyGXbYAfHkkziOQ2FLP8Zvfo3xtG8d5Bx1FN7e+0z/PPr66DnkmX6RsyZoHQI4DkIKBv9yG2rNmkmPGTo4hEyFbndqCGXfIW3Tae+5VMpfXZXLFcrlSsN7TuRyWH+8ETkwiM5kqB11NHrp0mmft06ncY59OfYvf+E/xwQtMwH+7Wefvc17LhLBe9XJ037eceexdCmV17+B2Pe+i3Zdfy+pfi5ag+ch0FRPfk1bxQh8gUMkYjeEBbNBXeCQyaSCiw1f4NDfP0Q0GmkIHHyX8Hjj53jcplRyQxl4SNuEK6SQSZlN77liccRwa61G/MsXEbn8MmShgEYgtEbHY1RPfjXFj34MEolpvQbjnrvJnnwSVKvoaHRUURJaQ6WC7ulh+Pd/wFs19XZc2zgOyfPeS+S6a4Ft3TshAA21l76U/Le/C7H2nSYiEZtUKsHwcH5WIir881RkMimklI2IimKxjOt6ZLMpBgaGsSyTZDJOX98Q8XiUeDzKwEAOpcKdgW6h21dIYUEKacmctedcl+S73knk979FSwnRWGBoqqBaQXgezmGHk/vxpW25bU9E5A+/J/med/uCAa3BMBCehxYC3dND7kc/xj3kOZ375TVDa6y//ZXIpZdg/eMfoDXuQQdROet1OEcdPfle1wRkMkmEkLPm4KC1xjAkPT2ZEXLvWKNFJ6Wgr2+IpUt78DwPw5AYhkGtVgsdHLqIsCCFLEgsS5JMRjFNY1R7Lh6PjmvPxWKRce25kSsnIUTjPmPbc3Xsn19P6txz0HZkYmm24yCqFYqf+gyVN7xx2q8rsWUT0SuvgGuugaEhVO9SKq9+NZXXnjplGXc3YRiSbDZNqVSeFQcH8FdJiUScaNTfN+rtzeA4HoVCkd7eLIVCCdd16enJoJQmlyuQzaYYHMzhOIRFqQsIC1LIgmJke04IQalUoVZzOt+eG0P61a/C/PudkGzxgSnkUbvvwdBNf5rWKmJkNIRSiqGhXMfcuLuBbhA45PNFMpkkSim2bh0IBQ5dRrcXpLC5G9Jg7HCr47hEo/bsD7fWapj33A3mJBoby0aufRKxdeuUXtfYaIhisYxhGFjWNCMqupT5dnAAf9asUvFXxaGDQ8hUCVV2IU3Vc7ZtIaXEdT0KhdKM1XNNcd1gd3+Slo4QoEG4Du1+tTVLbrVti0QiNmIfa+FTd3BIpfyiMBsCh7EODnWBQyLhm+OapmxczCSTccrlKoVCiWQyTqWSQ+uwbRfSnHCFtB3TznBrtVoLLGNmcbg1FvOHRt1JioNTQ6czqGXLJ31tlmWSzaaJx2NUKlUGB4dHxYgXCiWklMTj0xNIdCvz6eBgGBLHcUil4qGDQ8i0CAvSdkq73nP1vQjTNGbPe04IKmec5d+7mXO2UgitqZ5yysSih4B2k1tHRlTIaexHdTNz7eDguh5CiOD9EDo4hEyfxfVJDJmU6XjPOY4/3Gjb1qx5z1VPPQ1v9z2gXPKdDEZ+abkulIp4q1ZRfn1zhd1Uk1vHRVQsEuYyogI0mUySWs3BsswwoiJkRoQFaTthJt5zjuOitcayjFnzntOZDLnLf4J3wIFQq0KxgMgNQz6HqFZQe+xJ7vKfolesGPfYydpzrRgZUbGYmAuBg2EYaO0H8hWL5TCiImTGhLLv7YBODLc6jotpGriuh2kas+c9pxTW3/6K/atfIfu2opYsofayY/2B0TEqPCl9JVckYvvec4Vy+2F5I0inkxiG70CwmJgtBwchBPF4jGjUxvO8RlFqx8EhGrVJJuP09+fQOrwenmu6XfYdFqRFTDP13HSHW+PxaCB0qAb7Mm0o42aJkeq5+tX5dJFS0tOTplyuUCpVOniW80+nHRx83zzf1qhU8lWUU3FwqBctz3NDB4d5oNsLUij7XoSM9Z7L50vjvOfy+RJKKRKJbcOthYI/3JrJJJoOtxqGREqjI9EQ02HkcGulUh3RKpwEz8O65c9Err8O8957QGnc/fejeuLJOEcf3RA4VCo1VJP48IVIoVAim00Ti0Vm5OBgmgaJRBzLMqlUtl2QjBU49PUNEotFGhEVvb3ZhsChpyfTEDgsWZLBsqo4jg6LUkiDcIW0yJht7znLsshkUuRyhTmd4ZlRe65aJfGJj2H//ncI10VFo4BAVCogJc5RR1H4woX0rF6J5ylyucKsvpa5ZiYODmPbc7490ES/9zCiYiHQ7SuksCAtEjrdnmvlPZdKJTBNk6Gh4Tmx3mmrPVepYN1+G7KvDx2L4Tz3eQ1vutgXv0D00h+j0xn0WLfwchljcJDKq16Fe+FFpNPJOS+2s40Q0NOTwXHcKUVUjG3PVSrNV1haa2zbv1gZGBgO5pIM+vv9dl2t5lAoFBt7TOVypfFzGFExd4QFKWRWmbNoiFHPWd9zqQbS39mhrfacUkSu+CnRSy9Bbtzgu4PjZyDVjj2OyplnkT79VD+cr6dnwucRuRxCK4avuZ7kAftt9wKHZu25yZhqREUocJh7woIUMmvMZ3JrLObn3QwN5YKi1Tnabs9pTezr/0P0Rz8EIVDZHn9oVilEbhhRLKJWrMTYuAFvxcrmhqxKITdvpnze+6m95a3brcCh/fbcxEw1oiIUOMw93V6QQlHDAmTWk1uLRezf/x7rrn+B6+HtsQfVE09C9/Y2zqFc9meYEol4R/dcmnnPTYR5z91EL7sUHYmis9ltfyElOtuDjsUxHn/cr6mt3BikBAGyv2+Ug8P2JHAY2Z4rFsst23PNCAUOITMlLEgLiNlUz+VyfnvO/sMfSHzso76j9oh47fiXv0T5ne+k/K5zGrcXCiUymVTjGDNhOuo5+xc/h3IFvWrVxHeIRMA0EIWCb0lkNNk81xo0jf2lUqlCJOK3kxaTwGGkg0O1WgvMUKfXnmtG3cEhEomQSsXJ5YqjVpyJRJy+vsGGg0P951QqwcBAHggFDtszYUFaIDRrzyUSiQnac77zst/q2rZ3NLI9l04nAieGfKOYWH/6k5+oWquhk8ltX+BKIUolYl/+Emgon/MeABzHpVqtBTYx0xM4jG3PDQ0V21bPWXfcgY5ERsWRj8Vb0ouZzyOHh1BLeie8jyiXwbaoPf/Ixm3FYpl0OtmRYttN+MXCX9kqpRvtuaGh3JTac62oR1RkMqngfek7OPT3DxGNRhoODr5LeLzxczxuhwKH7ZxwJ7HLmY73XKVSI5crBi27Nr3nPEXis+dDtYpOpUavJqT0C5SUxC7+JmLTpsZfFYtlpBTEYrEpv7apes+Nw3VbFiMAolF0JOKvkpwJCovrIocGcQ86GO/AAxs312oOtZrTaGMtFrT2HcF901OLYrHM0FC+Y8UIxkdU5HKFwDE+Rj5fJBaLYBgGhUIp+P2Kxs9ShpZC2zPhCqlLmYv23EgTVOu2vyGffAIdjzf9ktfxOCKfJ3rdNZTf+W6AQMVXIR6PUq1W2xI4THu4dQzenntgbdjQMhtJlEqoHVagVq3CeOQ/YFmoRBIEiGIRWa3i7rEnxc9+btzrLhRK9PSkicejXS1wELkc9m9/g33TjYjhIdSy5dSOO57ai18CIy4URrbnlFJorae1V9TmWQW/v8yoiIq+vkFqNSdo1w0Ri0UbuUqxWJRUKhoKHLZjwoLUhcxFe26ses546CH/8tk0wXUR5TIiWFFo20bHYv6qSWuMhx8e9dh2BQ4zac9NRPWVJ2HdeitUKhCdINdIKWSpSOW1p1B+z3uJ/vRy7OuvR/b3AxrV00P5v06kcsaZjZml0Q+fQ4GD6/oruGh08lXfCMx//ZPk+9+H3LgJ0GjDwPD+jX3zn/B234P817+J3n33Ueo5P7pdd8TBoRmhwCFkOoQFqYuYdfVcK+85KUFrfyanXB4V/yCqVUSh4Lfy6vcdw2QCh6mo59rFOepo3EMPw/rrX1DZntGru1oN2bcVb8VKKmedje7pofyucyi/6S3I9esQWuOt2hHird2wZ1XgoDXmnXcQ+fnPsW7/G3gK1dtL7ZUnUj3hFeilS1s+XD7+OMlz3o3o70MtXTqqzaodB+OR/5B+9zuRv/k1RNLj1HNjBQ6dJhQ4hEyVcA+pC5hJNESnklvdAw9EuC6iWNx2UiP/0xoxPIzwPNwDDxr3+JECh5EXtjOJhpgU26Zw4ZeoHf0iRLmM3LABuWE9cv065EA/atfdKPzP11G77bbtMdEoavc98PbYc9JiVGdWIiq0Jvqdb5M89xzs3/0GajVAY6x7mtj/fIXUm9/otxhbEL38MmTfVn91N0ZBKCwbsXw55pNP4F1xJYODuXHtubmIqKgLHGIxPy02jKgIaUU4GDvPzOdw60jkhvX0HPIsv20k5cRto0A6PXDXPeiV46XWIx0cKpVKR6Ih2kJrjHvvwb7hD8gtW9DxBM4RR+C88Chf+t0BOh1RYf/qlyQ+8yl0JILuWTL6L10XuWED3h57kLv0chhrdwRQKJB9ydGIcnn0DBYgtUbk875aslBA7biaoZv/POHvYrYiKkYSOjh0D+FgbMiEzGt7bgLsX/4SbVkIzxud1ipEMKejQQi0ZWH961/Ujh9fkEYKHGKxSEfbcy0RAu+ggykfdPCsPUVHBQ5KEb3ip+Cp8cUIwDRRK1diPP4Y9s1/onbCK8bdRfb3+8VoRJERQiCHhqCvz9+TAoTWGI89Sub4Yyl97BN+rtQIqtVaY/+vUxEV4wkFDiHtEV5+zDHd0J6bCOOptf4X4fLlYFnbipBS/v8Nw3dqiEYx1j454TEsyyQSsQG/OHW0PTfPjBQ4yFauD21gPPgg8rFHUU289QD/3wCwb7xh4r+P2P7FglIIBIaUGENDsHmzv5I1TTAtkAYYBsb69SQ/+H6sv9w67lCFQgnDkMRi019NynXriP7g+8S+/CWiP/qh7ysYMFbgUI+OrwscDMNoCBx8ObovcIhEIsFbMWzdbS+EK6Q5JB63icWsOVXPtYsOvgCJRlErV0K1iqhWQWv/74L9FjE4hDZH76WM956rkEolsKzFNlTaGYGDGB5GOC7atlvf0bIQfX0T/pXaYQXeXnth/fvfkE6D66K3bvX/0gg+1lqDVuhUBrXDCsTmTcS+fBHO4UeMEqZM5ODQ9mvJ5Uh85EPYv/stwnVBSFAe8QvOp/rKEymefwEkEqHAIaQtwhXSHFAfbk0kIvjti/LsDLfOIDDPPeQ5wQ/BsGk0is5k/P2JRIIgKhZMA/eQQxqPm2i4tVqtTShwWAx0QuCgk0l/BTPRoO5IHLepQ3kkGsF84xtBSlShgBoaGm+PpFRgOpv1263ZHoxHH8O8445xx5uWwKFUInXWGUR++Uv/wiUWQ8eivtrR84he/TPSbzzbf98QChxCJicsSLPI2PZcoVBGKUUyGZvX9txE1I55KWrlSkQuN3oPqY5SiGIJd7/9cZ/5rEnVczNxcOhmOuHg4O27L97OOyMHB5vfyXUBTe1FLx51sz/0nCKVSlA76VVUjj8Bkc8jhoYbnnwoBa4DaN8uqa4mjEQQnovx2KPjnk5r/98sErGxrPYaJ9HLL8O661/oiO0LJupXH/ULmkgE67bbiFxzdXBz6OAQ0pqwIM0SsZhJT0+9oFTJ5UoI4be3wP9wKqWxLDMQLUgKhTKlUpV4PEoisc0qSAhIpxMYhiSXy5PL5SfMKZoR0SjFz38RHU8gBgb8YdP6PlKphBgcRPf2Uv7850mlk2QyKbRWDA3lA0PO0YerCxz8L5nF9TYrFEpIKYnHJxjGbQfTpPraUwB/7mscSiE3bkCtXo3zkmMA//2SSMTJZFIIAUNDOQrlKsULPk/pvA/4xrBag+f6bbpIBLVihS8JH7tMbbJsrVZ9V4+2VklKEb3skm3D1E1ep9YQvfSSERc5ItizMkYJHBzHaQgcikW/TT3y51QqGq6StgMW1zdFF9Cu91xdQReJWNRq7py255rhvOBI8j/4Ie6zno2o1RADA/5/SuEc+UKqV19N+vDD2vaeK5crgbXR7M25zAedEDhUX/0aqq9+DaJURK5b5+8rFQqIrVuR69ahVqyk8NnPo9NpIhGbnp70xN5zlkXlLW+l9NH/RqczqFU74u28BrXrbuhsz+jiU6mgLQvvGc9oel7tChxEfz9y3bpte4/NMA1/nqpU8h8XChxCWhDOIXWI6Sa3gt+GqTOV5NZZQ2uMf/8b49FHfM+3Zz6L2MEHTct7zrJMMpnUoosFB+jpSeN5avoCB6WwbrqJyPXXYv7736AUOpmkdvzxVE86Gbnbrm1HQ4jhYTLHHYsYHp7QBgmtkZs34R54ELkrf9bSniiRiBGNRhgcHG4qcBCbN9Nz6HP847QSZwT7R4P/vh+dHDkDo+npyVKr+aMMPT1pBgdz2LZFLBalr2+QdDqBZZmNgD/TNBkYyCNEKHCYLt0+hxQWpA7QieFWrTWu6+G63rSHWztN28mtk5BKJTBNk6Gh6UVUdCu2bZFOJ2debLVGDA35TuvZLCIWm1Zya+Taa4if/xlwav58U331UqkghgbR2SyFi7+N+6xntzyOENDTk8FxXPL54sR3cl16Dn0OcvPmRo7UhMcqFvHW7MLQrX8dVQS11ti2RSbjD8YmEjEMw6C/30+UrdUcCoViY4i2XK40fg4jKqZPtxeksGU3AzoZDVGp1DBNg2jUplqtznp7bjJmHA0xglDgMAlCoHt60CtWEMmkm7fnJqF68qspfuZ81MqVyMEBxJbNyM2bEYUC3j77UPjmtyYtRtCmwME0qZxxll9kmr0vgtsrrzt73IosFDiETES4QpoG023PlUp+NMS2ldP49pyUAq01w8Pzk1TaqWiIscRiUeLxKENDubYiKhYK2+ySKjNycOhocmulgv3n/0M+8QRIibv/AbiHHto6xn0CMpkkQsimDg5icJDMia/AePxxf6bKNLc5e7guwnFw996b/A9+hP3732Pd+mdEuYK3225UX/NanEOeg2Ea9PRkRsi9Y40WnZSCvj5/xeR5XsN2yPPc0MFhmnT7CiksSFNktr3nTNOXU8+J5c4IOtWea8WM91y6FN8qKcrgYG7KERVCiGm15+YCw5Bks2lKpXLTiAq5fh3Jd7wd65670VojAosphMB5znOpvvJEEp89H1EqNuyn6v/VXngU+W9eTHzFDkSj/r5Rb28Gx/EoFIr09maD34dLT0+GwUE/NmPJEv9nxyEsSlMkLEiLhGbec/F4dJz3XCwWGec9N3LlJIRo3Gci77lkMo5tW40P4GwzMhqiWCzPWiEMBQ6j8aPE/XZfqVSZxbC86dOOwAGtMf/xd+zf/RY5OIha0kvthBMQw8Ok3vh6hOP4eVr1FZrW4DgI16V25AvJX3oZPb1LQoHDHBAWpAXObLbnmqnnpBRksxmqVb91M1vMVnuuFaHAocPtuVmmLYHDRGhN5oTjMO+52xc9TLSScRyE4zL8k58iXvSiUOAwB3R7QQpFDS2YaLhVSkE6Pfq2bY7cnRluVUpTKpWJRv2N3U7ju4onJh1unQ22Z4HDhMOtgW1OtzIdBwcA4777MO+7z3cjb1YwTBPQRK+8MhQ4hABhQZqQTqrnpjvcWqn4+07JZGeHSjupnpsO26uDQ8vh1i5nSg4OAcaTT/iuEc1cHKAhgDAee4TQwSEEwoI0im6LhigUSqMiHWbCrCa3TpHtycFhlPdczZkwuXUhMOWICtvepribBB2JhA4OIUBYkBrMV3uuFa7rBsUvNu0++Xy251pRKJQ6HwveBdS/SJPJ+IJrz7ViZERF3Y+xFc6zno2ORoNo9iYEqjvn+S8AaERU+M7ffkRFNGpjGEYjosJxvEZEheM4jYgKWDyjBNsz231B6ob2XCtKpTIgRrWBxNAQ9q9/ReSKn2LdcAOUJxY+zHd7rhWO4y7aiIpazcG2rQXZnmvFVCIq9LJl1E54BUKpiQdntfYTby2L6qmnj7w5jKjYjtluA/rGqufy+dI49Vw+XwpaS9vUc4VCJWjDJJqq53K5keq5mX3b1gUOiUSMSv8gkS9+gcg1P0PkA9WjEOilSym/6c1U3vI2MIy5Uc+VShiPPw6ei9phBXrFiikfolgs09OTJhaLBYV3YTNSPef/++sF2Z5rRl3gkEolqFSqDS/GZhQ/8t+Y//g7xtq1aCn9iAoAz/PDHw2D0ic+hdppp8Zjxjo41Idh6wKHbDZFuVyhUCiRTMYpl6uN/aZKJYfWi+zqZjtju5R9z/Zwq09nPxjZqI085RS45c9+nHgs5oexuS6iVAKtqZ1+BuJrXyMSjczacKso5IlcdRX2b3+D3LqlEczmHP58qqedhrf3PlM63mJwcJhouFUp3REHh25kMgeHkYjNm0l85lPYv/+dX4QApMTbZRdK532A2oknjXuM1hrDkG05OIBGSgMh/JXp8HAllIG3oNtl39tVQZrL4dZOE//RD4hf8FlUNDqh5b+oVBCeh77iCopHvGBWBAsinyPx0Y9g3nknxGKonh6Q0o9NGBxE77ADxU9/pi2/tJEsZAeHVsOtM3Fw6GbacXAYi9y4AfP22xG1Gt7qnXAPO6yllZHWvuilmYNDuezHyRuGQaVSpVyuNgZqQweH5nR7Qdou9pC6TT03ZTwP+7LLfGuWyGiVkxACKSUiiI12vvf9WVPPRX/4Q8w770StWoVauRKiUbBt9JIlqF13RfT3Ef/C56A4hQFKFqbAoR313EiBw2JiqgIHAG2Y6GQKnU6jl/Q0n00KaCZwqLdD43F/z7darQUXmLVQ4LAIWPR7SM3ac4lEYoL2XAyldNDq2ua8MLI9l04ngmKVn7NoCLluHXLd06hIBIlfhLTWSCF9ZS2glQbDwPzbX7d5hnUQMTiIfeMN6FTSL0TjTlKiVu2IfHod9q23UDv25W0fe6TAwXG628FhbHtuaCjXUrBQLJZJp5ON/cXFQqlUDlaH8ZYODmLzZuJfuhD7d78NWsuAbeEe/ExK7z3PXyk1oS5wyGRSVCpVXNcjnU6ilAr+0w0Hh/p5LF3aQzxuhw4OC5RFu0LqdvXclHAd/4McFKL6qggBSmuUUmj8IiQ8r63Zj6li/vtexEA/eklv8ztZFmiF+Y+/T/n4C8HBYTrDrR2LqOgy2nFwkJs2kjntFCLXXOP72aXT6GwGbZqYd95B+s1vwLrxxqbPURc4uK5LNptuDFL7Ixihg8NiZNEVpAXfnpsAtcMKdDyGcJxRV31KqdFSV8fB23XXKccMtEWlilB68mNLA6ahmOtmB4eZDre2cnBYyEzm4BD/7PnIJ55AZ9LoZNIX4UgJ0Si6pwfKFZIf/iAiN7E4wjAkmUwaM3B7KBZLoYPDIqe7PvkzpBuHWzuBTKfQr3kt0vP8mGut/bXZyJaE60twK6edPvFBZohethQdsaHSQjGmNXgueocdpvUc3ebg0CnvuYkcHBYLzRwc5Lp12H/8Izoa8QvRWIRAp1N+K/iXvxj7V8TjscaqaGgoR7lcIR6Phw4Oi5xF8elYVO25MdSHW+U570b19qKHhtCO02jdAVCr+amge+1N7aRXzcp5uAcciLfb7r7UuwmiUIBYjNrRL5r283SLwKHT3nPbm8DBvPMOqJQn3m+sYxigNdZtf2vcZNsW2WyGWCxCqVRhcNDfowsdHLYPFnRBMgxIpxdXe67OOO+5nl6Gf3QJavVqRLGIHhyEwUHk8DCiWsU94EDyP74EnZwlWadpUjn9DDAtxObN4/epikXE1q3UDj8C74ADpv008+3gMJvec8ViuSuKbaeZyMFh5MzRZIhKBcOQpNNJ0ukkrusyOOiviuqEDg7bBwtWZReLmSQS0YYpY6XiBJb10QWjnpuIscmtQ0PFxnCrd8ABDN10M/YNf8C+4Q+Y+TzGTqspvuzlVA8/YuLWSAdxjnkp5aEhoj/4HsYTT/gtPGn4bTzLxDnqKMof+eiMFX7z4eAwVfXcdBgpcFhMiruJHBzU6tX++7FW841Wmz1QCOQee5DNplFKMTycn9ABYjoODvWfQweHhcOCG4wdO9zqui6WZWGaRlCc/BaC1nTdcOtktJXcWq1i/utfiEIB3ZMlefRRaCkZHp67fx/5xBPYN/wB6x9/94UUu+xC7aXH4j73ua3jBqbAXDo4zGVyq5Ry+3Bw8Dyyx7wI+eSTvoBhAkS5glAe3HADpb2fMWpFNBFTcXDwPK9RtDzPZXi4GsrA6f7B2AVTkNpJbnUcF9M0MU1/peB5inK5ihBMK7l1rmjLe87ziP7oh0QvuxS5eZNvWGmYqDU7Y55zDvmTX0N1EV11w+w7OMxXcuv24uBgX38dyQ990LeXSqW2rZy1RlZriEoZ779eyfD/fB3V5u99MgeHQqGE67r09GQYHMyhtWbJkkzo4BAQFqQOMFXvOa01juNhmrKRuOp5qhErPhfec+0wtj3X1HvO80h86P8R+cX1IKQfCW2a4Dq+bY+U8M53MnDueSymbrllmWQyqbZiwafCRN5zc+3GvZDtklqRSMSIRiMMDg6jlCZ6yY+Jf/ELvsAB/Peq9oe4nRe/hNyXvgKJxBSfRdPTkw3cGWoNyyDbtojF/EKVTiewLLOxejJNk4GBPELMblu72wkL0gzojPecxvM8TNNAShlsiI68Gu7i9lyAfd21JD/8IT9fZqLB0WIBqRS1H19C/rmHzuJZzz2pVALTNBka6oyDw1y251ph2xbpdLLjxXa+EQJ6ejI4jttwcJBPP038+muJ3HE71Gq4e+xJ8cSTcJ/7vGntN2qtsW2LTCbFwMAwiUQMwzDo7/fbdbWa03BwKBbLlMuVxs/bu4NDtxekrhQ1zEY0hJQSx3ERAkzTnFf13JSiIbQm+tOfgPImLkYAiSQM9GNfeQXGYUe07fAthocRxYI/QT9b6rwZ0imBw3y155qxvQgclFIk9nsG9jM/TbVao1gsz7hNGQocFi9dt0Ka7WgI0zTIZjMUCqU5vTpuuz03BrFlC9mjX+hX6XjzGRaRzyMiNu4jjzJcKLU8pnnH7USuvx7zzjsQnou2bJyjjqJ64qvw9t9/yq9ttpmJwKEb2nPNWOwCB8PwhUZKKQqF0qT5SVMhFDhMj25fIXXNHNJcDbe6bn2QLzpnb8qZJLeKWg2h1aTzHFpKtOdhaUUk0kRmC0R++hOSH3w/1k03gMDPVfJcIj+/nuS57/YTaLuM6To4dHq4tdMsVgcH27aCjCKB47iBoKBzxQhojHuUyxWSyUTo4LBImPeW3Xwkt9YHFBOJGIVJVhMTojXmP/9B5KorMTZsQCWT1I59ObXjTxg1c9GJ5FbV24uOx/1h2BZT76JWxVu9mhqi0QYa+6Ez77id2He+5R93l122/UUyibdkCXLDBuIXfp787ruhdt9jaic6yxQKJTKZVFuu2d3WnmtFqeTn+iST8QUvcPDnAOPYtkW1WkNrTSRiI6VAqc7//usRFZFIpOHgMHLFmUjE6esbbDg41H9OpRIMDOSB7Vvg0I3M62XZfHnPae3HgkejkYZEvF3E4CCZE19B9uUvI3r5ZVg3/IHIL35O+q1vZsnBB2L+659IKUilEmQyKbRWDA3lgy/Faf2SqP7XK8FxoFnvPfC4q772FErlCiAmNPKMXH8dlMuo5csneGECtWoVcnCIyK9/PY0TnV3acXDolPfcXLPQHRzGes8ND+fJ54sUi6VxDg6dJnRwWFzMS0HqBu+5SsW3tZ+St1i1Svo1r8K84w4AhOv6zxK030R/H5lXnUjPpg3Tas81Pdezzkb3LkUMDvjFZySuixgaRO20E9WTX4NS24qtMcK5QQwNYd55Jzqdbv5EQqDjMeybbpiVCIuZ0iqiotvbc61YcBEVpRLysUeRjz2Grbxx3nP19lw7ERUzZazAwW/XyYbAIYyoWFjMS8suGrUaBcV1vVEOCp1uz7WiUCiRzaaJRiNtCRwiP78e6557mv69UApdreJ98pMMf+d7HftOV7vuSv5b3yZ57jnITZsB7Vv2KA+EwNtlFwrf+g562TIAKpUq0WiEZDLecHAQxQLCddHx1l962rKgXPFXZM0sX+aJekRFPB6lWvWFLAupPdeKQqFET0+aeDzatQIH0ddH5OqriPzmN8ihQYSUyOXLcU48kdwrTsTLZMY9plqtEY36QX5DQxPHTHTgzILfX4ZIxG5EVPT1DTYiKvr6hojFog1VXiwWJZWKbtcCh25k3lV2UkI2m6BSqVGrTV89F7ycKT9/Mun3vOtT3a3IvOwYzLvvQkwiW9WGwcB9D6KXLp3y+bRC5HLYv/4V9h9+jxwYwFu+nNoJr6D2smPHKfBM0ySbTZHPF6lWa4h8jvRJJ/rxEL3NQ/bkpk2oZcvJXffzjqfOdoqeHt/3zHVVV6rnpks3OzjIDetJfOD9mA8/hIgnED1ZUAo1NATlMu4BB1K46MuNi6KRjHVwmA1CB4f26HaV3byLGpSCgYHiqD+Xy/5wq296Gm+snIQQpFLxjnrPTUXgYD780KTFCPwWnvH4Y7gdLkg6naZ6+hlUTz9j0vu6rhu4nAcCh1Qa58gXEvnFz/GWLJm42CiFqFSoHXd81xYjgGrVIR6PYhiKYrE8b8OtnaZrBQ5aE7/gs1gPP4RYswvYFspTeErBDiugVsP8973Ev3QhxS9eNO7hIyMqqtVaKHAIaUrXaU1LpRrFYg3Pg2rVo1SqMTTkFyM/PqKz0RBTETjoqUhzO2QyOhP8QdJtAofqSa9CZTLIDRvG7xEphXx6HWr5DtSOO27uT7YN6tEQ8Xi0sS9XrS6OYlSnGwUO9oP3Y//7XuSKFWjTwHVcvxg17mCjepZg3XYb8vHHJzzGRBEVnSYUOCx8uq4gjcT3n6uhlF+c6qF0nTZCbVfg4B5+OLqNQqMTCdy99+nU6U2bsQIH74ADKH/ow+hEAmPtWuSWLYjBQeSmTRhrn0IvW0bxU59G7bh6vk99FBOp54aHC4Fb+wIRArRJNwkc6uq51L33ICoV3GgM11MT+iXqdBpRKmLdcfuExwoFDiHt0NUFaSRKaSqVGqZpEo1GJn/AFCkUSi2PHYtFsN77XoTbesBPGwaVM86chmHk7FCp+Ptw9WJbe9mxFL79XSpnvQ6dzoCQqGXLKb/1beS/9wPfX6yLaKaeqwsc/C+ZBfM2botCoYSUckLp/lwxMrnVGc7had06e1UItBCIFhH31aovRkok4qA1xkMPYv7tbxj33jtePTptRBCrbowSODiO0xA4FIt+LtrIn1OpaLhK6gLmv680BYpFf1+p3ovu5PvHdb2Gg0N9qA/GDLce+ULka16Lfc3ViAmeXBsGavVOlN7/wc6dWAfw1YQpIhGbarWGt+delM/di/J73rstQK3L9ozaUc+Vy5WGgqur9lxmyEgHh0qlNqcCh7HDrcViGSuZIq60v8HbrG3teQgNqkn2UZ1Cvkj2//5E9uKLEQ884Cs/DQO1085UzjyL6mmnt5Uy24yxDg59fYPEYpGGg0Nvb7bh4FBX5eVyBZYsyWBZVRxHbzcCh25kQV1a+sv+aqMH3GmKRd+801/CTzDcWqqQ/8bFlN/zXnQ0iga0afq2PUJQO/pFDP329+glSzp+bjNhpMBh1IdNCIhEuqoYTXW4tVAodd2eSyeoW+FMaU5uBjQbblVK4Rx1FDqVQgwNNX/84CAqm8V5wZEtn8f69rfR73oXxr33oiMRVDaLjsUwnnyCxGc/Q+ITH2s+AN72a5GNPau6wCEatTEMoyFwcByvIXBwHKchcKD1OjBklpl32fd0yGZjWJY5KzHT0ahNMplAa90yGkLkc9i/+hVy40Z0MkntpS9D7bprR8+lk0gpyGYzjeiNbmS60RCdjqjoFuYqosJXmfrq1VKpMmFya/zznyNyzc9QS5ehk8lRfyfyOeTAAJWz30D5nPc0fR7zn/8g9Yaz/YC+3l601qNWf6JYRFQqFD73BWonnjSj1xRGVExMKPueBQqFCtlsgmQyztBQ5wqlZZlEo9FGMRocbP4Fp1PptuTX3UJd4JBIxKhUajN2j+gkMx1u7VRExfSoIuUGhKiidQylVgGdWa3NdkTFRO25Zu3B0rnvReRyWH/6I7K/DxVPINBQLIFtU33liZTf9vaWzxe5+mpEpYpavgylFEbQWaj/W+tEApHLE/v61wDw9twLb999p7WCDyMqFiYLcoUEkEzaxGIRCoUilUrzcLt2GBsNUS77b+K5jqiYC7LZNFrrhoPDfNLJaIiZRFRMDw/T/Cem+U+k3Ay4gI3nrcTzDsF1D6ITHfHZiKgQAmKxGLFYZGrREK6LddvfsH/1K4wHHwAhcA88kNrLj8c97LDWez9akz30uYhSEZ3NAmBI6TuNeB7UasitWxHFoh95nkhANIp7wIGUPvDBaYltwoiK8XT7CmnBFiQhYMkSX8nWaiUzGc2SW6fi4LCQGOvgMF/MRnLr3MWCe1jWTVjWrWidRKnlgI2/WtqMEFUc52gc54V0Yjyhkw4O7bTnZgXXpee5zwal0Cn/S1EgkIZEl8uIdetghIJVrVoFrucXsGSSwsXfxnn+C6b8tKGDw2i6vSAtKFHDSGYqcLAsk2w2TTweo1KpMjg4POoLeqTAYTHRVOAwR9SHW1OpBLWaw+BgrmOr0LkSOBjGf7Cs21FqFUqtxi9GABGU2hmlerGsvyDlkx15vk4IHAxDkk4nSaeTuK6fUTRnxQjANH2X+RGDzBqNVgqxZYtfjIIVE1L6g+WxGHpJL6JYJPHhD0ELSXkzQoHDwmLBFiSASsXFcdwpxUi0Gw0xk4iKbmesg8NcMBfREO1EVHQCw7gP0Gg9sXO61kuAKobxYMeec7oODq3Uc3NN9VWv9q23RuxfqnIZyuVtxUhrdDwBQjZegE5nkJs2Yt84vfDI0MFh4bCgCxL4AgetdVtXj1NNbp1WRMUCoFlExWwxl9EQrSIq2kMhxNZAqDA4wd+XMIynUKq1tF+pLIbxKJ26yp6Og8PI4dax0RDzQfWkV6FWr0b29TXac6JcBq39xqbngZTo9Ji2UuCOYv7j79N63tDBYeGw4AuS6450cJg4LmGy9lwrJnNwWKiMdXCYDWazPdeM6Ts4KAzjASKRa4hELicS+QmRyGVY1q+Q8qlR9wPN5B8dGdy3cyuRdh0c5r091wS9bBn5i7+Nt/Ma5MCgb11VFzEoBYaBWrYMrIk/x8KZidIwdHBYCCz4ggS+g4NSing8NqpV04nk1pEODottg7NQKGFZJpFIZ3OP5ju5tVyuBJla7RZbhWn+Bdv+FUKsQ6lePG8ntE5hmvdh29djGA8E942hVAYhWot8pMyj9TI6OVkx0sFBTqBo66b2XDO8fZ5B7vqfU/zM+TjPfR5q+Q5gGOhMBlavRsQn+DcLzt/beedpP+9YB4f6vlzdwcEwjIaDQyRiNxwcIpEIlkVYlOaIRVGQxgoctm7dwl13/ZNsNt2R5NZQ4NA+3ZLc2lrgoJByHaZ5F6b5Vyzrl1jWLSi1DKXWAAnAQus0nrcXILCs/0OIfsDA8w5CiALQ7Iq9Cji47oEdf13NBA7d1p5rhU6lqZ5yKvnLfsLQ/92Ct9vu/ipJSv99OOatKIpFdDRG7b9eOaPnDQUO3c+CHIydiErFxTBK/OQnP+Hiiy+mXC5z001/JB5PzHh6vy5wSCYTVCrVrguCE1u2YP/pj8gtW9C2hfvMZ+E+57lteYKVSmVs2yYej87IwaHbkltHChwcZ9tYgBBbsKxbMM27g32iYaTcAmgc5zA870C0Hv1lr9QqTPMhDOMxXLcX190Xw3gQw3gEz1sDRAAP/+NUwjDW4nkH4Hl7zsprKxbLpNNJbNvC87y2h1u7kliM8rvPIfGpT8DgEGQziPqwrNZ+S89xqJ71OtSqHWf8dHWBQyaTolyuNgQO/f1DRKORhsDBH6KNN36Ox+1F7eDQLSyagvSPf9zJV796IWvXPklvby8f+tBHiMVmXozqVCq1Rix4u+4Qct06IlddgXXnnaA17r77Uj31NLx9ntGZk3IcYt/9tj8BnxsGfJUSloW7996UPv4JvEliMGbq4DB2uHU27Jymy1gHByH6iER+hWH8CyFygIlSSxFiECEq2PZfcN3NOM6L0HrkxrpAqRSG8Qiuux+G8RRa9wJ3Y9u/R4gKWluARKlVuO7zqNWOB2ZHxVgXOCSTcYQQKKUYHs537YpoMqqnnoYolYj9z1cQ/f2N3Tc0ELGpnHUWpf/3oY48V+jg0N0s2MHYOq7r8tnPfpKbbvoDUkpOOeVUzjvvfUhpzNjBYSymaZDNpid3cNCa6He+RfyiC/2N2PqvWEq0lFRPO4PiZ84HawbzMloTu+hColf+FB2NoXuyIAPFXLmM7NuKWrWK/De/jdptt0kPNx0Hh9kYbu00Ix0cpPwttn0DQgygdQqtE0AF03wIrSMIUUWIAq77bBzncEZer0m5CSH8fSEh+pByE1KuDQqbhVLL0LoHraN43n7Uai8P9pA6z8jhVsdxF43TuVy/jsh11xG7715Qmsruu1N91cl4e+3d0efZnh0cun0wdsGvkPr6tvJ///dHDjzwYN73vv/HXnvtRTLpOzjMVUTFWCKXXULi85/z3cBjsW1eXFpDrUb08kvRlknpM5+d9rkYDzxA5Ppr0Ylkw4qlQSyG2nFH5Lp1RP/3h5TOv2DS442NqGhFt7XnWrEtoqJMrfYIUAEEQtSQsh9/H6iAEG4wV1QNWm57odSKxnGkfBohhvC8BFonESIfODSsDgQOCs/bFaVWYRiPYds3Ua2eRCdXSWO955RSRKMRpJQLq03XBLXjasrnvIeaIf0LpFIZr9z5i5wwoqJ7WfCihhUrVvKb39zExRd/nz333GtOIyompFIh/pUv+8VnZDGCRtyDlpLoZZcin3562udh//bXiFLZVydNhDTQqTT2/92M3LRx0uO1I3CYb/XcdPEFDgUsaytSDiLlVqR8AiH6EcIvRlJuDAqUBGpI2TfiCGUM42mU6kWpHZHyKYQYwDA2YxiPBI9dh2X9FSGG8bxdkfIpDGNtR86/mXquvl+02ObkPE8FF35+DMxsEAocupMFX5AAEonkqC/R6Tg4tMtkDg72DX9A9vWhIy3mliIRhOsSuebqaZ+H+cD9fsuvxZWaTqUQxRLy8SfaOmYrB4duUc9NB1/g4GBZZaTcADhonUbrDFqnUGoFWieCVlywF0d9lVjFNO9FaxvP2wchhjHN+xFiAPAdvrVOAjGkfBrLuhUpC4BAysdmfO6Tqeem6+DQ1ShF7eab0d/6FqlLf4x16y0dTJTdRujg0H0s+JZdM2YrogJaCxyMJ59EGwa0ckAQAjQYT7ZXKCY+RpvXEoK27fsnEjgspPZcK0qlONmsg5RVXHcJo6/FbJRagZSbkXIAIaIIUcAwHsEXNOwYFJ0khnE3Qgyg1DJ8dZ2Pr8xLIWUOw3gIz9sRIYrTPt92oyFmO6JirjHvvIPEZ8/HeOQ/4HlYUpICvF12pfShD+Mc/aKOPddUBQ6VSpVyuUIiEaNazaFU2LbrNItihTQR7Tg4zIRmDg7aMhFKM+nmlQA9A1GDu/8B4Dotn0fkcuhkCm/33ds+7kgHh4XYnmuG50kcx0LKGFKWJrhHFK2zKJUN5o/2wXWfTbX6ahznaPzi4yDE5uDnib+MlOpBiH6k3BKIJqbGdIZb23Vw6HbMO24n9ba3Yjz4ACoWQy3pRff2IlIpjMceJXnuOVg3TM/PrjmjHRz8dl0Mw5B4nkc2m2p8xn35t9/STqXiC/az0M0s2oIEzR0cOkEzBwf30MNAilFW+uPwPNDBfadJ7fjj0fE4YmgivzX/OUQhT+1FL0IvXz61Y9dqgYPDwmvPjUSIHEJsCQrEZhwnGuwBFRFieMQ9vUCYoNG6B9c9kErlrTjOMSi1O0qtROsEhrEBIZxAKj52ZssFZFCEPIQYRqnJ1Y0jme5w62QODgsCzyPxqU8iCnlU71KIREEIlNYQicDSZYhKhcRnPz0t1+9mCCEwDNlo0dX3UJPJOFrTmIkqB+KKXK7A4GAO27ZCB4dZYIG+e9tjPgQO7sHPxN3/gNFy7zEnJapV1NJeqsefMO3n9vba25/fKJe32fcHx6dQQK5fj9plFypvekvbx6x7z8XjscY80nxmJk0XITZhWX/Etq8kGv0B8fhnicU+j2XdhetWMAwDw9iCYTwWCBL60NpB6xSetyYI19vWctV6GZ63O0JsAlTQrhNAvShphCgGUvI4Qgyj9RI8b5e2zrcT3nOdiKiYT6y//RXjicdRqfSoFrM/H6v9LrdpIdeuJX7B+f57foZIKQNrsTSu6zX2kYaH8wghyOeLlEoVTNNsjHr4rdFaKHCYJRZ1QYJ5EDgIQeHLX0Fls76Tca1W/1SB4yBKJbRtU/jyV30V3nQRgvK730P5XeegUynkpk3I9euQ69YhyiWc5x1K/mvfRK1e3cahxqvn/HmkuY2o6ARSPolt/xLT/BdCFAM3hq3B/tDjGMY9eF4/fjHxVzVKLcPzno3jHIVSu+J5+wZHqyLlU0j5KEqtDvaFhvGL0iqEcIMV2GbA30cyjM1oncRxjmQyyXenvecWssDB/Pe9vmfdWDGQ1jAwCE8+iezv8wdof/RDsi96IYkP/z/E8PDEB5yEWCxKT08a0zTJ5QqN330kYuN5KhQ4zBOLVtQwkrkWOHj7PIPcNdcR/9QnsW77m29/AmCauAccSPFjn8A94oiZP7mUVN74Jqqvfg3W/92M3LQJIhGcZx+Ct99+bYkZRg63FovlUcOtM3FwmA+EyGNZNyNEGc9bg2XdSV0t59sDKYRwUSqDadooVUApgVIZlNoZX8CwAqV2xjDuxTAeQMqtjeMrFUepHYMClESpDFLa+Ko9//9KZfG8HXHdg1ue62wkty5ogYNS49+vWiP7+wMXEhBS+rN9kQjCcYj87CqMhx8mf9nl6GR7A5+WZZJMxpFSUi5XA2Vp6ODQLcypU8O6dU9zxRWXcf/99/HEE4+x885ruOyyn036OK01l19+CddffzVDQ0PsuedenHPOeey//wFtP3cyaROLRSgUinPq4GA8+gjmXXeBUnh774170MFtq95mk3bVc9NxcJgvDOPf2Pbv8Lw9kHIThvEvQGCaD+IPxCaBfoRwgR4MAzyvhuctA7I4zvNwnJcjRB+m+Y+gBbcU/7rNDWThTwVOD8vQOoqU6zCMDcAgUpaBKkqtplo9Bcd5OVpnx5xje+q56SKlpKcnTblcoVSa/8iJdrF/91uS556DSme2OZiUyxgbN/g/1/fGlEJne9DJhN9xKBQon3Mu5fed1/L4dTeGSMSmVnMoFkt43ujf+/bg4NDtTg1z2rJ74onHuO22v7J69Wp22WXXth93+eWX8KMffZdTTjmdCy/8Kr29SznvvHezfv26to8xHwIHAG+PPam+5rVUTzkV9+Bnznsxmupw62xFVMwGhvE4WkcBGcwJEQy41vDbZxLIAjG0liilg7ZbEa0tXHc/tI5imveg9Qq0XsG2JoKJ1itQ6hlBIqyHZf0Dy7oHKTcGK680nrc3WmeIRK4lEvlB4BA+d9EQC1XgUDv6RagVK5C5bS04mc8RKAv8G5TyC1N9P9iyQEoiP7uqpdBhbHsulyuMK0YQRlR0A3P6jj3iiCO57rrf8NnPXshee7U2/axTrVa5/PL/5dRTz+SUU87gkEOey6c//TnS6TRXXHF528897w4OXcB0hltnI6Kic5SR8onALWEdfuxDfZ/QQYhaoJ6TQH1fRaK1jVLL8bxVGMZKYGWwJ6SD2SM1xlx1G/VVk9b+cbSO43mrAjXevsH/d0GplVjWXVjWjdi2nNNoiAUpcIhGKZ33AbRlIQb6fZVouewXIyEamUik04gRM346FkP29WE8/vi4Q1qWSU9Pmng8SrnsB3NO1soMHRzmlzndQ5rOFdt9991LsVjkRS96SeM2y7J44QuP5s9/vnlKx6pUXKJRX+DQ6RiJbo6omOlwa6ciKjpHLcgy+newAtGAFayKPJRaBcSDglR/nfViuu3fRWuB58UwjAye5+JbBg2Oa7P5XzSV4HkiaG1hWY8HxWhp4IE3Gv8YReLxhzHNYarV2JxGQ4yMqFgo+0m1E09CuC7xC7+AHBwA1wN0Y39Jp9PoZBIJyLokXAj/PiPSZMe255qtiJoRRlTMH12/pl+79kkAdt55l1G3r1mzK5s3b6JanVqfvFCooLWelavHSqWG67pdc2XaKe+5uoNDNBrBaOVAMSc4WNYfsaw/AjU8bxc8b088bwUgMc2HkfI/gfu2HWw2S/yCUkLKPoQoI+UWDONpPA+kNDFNI9gvgpHFS8oNmOY/sazbsKy/YZq3YRiPBZEVbuDgMBohwLJsbDuBEHkKhUfnPLl1pMBhIVF99WsYuulmip8+H7XTajBNdCaLWrESnfJXrb4MXCAQiGoVHYmgdtoJaL8914qxAge/XScbAodYzP8cFAql4PcrGj9LGbbtZkLXF6R8Podt20TGyEFTqRRaa/L5qW22z5eDw1zTae+5kQ4O84lhPIRp3oPn7YxSO7BtkR/FdQ/E83bEsu4AXDxvN/ziohBiIBAjyKCIuGit0HoI130a01wN7IXW9XhyDykfDqyCBhuedUK4GMZ/EGKQumx89PlJbNsfA3Bdl1rNwXXnJ5ZjoTo46HSa6ulnUPzYJ/xolWgEjG2/Z601GhDByqj28uMwd1g+5fZca0Y7OBSLvuLUcfxCn04nKBbLeJ4a9XMqFQ33kmZA1xek2WC+BA5zQX24NZVKUKs5DA7mOpZTNP8CBw/TvB+tI8BEV/4S1z0MrdOY5gNADKVWIsRQ4CunAtGDR92/TgiN1kWU2oF4fCc8b2/81t1jGMYTQcbRUnxRRASt48EeUgIhNuLvW/mrItu2iERMtB6kWn0Kz9uKb8A6P6vKhSpwqFN7+XG4z3omolCAanXUoLmq1WBoCHp7Mc57H5lMCs9TDA3lGlLumRAKHOaHrp9DSqXS1Go1qtXqqFVSPp8PPKWmLmOsCxxSqRiJRJxCYSJvs+lTH1BMJGIdP3Yz5iK5daTAoVZz5vxDV7cCUqq36X20juK6zwQknvcMtE4H7bUCQhTQWiCEBDRS5lBqCUpl0Pr3RCJPolQJrddjGA+jdRKlVLAflADKCDGI5+0KrMa2/4jWGzGMPTBNA6WGqdXWAnmkHESIMlpvIBr9Hq77b1z3BYF7w9wNrpZKFSIRm2QyvvCC/KJR8t/9Aalz3oV5553+EGx9r0gI2GEHxKWXYuy7H7lcoeN7ZXWBQyQSaQgcRkrqE4k4fX2DDYFD/edUKsHAQJ6Rbh8h7dH1BWnNml0AeOqptey5516N29eufZIddlhBJDK9dsRiEji0Gm7tNPMrcFAQNGtaY6B1D47zkqCYVNG6Fyk3BCmvZbSOAXYwR/QUQpRw3cexrAJa5xGiilIJpOxH6wRaLw+GZtcEqygXrR/CsjYjZRbHMdH6cYSoADmEKCGEhVI7IGU/tn09pnk3tdpLcJxjmHiFNzssRIFDHd3bS/H/fZjUG8/G2FQChO8VaZqQz8N734v3ox9R23Hn2Xn+UOAwp3T9On7//Q8kkUhw8803NW5zXZdbbrmZQw+dmdvBQhc4zGZ7rhnzKXDwjUsTSNl639CPFa9iWb/Csn6Dad6LlP0otTOOczy12sm47nOQcj1CDKH1SvxC14+UVYRQQC0wUC3jCyIcPG83PO9ApIwRiy3Fto9EqUOo1YYQ4m9B6N9aDGMTvutDD77RagkphzGM+4lGryAS+QlzKRFeqAIHAPnUU6Te+mbk0BBqh+Ww02rE6tWwwwpUKgWPPop95pnYgwOz8vyhwGFumdOCVKlUuPnmm7j55pvYtGkjxWKx8efBQd+1+txz38Epp5zYeEwkEuHMM9/AlVdezs9+dgX//Off+dSnPsrw8DCnnXbmjM5noQoc5ju5df4EDlFcd3+kHGSkfHskQvRjGI8i5ZNBYquvsDOMBzDNO4N5pRqm+Tek3IzWvUAhsAiqopSNlFl82bgADITwkLIfKTcFq1Hf8qdc1hQKJ1OpnBCsopKAhVK9CGEi5aZASJFCqZWAhdYa274By7oBvwjODQtV4BC97FJk31bo6UEGgZRKaZRWYJqodBr91FMkrrtmFs8iFDjMFXPashscHODjH//wqNvqf/76179DT88heJ43zjftzDPPBjRXXnk5Q0OD7LHHXnzlK99gxx0nNw6djGKxRiRiEY/HqFZrExp0T5eRAgf/2DM/+Fy251pRKJTIZlNEIvacOoJ73jPwvEcxjMfwvJ3xxQYeIBAih23/BaVieN6BQAQhlgRuDVGghGX9H1rbGMaD+Eq6jYBfjKAHrS2UkviZR8VA2p1BiByRyKNI+TyqVQPHeQopc8AwpvkQWi9FqR0wjEfxW4pbgAxCVIHBQAgh0DoD5LGsP+N5B6DUjnPyexspcKhUanMqQZ82lQqR669FWBYYhq+uG/sZMgy0EMjLLyf2rndT7rAtGIwXOPT1DRKLRRoCh97ebEPg0NOTaQgclizJYFlVHEeHrbs2mVMvu24lGjVJpWJUKtWOixCEEPT0pKnVnBkduxuTW5NJ35NtcDA3p+ciRB+2fQOGcTdSbg5aaypo6WlqtZcB24ZVDeNBDOM/QBHD2Bgo5AYBG1BI+QTgBVZBNr7/nYvn9aO1RIheDCOP5wlc99koZSFEPnB3kEi5Hq1NtF6CYTwSFKEyEKc+VKvUCqCE1ksRwo+6qFTOwHVfPGe/N4CenjSep7pe4CClJDHYT+SwQ9GGiWql7CyVkELAY48xWHNRarbei5qenmwQP1GjpyfdyEaKxaL09Q2STiewLLPhf2eaJgMDeYToDoFD6GW3AJjziIopMN/tuVb48tq5j6jQOoZScfzoiEyQYbQXIJCygGGsY+QejeftgdbJIBoighAFfH+7YaAeM66CIlUBFJ5n4Ie31YBcsGrPIeX9GMYjaJ3FdZ+N7/BtBoVxiG2uDvUvUIlf9DZiGFsxjPVBO/ERotHvYlm/BOauOCyEiIrGcGsigQKUai0KEsFckrasWbEFq1MXOMRifisujKjoPF2vspsr5jqioh0iEZuEU4Vrr6a29ikcw0Q/93lwwAHzbtIK2wQOcxtRobGsW7Cs+3Fdvy1Xx2+LiECyHR2R2CrQOoLr7hGYqa5DiApSVtA6jv8x8PD973L4KxsRGKb2BL52GxFC4nn7oVQvUlaCeIo+/GJWwjQfQqkU4LBN2q0CxV05kKv7HntaCwxjLbHY97Cs26nVTgoK3OxeI3ZzRMW4aAjDIr3PMzDvvhvdKjusVqN21NE4NYdUKkmlUp0Vr8AwomL2CQtSQF3gEItFiEbtjkdU+Hsu6YbMvBWmaZCIRbEu/ibqe9+DgQEsIbCURtsW7kEH+9Yqu7bvmD5bVCrVRrGdi4gKIbZgGP/B83ZkZDHysfFduS0M40mU2gnf466IECW03gGtFVIqfIGBhz/s6jsw1CXlhlHG88ooZSFlBtc1kVKj1BqU8ldiWj8VhABW0DqDUj0YxlNIWcWXmUv8uItyMP8Ux0+WHUKpJfiO40tRKoNhPIZtXw84uO7hs/47LBRKDVeDboioaOo9JwTVM87EvPce3807OsFKvFQCKamedjpO1SEadUgk4gwN5WbpbEXw+8uMEjj09Q02BA59fUPEYtFG0YrFoqRS0QUTUTGfhC27Ecy3g0OjPZdOYnz2fNQXv4jO5VG9S1HLlqOWL0dHolh33ulLYde1H78xm8ylg4NhPI2UxQnduJVaSn0vyR+irUuBdfCfDNpqDkrtiFJr8FczBmAEhaqA59VbehKlBpByA1rbuO4z8FdOg0i5JXB56An2B5bieauDczCRcitCbA1k5QZgB0VxSTAbJdA6i9Zp/HA/D9O8k3r67GzSTQ4Ok3nPVU88ieoJ/4Uol7cNxtbTl4eGENUqldPPxHmxb77sq+EksdjsWHeFDg6zS1iQRjDViAq5di2Rn1xO9Ec/xPrTH8Ft3SZoFVEx0nuufMed6P/9MToSRS9ZAvV5Hz9UB7VsGcbTTxH79sVTf5GzwNxGVFSbtj6UWopSSwJZuEKIcmAbVMGXb9ejyy20XoHn7YPWeyPEDkhpopSDr5Cz8VdOBlq7GIaHv6LJ4K9ytiIEgb9dGr/dVwMiwazTC1BqZTB8S1B0rMDwNRMM5vaiVN2YNYoQNaTMByq92We+IyrajoYwDIpf+jKlD3wQtWw5olhEDA4iiiXUjjtS+vgnKX3yU40Wtuep4MIvhpSz814MIypmj7BlN4Z2HBzk+nUk/t8HsW/+E3he48OgVq6k9OGPUj3l1AmPPZGDw0TqudhVP0NUKqgddpj4JA0DHYtj33ADpfeeh162rGOvf7rMnYNDq1WYiec9A/gXlnUfvrgghi8q2AK4OM6zkDKHEDaWZWOaPbjuUly3AqTw5eFlhOhHCL8QeV4PQhQxjLtRag1CDAd+evnAXiiKlJuAAkotReuluO4uCOFfMftR5/UWkg6K4Qq2OU74BrBaJ5FyQ+d/ZU2YDweHaUVDmCaVd7yLyhvehPWPvyOGh1E9S3Cf85xt6bIj8O1+7IawYDYIHRxmh7AgTUArgYPcsJ7M8S9HbtnivytNsxEgJjdsIHnuOYjBQSpvf8eExx4pcHAcb0LvOfOeu9Gm0VK4oBMJxOAAxn/+g9sFBWmuBA5KrUbrWLAvMz76ASJIWQ5MVONBO8zA85IIsRHDeBrDSATec4JKpYrWfZimxPOei1JJDOMRTLOMUulAGBHHtp/GNPvxvApS5oPVT30PailKZYPWnRmsyiwc5yhctxwo8J5EqSy+WevYdlI1cIuAyW2ROsdcCxxisSjxeBSl9PS856JRnOe/YNK7+Z2OUihwWICELbsJaOXgkPjEx/1iJITfSqsXDSkbV2uJz3wKuXZt0+NXqw6maTaPhlCKdr+YxBxO+0/GXDg4KLUCz9sDw1iP3yYbjWHchxBbcJwXUaudgOseheO8EMd5KVqfhGUlMM0teN5DOM5atF6PlDk8b8dgrkghhApabkuor8hctxcpQcp+YAgYwHcF34Bh3IVhPI7n7YvjHI3rHo7n7Y/nHYbrHgP04nmrghbf+GLk71WtRIjCnA3K1pkLB4fpJLfOlGrVwXGcWZWBhw4OnScsSE2YSOAgN23E/t1v/UuwZquXYL8netml4/6q7j2XSMQaBWgilwN3/wMQrjPKbn8solSCWAx39z2m+Mpml9kXOAhc94W47j4YxlqkfCpYkQxgGPcj5eNBMdgT32Q1hpRRYrEosdgqlHoplcquOE4Sz1uJ5x2A5+0VZCtJhNiAEAMoFQ+UcVuBAZTKoZQXFKwyUg4hxDBS9lN3EveLYS4oPFGUWhU4S+wNSKQcYpvlkUaIIlIOBGpAjVIpPG/3Wfq9TcxsChyklKRSiY5HQ7RLKHBYeIQFqQkTCRzMO+/0hQutPrhCgOdh3XrLiJvGD7fWJdITCRxqJ56IjkQQxSYDk0ohikVqL3oxesWK6b/IWWAuBA5ap6jVjqNWOwGtV+FHlXsotRyldsN1n4svYqhbLUWQ0qVYLFIu2yi1Mpj58RNhhaggxMZg6PVhwO/xC+EhhA7miLaidRXDMPGjKBJovQyllgdtuBiGsRHLuh3D+A9K7YRSOwbn+jIc5/koFcU0H0DKJwOfOweldkPrLEIM47qHovXyWfmdtWI2BA6dSG6dKaHAYeER7iG1YKzAYTIVXQMhEDV/5dPKe65ZRIV78DOpvvJEoldfDa6HTqf9Iqg1VKu+8/HKlZTfNvE+1XwzNwKHaLAS2hdfvCAxjMeR8neAEbREK0j5FI6zFs+rIqUJrEKIdUhZC4Zk+wIp+RNBgbDRujfYn4riF6eNgZDBwvNsYAlaF/Hl4hJ/CNbBj0h/ANfdC8d5PvWPl9Y91Gqn4nnPxLL+EKzq3EC1589Bue4huO7Bs/S7mpxOCRzGDbfO4YpoIkKBw8IiLEiTMFLgUNhtNxCydctOa5AS9Yx9yGRSLb3nmjo4CEHpY59AJ5NEf/Yz5NZgz0prtGni7rsvxc99HrX73LZ32mVuHRwkvrMCaB1HCINYzMCyhnDdf1KtDgatNwvf5fsvSLkJ1z0CxzkMw3gsWAFtwI+KcBGiH62raB1FiGrQEizj71kppKzhectQKo2UBeqpsf5sUTooaNlx5+m3756BEFuDFZKL1vHAJHZ+oyFmKnCYlnpuDggFDguL0Fy1DZJJm1gsQiFfIPL8IzDvv3+0oGEknud/Cm68Ee8FL6BQKLUM6DNNg2w2TaFQmtDBQWzaROQPv0du3ICORHEOPQz3ec9r3TbsErLZNFrrOXJwgFjMIBa7FqXW4bqPolQFP15CBPepYhh3A9XAaXslUj4FOIEsXAaRFU5QjHJonUCppRjGeoSoBkO3Cil7qNWW47fv/OBAv63XQ632cqrVU4O/WzhIKUclorbLSPVcsVjqOksigEwmiRBy1hwctNYYhqSnJzPCzy7WMFmVUtDXN8TSpT14ntcoWp7nzqmDQ7ebq4YrpDZoRFQk4hQ+/glSp5/uF56RRUlrXx0nBBxzDMVDnkulDd+6ySIq9IoVVM5+/Sy8qtlnriIq/Lj4ekbRAQhxK0JsQuvdGalWFGJ9YN2zLHAAfygQFCj8lVYGz9ul4SDuS7szwFJ8VV0RPzMpCeQwzSJKpQMRg+807v/fZCF+tKYaUdFt7blW1K27YrEI5XLnI1vCiIrO0P2X2V3ASIGDfcIJ5L/9HXQ05hcl1/XtTAjGG1/6Uga+9V0qU/gCbuXgsJCZbYGDYUjS6STpdBLXdRkczFEqrQmSWiNIuREh+hCiP3DYfgLfwUEDVaTcjJQbkXKkhDyJH7YXB9IIkUeIvqCtIhDCQUo/N8sw3ODPw8EelIvWCTxvF8b77C0M2hE4zLd6bjqEAoeFwcK7jJsnRgkcTj6ZwSNfSPRnVxG99RZktYLeYw8Kp55O9cCDpuzEPZGDw2JhNgQOfnsuRiwWQSnF8HB+xN6Ai1I7oPWSwDJoCCEchCiidRKtTSCL35YrB1Y+W5Ayh1IZ/I+EDu67BCm34puypoL9pCq+KauJ5zn4juAW/j5WBT8ao7uk+FOllcCh7eFWrTH+8zCyrw8dieDuux/E58emqE4ocOh+woI0BeoCB8ep8tX//SHr16/jh7/+FcVKbcbJrdONqOh2OidwqAIa206SSCSQUlAqVSiXx+51WMF/Cs/z4yekfAopN+F5azCMdWitg1WSH1Gu9XKkfCzYA1oSPN5Xz2kdR6kepPTwjVE1vrVQNdg3iAYrqlJQxHYIwvgWLhMJHKbSnrNuvYXIZZdi3n8/olpFGwZq+XJq//VKKmeeBa2iJGaRUODQ/YQFaQpUqy6XX/4TvvnNbzA0NMRBBx1Ef/8QhtGZUL+pRFQsJKYfUeFhGA9jGHdjWeuwLAPTXE2tdhDDw7sHK5OxGHjePljWnwF/pkfKdYFDQhKtt+IbrGp87zoHsFGqFyGGARetVwSrq2JQvEqBZ90afPFCH5ALnBXAMAxqtX2o1fZHCJByM0qtmv4vrAuoR1QkEjGklG2r5+yfX0/8yxchymXUkl7U0qXguciBfmLf+TbmA/dR+NwX560oVcOIiq4mLEht8sAD9/HlL3+Rhx9+kFQqxcc+9jFe+tJjO1aMYHKBw0Jm6gIHB9v+LZZ1O6ZpYJrL0FpQrf4bpf6FZe1PtXoSfubQaDxvTwzjPqR8OrDkqeDP+0RQagdM8z9obeN52cCXzgxUdYXAMiiDlE9hGAN43h543v74abGb0XoZQiwPjp1Aqb2JRtcg5VK0LiPlo/hFbmGjlGqkKLfrPSfXriX+9f8Bz8PbaedtrWvDQO2wAsplrFtvJXrVlVRe/4bZfxFNCAUO3UsoamiDxx57lLe97Q08/PCDvPzlJ3DNNT/nrLPOIpNJd/y5QoGDj2X9lUjkb0QiazDN/ajVeiiVkjjObnjeGkzzbmz79xM+VuteHOfFaB0P5owKgThhGCEcPG/nQHiQCMbG1jXUd7Xai1BqT5TaB8c5GMc5IJgTSuG7fzsIUUKpnXDdo/G8/ahUMliWiWl6gZnr7OdCzSZ17znLMtEaPM9rS8pt//53iKFhv/hM9G8ci6EtG/uXv4Dq/HUAQoFD9xKukNpg2bJlvOY1p/LCF76Ygw46GKBx9dhpEUIocADDKJNI3INpLsdxktRqlTGrxShKrcY078N1D5+wPabUrtRqJ2EYj+JHR/wHrTNBBPmKYEU0EBSrfkzzMZTaESkFnrcrtdpxaB3HNO8NClYeIQbxjd19g9f68KvneTiOSyQySKm0EqXm3v6nE0w03GoYRtsODtbf/oq27ZaiHpXNIjduxHjkEbz99+/0S2ibUODQnYQFqQ3S6Qzvec/7R93WKqJipmyvAodt6rmn0Xor5fIueN7E7T2/rbYeKZ9oul+jdS+u24vn7UQkcg1godTKEX+/HK2XI0Qax9kBx3lJ4D9XnyWCWm2XQD4+gGUtD1weDmTsKqha3Uwi4WCah1Ctdq6NO1c0U895nmrfwaFa3RYm2QzD8L0YndmbS2uHUODQnYQtu2nSKqKiExQKpeDYC3OepRnNIips2yKbzRCLRahUhqhWa3jeZG9PEewPtUbrVTjOi/FFEo8G4oUqQuQwjMcRooTrPh/P2zfwlxt5nSZRakc87wCq1dfhus/EMJ4M/OgGAy+8R4BhKpXDiEafiWEsnI9VO9EQ7UZUqJ12QlRa/3uIUgmikebhk3NIGFHRfSycT04XMlFERacYKXBYbEv3kREVEw23VioySN6Y7KpV4wfxTY7n7Uet9l+47v748RGbEaKA6+4V5CY9a9JjaJ0OXMaPD1ZaHiBx3WdRq51EsXgwSulZ/oLrDFMZbm03oqJ27MvBkNCsKGmNHBrCOfQw1Kq5zX1qRhhR0V2ELbsZUHdwSKViJBJxCoVSR49fLJYDW5xYx489n7iuS7VabaySxg+3rkHrpUi5dVSLbSS++3YSpXZt+3mV2hmldsZ1/RUS2MGKaCoFP4bn7Re4jDv413TbPkaFQolMJjWnseBTZTrJraVShUjEJpmMk8tNHIviPP8FOM98Ftbf78TbYcVoabfn+X6M2SyV08/s1EuZMSMFDtVqDaU6XwDqAodoNEImkySfLzXadXWBQ1/fYEPgUP85lUowMJDHd5XfPghXSDOkUnEbAgfTbO+NI4aHsX/5CyI/uRz797+D0sTFpi5wmMqxFwK2bWGa/gyR6/pGk6N7+Elc9zlBS2yiPbQKUq7Ddfed1ryP1plg/yjL9CPDBf4+0uhrOsdxqVZrJBLxjq+aZ8pMk1vrF0i2PdH8FxCJULzg8zjPOxQ50I+xdi1ywwbk009hrHsavaSX4qc+jXfQQR16RZ2hroabrZVt3UC5Ps+VzabQWpPN1o1OdaNQ1fPX8vkihiGJx+3tapUUun13ANMUZLMJPM9rLUIol4l/8QtEr7oCUdh2lamWLKHyxjdTfte7wRy/aK2/cRe6wMEw/A+bbVvBHpFHLBZlaCg/gYODg23/Bsu6Az9NdQn+npFvBeS6+1OrnYTW3edevM01uzsMR8eq54rF0rSjIdLpJIYhGRxsMVTquli334Z1ww0YG9aj43Gcw4+gdsxL0b2903wVs0skYpFKJces1GeGH8wZIxqN4DguhUKx4XZRqdSIx6PB4LVotOWVUgjhu5AYhl/A+vtzaN2ZtUO3u32HBalDNCIqCkUqlQkURNUqqTecjf2XW9FSQiTiR0h4HlSrCK2pnPxqil/+6rhoickiKrqdsd5zhUKp8aFvHVHhYRgPYRh3YxhP4+8ZLcd1n4nr7osfoNed1NtiQ0O5ec0F6nQ0xHQjKhYCnYyoiEYjDRFIsVhuDLo3i6jIZlMNW6Z43G911mekXNdlYKDUkb3kbi9Ii24Pae3aJ/nqVy/kvvvuJR5PcOyxx/GWt7wTy2rSZgh49atfwaZNG8fd/sc//pVIZPINz0ZERdCLHlvmoz+53C9Gtg0jz8UwIB5HV6tEr7sW52XH+pvDI1jIDg4joyEm8p5r7eBgBPs1++Gnwmr8ItRlvbAJKJcrRKP+nEuzPZfZZLaiIaYaUbGQ6ISDg2kaJJNxDMOgUqk12oHQ2sFheNh3cPAvznL09GQYGsohpS/6sSy2CweHRVWQcrkc73nP29lpp5254IKL2Lp1C9/85lepVCqcd96HJn38UUe9mFNPHb3hatvtSbpbChyUInrJj/3v02aFMRKBYpHIpZeMK0iw8AQOY9tzxWJ5wi+vkQ4OtZrToth272qoGfMhcJiL5NZ2BA4LkZkIHMa254aH8xMOtdcFDpFIpOHgMHLFOVLgkEr5Rcu2re1G4LCoCtIvfnEtpVKRz33uItLpDOBP0X/lK1/kda97I0uXLmv5+CVLlrD//gdM+/lHRVSMcFmQmzYin1qLbrYZHKANA+vOO7aF/438uwXi4NA6GmJiZiOiohsYKXBwnOFxq+ZOMx313HRpFVHRlXge9q9/RfRHP8D6179Aa9x996PyxjdRPelV/gUh03NwGNmey+eLk3o1hg4OzVlUKrvbb/8bhxzy3EYxAnjRi45BKcWdd94+J+dQKPg2N6MGP2uO/y6c7I0kBIF52IR/XanUcF23ZXjafDJyuLVUqkygnpuYuoNDNBrpqFltN1AslpFSEJtFd+uZquemw8iIiq6nUiF15umk3vImrDvu8B0lajXMe+8h+Z53kznxvxDDw8A2B4dIxMayWl+v+3u7KRKJGNWqw+Bgri3j4LEODv48kmw4OMRi/uegUCgFv1/R+FnKhdOunw6LqiCtXfskO++8y6jbUqkUvb1LWbv2yUkff8MNv+foow/jmGNewAc+8B4ee+zRKZ/DRA4OaocdIJFoJMs2Q7guauUqaNEm7EYHh4mGW8fnFLWmmYPDQkcpRalUCb5kOvtxm+/k1nYdHOabxEc/jH3zn7ZdEEq5TTgkBOZd/yL5jrc17j+Zg4MQgmQyHghyYHg4T7FYmuLebujgMBGLqiDl8zmSyfEqklQqRS7XWjnz/OcfyXnn/T/+53++xXnnfYh169bxzne+ifXr1035PMY5OMRiVF79GoTW0GwTOFgVVc48q+Wxu8nBQQiIx2Nks2kMQzI8nCefL057o3ukg8Niolz2p/M7OecSi0Xp6Uljmia5XGFW9oomo10Hh/lEbtpI9Iqf+p+7iT4vvlsu9h9vwnjwgcbNzRwcotEIPT1pbNsiny823SuajNDBYWK68100D7z3vR/kpS99OQcd9Exe/vIT+OY3vwfAFVdcPuVj1QUO9SE3gMqb3oLq7UWUyuNbcq6LqFTwdtmF6qmnTXr8boiomG57rhVTjahYSBQKpdZDpW0yH+25VtS/SLt1ZWv//PrJ2+V+qiKRq69u3DQ2omK67blWhBEV41lUBSmVSlMsjlf95PN50umpZRctXbqUAw88mIcffnBa5zLWwUHtvDO5S3+Ct+MqRLmMKBSC/4qIahV3r73JXX4FOpud9Njz6eDQifZcK/yWk+j6NtBUmamDw3y351oxqYPDPCK3bPEFQpMVJEBu3jzq5nqxyGRSM2zPNacucIjF/FZcXeBQKJSCQpUYMbO0+B0cFpXKbs2aXcbtFRUKBfr7+1izZpc5P5+xERXeAQcw9H+3Yv/+d9i/+y1ycBC1fDnVV56Ec/TRE7o0NGOuIyqmo56bDpNFVCxkisUyPT1pYrHYlIrJXKrnpsNIgUO3nZtOJP12XatVkp/SiE6NbvfX5w/9FUt5VgaBw4iK0SyqFdKhhx7OP/5xJ/n8ti/om2++CSklz33uoVM6Vl/fVu69926e8Yx9p30+E0ZURKPUTjyJwne/T+5n11D45rdwjjlmSsWozlwJHGajPdeKUODg023tuVZ0q8ChdtxxzfdtR+K61I4/HhivnnMct+15xOnhCxze8pa38NWvfolisYzjVBkYGNjuBA6LqiC98pUnE4/H+chH3s+dd97Ob37zSy6++Gu88pWvGjWDdO657+CUU05s/PnGG3/Ppz/9MW644Xf861//4Ne//jnvetdbkNIYNyg7VRZyRMVst+dasT0LHLq5PdeMbhU4eM/YF+eww31V3URf3sHqyNtjT9wXHDmheq5QKM5JRIVSimuvvZa//vWvvOMd7+Ctb30TW7b0sXHjRu6++1/bhcChe945HSCdTvO1r30bwzD5yEfez3e+801e8YoTOeec80bdz/O8Ua2glSt3pK9vK1//+pd53/vexXe+80323vsZfPe7P2LVDHNbJhI4dJLZEDh0Wj03HbZXgUM3qOemS7cKHArf+S5qxx39olRv39UVr1Kie3qoXnkVPUsyE6rnxgocOo2/Es7ykY98BMMw+OpXv8whhxzC008/zVVX/ZQf/OCHnHvuOdx222309fXzne9czLp16xalwCE0V50jstkYlmUyNJTruMtCNGqTTCY6cuzJvOfmEikF2WyGarW6qBwcAFKpBKZpMjTkOzjMlvfcXGPbFul0suv2ukRfH7FvfI3o5Zch6i39WIzaaadj/PdHMXbZZZz33KjHC+jpyeA4btsODpMhpSCRiBOJ+AKFctnhi1/8PFde+VPOOec9XHvtNWzdupVvfOMbvOtd72LNmjV88pOf5uyzz+J5zzuUL33pK/zmN79k/foNvOUt725rZdrt5qphQZoj2o6omCYzjaho13turolGIyQSsSYRFQuXumt2pVJDStGRaIhuoa2IivmiXMZ4/HEEmui+zyC6bCmO41Islia9mOtkREVdqOKLGlwKBX/ftFAocPrpJ1MulzjnnPO48MILOOKI57PLLrvwk59czgc+8AH++c9/cvPNN/PlL3+Fm266kd/97nd87nNf5MgjXzzp83Z7QVpULbtuZkKBQweZrsChG9pzrVjMAgd/LMBekO25VnSrwAGAWAzr2c8i/YIjsHuXTGm4dTIHh3awLJNsNk0iEQtUk2WGh8uNf/dkMsk73/keyuUy/f19PO95h/PXv/6FPfbYhyVLern44ot53evegG3bXHTRhbz97W/Hsiy++c2v4boLL5pmLGFBmkO6TeAw1+q56bLYBA519ZxlmWit8Tyvq9pbM6VbBQ6dGG5t5uAwGVKKhlDFMCSlUo3BwSLV6vjP28tedhyf/vTnOeGEV/Le934A27a5/fa/NQrVZZddxplnvp5NmzZx7bU/5+yz38CGDRv48Y9/NKVz6kbClt0cE42apFIxKpVqx2MkhBD09KSp1ZyWx+7W9lwrkkn/fAcHcwtWVTRRcquUkkwm1XV7Lp2gpyeN56l5j6gYGw3RTnuuFfVjDQ4OtxVR0aw91y5PPbWWVCpNNpvlve99J08//RRXXHEtZ511Clu2bOYHP7iMD3/4PPr7+7j00qvYaaedmx4rbNmFjGKsg0MnmczBodvbc61Y6A4OzdRzM3Vw6Ga6wcGhU95zI6kLHyZr3U3WnmuXnXdeQ09PD0IILrzwf/jxj68gEony/vd/GIDBwX7OPff9OI7DN77x1Wm/rm5gUTk1LBTGOjh0kmYODt2knpsOC9XBoR313HQdHLqd+XRwaJXcOlPqERWpVJJKpTquzT1WPVcq1SiVqh3Jw4pEIg0Hiec97zBuvPFWLMtCa82LX3wMGzasn/mTzCNhy26eSCZtYrEIhUKRSmVmJo1j8XvlaQqFUmMTdiG151rhDy1qhoe7+/04UXuu1ZVxva0zNJRbFMKGOnU1YT0RdbbpdHuuFZlMkr6+foQwMAOnlZm252ZC/at8/fp1XHHFZdx//308/vij2HYE0MTjCU466UTe+973Tug8obXm+9//Pj/96U8ZGBggmUwCUCqVWL16NWeccQannTa5+fNMCFt288RcCBwSiYXZnmvFQhA4TGe4dTYiKrqBuRQ4zEZ7rhX9/YMce+zLuOCCz3SsPTcThBAIIXjiice47ba/ssMOKwKBk+aCCy7irW99J1deeSXHHHMMBx98MEcccQQXXnghtZp/Qfz973+fr3/967z+9a9n1113JZ/PUygU+PSnP80LXvACPvWpT3HKKadw6KGHcuCBB/Lyl7+cX/7ylx19DWFBmidm08HBti0sy+/bu67Xteq56dDNDg4z9Z7rVERFtzHbDg6zEQ3RDoZhst9++3HDDTfw73/fM6l6bq444ogjue6637D//gcAsMMOK3je8w7jyCOPRgjBpk2bOP/883nf+97XKFAHHXQQX/nKV9h333156UtfykMPPcTHP/5xli5dyl133cUb3uBLzZ944gnOP/98vvvd73LaaadRq9X48Y9/zN57783b3va2Sc5scsKCNI90WuAw1nuuVKpgWeacR1TMNt0mcOiU91wocJganUlunT7xeIxPfOITGIbB+eefz+bNQxSLndkrmgn1lejtt/+N3t6ljT//4hfX4nkeQggcx+GYY45pFKizzz4brTUPP/wwr33tawG48MILyWaz/PnPf+aiiy4iGo2yzz77cMwxx3DYYYfxute9jhe+8IV84xvfIBqN8ve//51nPvOZHHnkkbz//e9n/fqp72eFBWmeKRQqaK1ndPXYTD1XLldwXXcRDpVuUxMaxvwW2057zxWLZaQUxGLzF744G4wUOHSCuW7PjWRke27PPffita89lSeffJKf/nTqYZ6zydq1Tzb2gcAvUIcffjjLli3j8ccf58orr2wUqI0bNwJ+1MbWrVvZa6+9SKfTPPLII2zYsIFf//rXlEolzjjjjFHPcdFFF7H//vujtWb58uV861vf4sMf/jD/+c9/eM1rXsPAwMCUzjksSPPMTB0cJhtuLQznMf/v/0h9/7vEvvVNrBtvhNrstzNmm/l2cJitaIipRlQsJDrh4DBf7TloPtx69tlvobe3l0su+QGbN2+ak3Nph3w+h2luW5GuXfsku+22G5lMhuHhYW655ZZGgdqwYQOGYaCUQgjBGWecwZ577tnYX1JKceKJJ3LZZZex3377ccQRR/D+97+fG2+8kU9+8pMceOCBrFmzhsMOO4zjjjuO73//+wwMDPDzn/98Sue8uN7xC5TpCBzaiYaw/nIriZe9FH366dhf+TKxr3+N1DnvJPvyl2JfeSXy8ccRg4Oz8IrmhvkQOMxFNEQocBjPfLfn/JVwJlBNugwOlhrtuWQyyTnnnEelUuHhhx+ak/OZDvl8blRy9uOPP94oUJWK/547/PDDWbp0KT/+8Y958sknOfXUUxv3v+aaa4jH4/zwhz/kda97Hb/5zW/Yf//92W233cbt565YsYIlS5awZcuWKZ1jOIfUBdQFDqlUjEQi3tJlod3kVusvt5J8z7sRhQJeJosRj4HW6IFBjAcfJPXec1DpDKRT1I46muoZZ+I+81mz+TI7zkiBQ63mzPqX01wmtxYKJTKZFLZtLSoHh1KpQiRik0zG23ZwiEYjjVVVPl+csxUR+CvhRCKOaRp4nqJYrEwoWHjJS17G/vsfxPLly+fs3CYjlUrjuuPfO8PDw2QyGXK5bQXKtv2ZqZ133pkHHniAJ554gl/+8pfcc889jcftuOOOPPjggxx66KE88sgjpFIp7r77biqV8XL+J554gv7+fnbfffcpnXO4QuoS2hE4tO0953nEL/gsolBALVsGERulFLK/H9nfB54HWiMLeXA9Ir/+Nak3vwn7V52VcM4FcyFwmI/k1lDg0J3tuVbquRUrVnSVd9+aNbtQKGwr+qlUmv7+frZu3cpuu+3WuH14eJgVK1YA/kVerVZDCMFee+3F448/3tinPfDAA9myZQvr16/n61//Om984xup1WqsXbt21PNqrfnsZz/L8uXLOT5I4W2X7vnthTQVOEw1udX6y60YTz6BymRofJvlC+iBAf/PluVHpisFQqB22AEqZRKf+TTGI/+ZzZfYcWZT4DDfya3bq8Chm9tzC4lDDz2c/v6+xuzhmjW7cMcddyCl5IgjjiCd3lagDj30UAD+85//4Dh+t+H+++/nhhtuIB73v482btxIb28v3/3ud9l7773Ze++9ARgYGMB1XVzXJZfL8bWvfY3bb7+dCy+8sPHYdgmdGrqMkQ4Ojz76KK7r8exnPxOlVOC8MPl8Q/TbFxP/2v+gli5t3CbXr0eUywjTpPEP7rroniV+QdIauXkTlTe8idKHPjw7L24W6bSDw8j2XLFYmre22fbm4DCyPVcslue5PVed13mi6VKpVLjttr9QLpe58MLPIQScfvrr+Pvf7+CBB+7jNa95DU8//TR33XUXu+22Gw8++CA77rgjAwMDVKvVYP8y0XCit20bx3GoVCqce+653Hbbbdx5550tz+GCCy7g1a9+9ZTPPdxD6jKKxRquW+Nb3/oWl156KdlslhtuuGlK3nNi7DWG6yIqFT+uuemDBNq2sX//2wVZkAqFEtlsikjEntGXWLclt5bLFaJRm0Si/T2XhcBIgUM9pHC2vOcmYza95+aDwcEBPv7x0Z/hSy75IUBjuDWfzyOE4MEHH2TZsmUA7Lvvvjz11FNs3bqVYrFIJpPBtm2GhobIZrNUKhX6+/s57rjj2Hnnnbn++us5/vjjefWrX83nPvc5KpUKTz31FG9+85unVYwgLEhdhdaam266gYsv/h+2bt3KzjvvzAc/+KEpG6G6e/pLaWo1sG2/NQfAiM2I4NOmRyrUTBNRKPr376JeeDvMVOAw1nuum8LyFrfAIUImk8QwDBzHZXg4j1q3DvtPf0IUi6gVK6i9+CUwxdZPu8yn99xssXLlKv7yl39M+He53BbOP/981q5dSzweRwjBmjVreMc73sFVV13FP//5T5YtW4Zt2/zpT3/i6quv5mMf+xiJRIJPfvKTfOtb3+Kqq65i+fLlnHvuubz1rW9tKOzWrl3LKaecwvvf//5pn3vYsusShoeH+MQnPsI///l3IpEIb33r23jLW95MuVyd+sBfrUbmuJdhPP2037ZTCqO+8RgUGuG6aMPA2213CPZeRF8favWODP/+xk6+tDlDSkE2m6FarVIstr+y6Zb2XCtSqQSmaTI0NLxgr9zHUm/PSSl9B/r1G0h8/L+xf/NrRK3m73dqjc5mKb/tHZTffU7HLpQWS3tuqozNQ3rsscc4//zzueuuu4jH45TLZfbbbz/e8Y53sHnzZr7whS8QjUaxLIs//elPAJx99tls2LCBG2+8sXGMV7ziFUSjUX7wgx+MEnYsWbKEnXduns80lnCF1CXcd9+/+ec//86RRx7Ne95zHqtXr8K2bQzDmHpEhW1T/uCHSHzw/cj+flQ2i47HEYWC/yH3PLQQsHx5oxihFMJzqb7yxI6/trliqhEV3daea8ViiqgYGw1hGJJIpYT12ldjPvgAWkp0NOq/V5VCDA8T/+IXkBs3Uvzc55mJ7HCxtedmyu67786Pf/zjxp/rBepd73oXiUSCV7/61bzvfe8b5Q6ulBr12brnnnvwPI9isTjODfykk07iC1/4QtvnE66QuojBwUF6enoaf04mI8Ri9rQjKuzf/ob45z+H3LLZ30cqFPxWnWWhly1H9i7B0xrteoitW9DLlpO76meolas6+bLmnMkEDlONhugWFrrAoVk0hJSSnq9+Cb70JbRpbrtIGkmthtCa3JU/wzni+dN6/sXYnpsq3Z4YGxakLkYIWLIkAcDg4DRbNaUS9o03YD5wP8ZDD2HdcQdohY5EEbaFcBx0tYZavpzCV76Ke8hzOvsi5gHTNMlmUxMOUS6E9lwruiUWfKq0VM9VKix57rMRAwP+ymgitEZUa1RPOIHCd743pefeXttzExEWpJAZEY2apFIxKpVqSweHdjEefojItddi//53iHIZuWwp7kmvIn/c8agdV3fgjLuDZNIPJRwczKG1XlDtuVZYlkkmk5p1p4hO0U5yq3HvvWSPP9afjTPN5oKUahWdzTL47wfaeu6x7bly2dmu23MQFqSQDpDNxrAsk6GhXGcdjZUiGo+STCY6f+x5pi5wqE+dL7T2XCsWgsBhKsmt5l3/IvOK48GyEaaB1qAnGlCoViGVYuCBhyd9/rA9NzHdXpBCUcMCoFCokM0mSCbjUxc4tEJKKpUa0Wik88eeZ5TSOI5LJGLPiffcXDLrAgetMe69h8h112H+6x8I18Pdbz9qJ56Ec/gRkyrdpuo95+28Bm3b4LpgGv50wgT1SGiNs+deLY/VrvdcSHcSFqQFgB9R4RCL2USjdnOBQ7mM/bvfEvnFz5Fbt6B6llA74RVUX/FfMCIXZSz+UGmaaDRCpVKdpVcxd4xsz2mtUUotmmIE2yIq4vEo1WqHr/yVIn7RF4lc8VNEpYK2bBAQefIJ7D/8ntqLX0Lxc1+YcC6onfbcROjeXmrHn0Dk+uvQSiGkRAgx+rGB/2L1zLMmPMZ8qefWrXuaK664jPvvv48nnniMnXdew2WX/WzSx2mtufzyS7j++qsZGhpizz334pxzzmukvG6vhAVpgVAsVolETOLxGNVqbdwHzfjPw6Te8HqMp59CE1xkao196y3Ev3wR+R/8CPfgZ054bNf1qFSqwRdcbc4m5DvNRMOtQsiOODh0G7Pl4BD9wfeJXnoJOhJF7bCiIbHWAKUSkT/8HmIxip//YuMxY9tz0wnLK7/nXOwbb0QU8v4wd11ppzV4HsJ1cQ862L+4GsN8tueeeOIxbrvtr+y7735orRq+cZNx+eWX8KMffZe3v/3d7L77nlx33dWcd967+d///Qk7LqK93KkS7iFNg7Vrn+SrX72Q++67l3g8wbHHHsdb3vJOLKu1e/FMr4qaCRzEli1kX3EcYuNGiMVGy2Y9D1Euo3p6GP7Fr1G77DLhsYUQ9PSkqdWcjogn5ppW6rmxAofFQqcFDqKQJ3PsSxHDOXRv78T3yedBKYavuQ61++4d9Z4z//VPUm95E3LTpsYMElKCEDjPeQ757/0QPcKfsRvUc0qpxiDoBRd8ioceemDSFVK1WuW//uulvOpVr+Vtb3sXAI7jcNppr+LQQ4/gAx+YPeuubt9DWlj+MF1ALpfjPe95O67rcsEFF/HWt76TX/7yer7xja9M+tj6VdEpp5zOhRd+ld7epZx33rtZv35dW8/dLKIi+pPLkRs3+m2UsTMchoGOx5GDQ0R//KOmx/bbHOWW8RfdSDvREHMRUTEfdDqiwrr5ZuTAADqTaXofnUwiKmViN/6h49EQ7rOezeBfbyf/zYtxTjwJcdxxOGe9juGrryN37c8bxWg60RCzxXTiJu67716KxSIvetFLGrdZlsULX3g0t9/+106e3oIjbNlNkV/84lpKpSKf+9xFpNP+B9fzPL7ylS/yute9kaVLl034uGq1yuWX/y+nnnomp5zi59IfdNAzOe20V3HFFZe3fVU0TuCgNdErfuL/ZbMPh5RoKYhcczW1lx1L5He/RW7YgE4kqL3wKGovPw5isQUlcJiK99xUHRwWEp0UOMgtWwDhy6+b3kkgEMSGh3CCaIiOqjOjUWonnUztpJNJp5P09W0FDOyg4i4G9dzatU8CsPPOu4y6fc2aXdm8+Qqq1QqRyOK6eGqXcIU0RW6//W8ccshzG8UI4EUvOgalFHfeeXvTx3XqqqgucDBNk2jUhlIJ0d+PnqRdiGEg+/pIn34qkUsvwbrxBiI/v57kee+l56gjMe+8A/AFDv6xI22f01zj59WkMU2TXK7QlhFqpeJ/cY3Nmlro1AUOsVgEw5jZx1lHo6AVzZQAQgo/c0oKqkJ2vhiNYWgox3HHHccnP/nxIKwvTSIRC1STZYaHywuuGIEfJW7bNpHI6M9YKpVCa00+390Xg7NJWJCmyNq1T467skmlUvT2Lm1c+TR7HDS7KtpEtdq+o3ex6GeWxOMxRMRumFA2RWtEseh72JkmOpVCZzKoTAadSCA2byL15jdiPPDAKIGD6EQfqIPMNLm1UChhWSaRkQ7ni4ByuRJk2Mys2DqHHoaOxvz3Sh2tEUphKA8pBLpcQQlB5XmHzvCsJ0dKybOe9SxuueXP/POff5/39lzI7BMWJKBarXD66Sdz+uknjyoMudwwr3zly3j729/YaPPk8zmSyfEbg6lUilwu1/Q5OnlVpLVflKSUJLIZnOcdinBbfEBrNV82a5pQN62sYxjoVAqRyxH75tcBGk7ZzRI955pOJbeOjKjotmI7UwqFUlux4K1Qu++Oc8QRvtLNcRC5YYyn1vopwo88Ag89BE8/jbd69bT95KZCLBblYx/7GJZlccEFF7Bx48CCTG4dSyqVplarUa2OHrGoZxSlUt0tPJhNwoIERCJR/vu/P8369ev43ve+1bj9y1/+IoVCgY9+9JMdj8eeKSMFDs7Zrwch/Un2CRAlXzWnE4mJnZKDWHP7jzchNm3qKoHDdNpzrQgFDq0pfuwTeHvuhbH2SeS6dVAKosOV8otUtYLs75/VqHvLMhvtuTVrduHMM1/H+vXrufTS/52155xL1qzZBYCnnlo76va1a59khx1WbLf7RxAWpAb77bc/p5/+Oq6++kruuecubr75Jv74xxt429vezc47r2ncL5VKUyyOn/vI5/Ok0+mmx5+Nq6JCoYLWmuhrTqZy2mkIx/EdvV132/xG0KrDNNGx5isebVlQq2EErcVKpYbruvO25zLT9lwz6gKHaDTSdRcZM6VYLCOlINbi33kyjNU7Io95iX+REkiuAV8Yk0qhVqxEDgyQfPc7odxZl4hm6rnTT389y5fvwE9+cknbitRuZv/9DySRSHDzzTc1bnNdl1tuuZlDDz1iHs9s/glVdiN44xvfyt/+diuf/eynKJdLHHzws3jNa04ddZ81a3YZt1dUKBTo7+9rXPlMxMiroj1H2J/M5KpopIND5StfobjrbsR+8H0/bkIpEBLV2wuVsh9hPtmlsxitsJoPB4ex6rnCXfdg/uIXpG+9BVGp4K1eTe244/0U0URiWs9RqVQbasJmERULkZk4ODSGW7VC/+xn6HgcnUqD4wAaTKsxUqANA2PtU9g33kDtv17ZkXNvpZ6LxWK8970f5L//+4M89NCDXTU4WqlUuO22vwCwadNGisVio9AcfPCz6enp4dxz38GmTRu56qqfAxCJRDjzzDfwv//7PbLZHnbffQ+uv/5qhoeHOe20M+frpXQFYUEagWVZfOQjn+DNb34dth3hox/95Li9hkMPPZxLL/1f8vl8Y1Vz8803IaXkuc9tvtE78qqoXpA6cVXUcHBIJhh8+9upvPFNWLff5ivvslmcQw8j8cmPE7nqSn/V1KQoiUoFncng7r1P47a5dnAYOdyayxXgyitJfPUriHwO7AjaMLA2rMe64w4iV11J8cKLpu1Q7hfb0MEBRnvPlX71G6KbN/vFSEqITKC2NE3QCvt3v5txQWrXe+7II4/i+ut/x5IlS2b0fJ1mcHCAj3989MhG/c9f//p36Ok5BM/zxo0anHnm2YDmyisvZ2hokD322IuvfOUbXVVs54OwII3hjjtuA6BWq7Ju3dOsWrXjqL9/5StP5pprruIjH3k/r3vdG9m6dQsXX/w1XvnKV42aQZqrq6K6wCGVipFIxCnoEs4Ljhx1n+qppxG59hpEuYyewIMM1wWlqL7mlHGed8ViGdu2SCRis+bgMFE0hHXLn0lcdCEoD7V6p9EWNrUa5n3/JvGh/0f+Bz/yhRpTZKTAoVZzFpWDQ6FQIpNJYdtWyzbnRN5zVl+f3+KdrJ0pJXJwYNrnOB3vuaUjXBq6hZUrV/GXv/yj5X2++c3x+U1CCM466w2cddYbZuvUFiThHtIIHn30EX784x9w3HGvYK+99uYLXzifQmH0VWY6neZrX/s2hmHykY+8n+9855u84hUncs455426X7Oroje84S1ceeXlfPCD57Jly5aOXBU1c3Co4z7zWVTe9BZ/Tymf9wsQ+PHQpRKiVMLb5xmU3/mucY+dTYFDU/Wc1kQuuxRRKaOXLR+/qrNt1A4rMB+4H+uWP0/7+bdXgYMQgmQyHiTr+sOtxaIvXtDZrL8yKuQRuWFELueLZcZWCq1R0ywQvlAlE7RlXQYHS4tCPRcyc0IvuwDXdXnrW88mn89zySVXsGHDBt7yltdxzDHH8tGPfnK+T29STFOQzSbwPG9ilwWtif7oh8S+823E1i2Ab+evozFqxx5L8ROfaupfBpDN+u3JTjk4tPKeMx59hNRZZ0A0hm7hUi7XPU3tqBdR/Or/TPs8otEIiUSMoaH8onJwkFLS05MeF0Y4mfecfd21pM55lz8qUK9mQvgXAb29YEd8tV0+R+F/vk7thFe0fU7d4D23vdPtXnZhyy7gkkt+yCOP/Ievfe3bxOMJ9thjT17/+jfz/e9/m6OPfjGHHTb7cxczYdKICiGovOnNVM44E+uWPyM3bYRYHOf5z0etXDXp8TslcGgnuVX09SNqNVS2p+WxtGX7r2MGbC8Ch/qqqFU0ROTqn5H4749su0EIGuFE1Spy82bU0mWIUhFv992pveSYts5lvqIhQhYeYUECHn74IS699EecfPJredazDmncfuaZr+cvf/kzX/ziBVx22c+6fmBtsogKAKJRnJe+bMrHnqnAYSreczoWA2kEgW3N36LCdf3Zqhmy2AUO6bQvo24VDSEGB4mf/xlwXb/oDA8jyiXfSqi+UnJd5NatePvsQ+Hib7e1d7cYvOdC5o6wIAF7770Pf/7zHeNuNwyD73//0nk4o+kxTuDQYRHCdAUOY9Vzk80Tec94Bt6qVRgbNqBWrJj4TsoDpXBeeNQUXsHELFaBQzQaQQgRrEYrDQeOiYj84ueIfA6dToMU6J4sRCOIYglqVb8oCQG2ReH8C/DC5NaQWSAUNSwyJhM4zISpChymPdxq21RPPtlfIY30Vdt2IshNm1DLllF72bHTeCXjWUwCB9+IdFs0RK1Ww7btlmNo5l3/8q9oRjjG61gMtbQXtXIlasVK1MqV/gzSk080PU43RUOELDzCFdIiZFxERQdpJ6JiKu25ZlRPPR3zgQewb7gBhodR2SwYBqJcRuSG0dkeSh/7OHr58g68qsURUWE8/TTJv96CVS7jZrPkjjwKp3dpQ+DQMqLCcZob9ApRjyAGIRBNUlHD9tz/b+/O45sotz6A/2Yy2ZqlaSmLgrSIeBWqLLIKiqyWpYAIbgiCG1xAQGV9uVevKIqKoOIGCILgxRW3DxdkBwELIioXRRS8LbKXkjbrJJnMvH9ME5pmadqmTdKe7z/a6WTyhKWH53nOcw6pLgpIdVCFCQ7VFCnBobLLc2GpVLA/Ox9C23ZQr/8M7F8nwYgiJJUa7oGDwd97H7w33hiDT3NZsiY4MGYzjM/NA7dlM8DL1bhZUYRep4PrjuFwzJwNh1oVsYKDt9W18uwowuFpeDwAw8DbsmXAZVqeI7FCad91FMMA6enyhr/ZXBLzjKbybcGjyZ4DIB+6LK2Rxlgt4PbvB+NwQGzYCELnzqEPZAoCFP/7E3C5ITZsCKlx49h+mDI4joPJZIDVaq/xBAfm3DmwlhKIxlRI4fbKKsA5HTA+OBbMwYOQtFpI2hRIpe2/GbsNjMsNd79+sC15E2kN0+H1iiErOLAFBTD17wtIYuhEEUkCU1IC79UtUbJ5q9xfq1z2nNPpoey5BEdp3yQuaivBwVd8NdLyHHPxItSffATNxx+DPX9O7snUoAGYwkK5xp4oARwHb/Pm4B95FK677g78VzrHVbiJHiu1keCg3L0L6rVroPz+e8ArAAoOQseO4EfdD89tvaJ6hr/23AfvQ/rhB7m3FVem9QTLQjIYISl5qLZthWrTRtjuuCNsBQcxMxP82LHQLF8Gxm6XK3r4fg+8Xrlor0oFx6w5gEJBy3OkRtAMqY4zmbRQKjkUF1ti3t3TYNBBrVaVLtM4Qi7PKX47CsPDD4I9cwZgGEhKJViLxb/8IxmNkExpgCDIfXhYBZyTH4NzytSYjrUyWJaByZQKl8sVMTOtKjSrV0G7aCEYlwuSXg+J4+ReVnY7oFTBOXUa+IcejvwM3+FWQYDUuTNw8i9IEWq8sUUX4el2M6zvr4XBoAPHcSguDjFrFgSkvPwSNKtXAS6+dE9J3j+STGmwPzMP0p131vrh1oKCfCxe/BKOHDmMlBQdcnIG4pFHJkJZQZfkESNycS7EObVt2/YG9SWrLxJ9hkQBqY6rsIJDFZRdnpMkCaIohnw2Y7MiddBAsH+dlNOJFQowFotcjqYMqUGGvyIDY7UCDGD594cQ2rWPyXiroiYqOHDfH4DhkYcAUQwZQBizGQAD6zvLIHTrFvz6crXn+N9/R2r/vhA5JRCh5QRjswIKBcyHfgarVIas4FAWe+oU1J+vh+LPE5A4DkK79vAMHQrdFU1qfXnOYrFg9Oi7cNVVzTF69DgUFl7AG28sRv/+A/DEE7MivnbEiFxcd11r3HNPYK3INm2y61yDxmglekCiJbs6LpYJDizLQqdVQy144HY4YRG8YBgmbIKDasOGgGDkb6UuP0z+r9crZ82VNg+U9How5ktQf/JxXANSTSQ4qD/6CAzPQwyzByaZTGDPn4f64w8DApJ/eU6jDjjcyoqS74YK3vny96NpUSE2awbnY1P8X2u1GqTFaXnuyy8/g8Nhx/PPvwyjMRWAXCdy0aIXMWbMgwEFjUNJT09HdvYNtTFUEgN0DqkesNtdEEURKSnaKncTTSm6iLSXF0DV8mpIjZtA2aQxUu65G8zGTeCdvH8/oSz1l1/I5bl9iQqCICc1lL2PZeXlO3dpoGQYQMFBuXt31QYaQzabA0olB7VaVf2HuVxQ7dopV6EI95vAMJC0Wqi+3e0/f6XRqJGWZoRKpYTVag+otCA2agQxLR1MBY3yGBcv78GV/j44nTxEUYROF7n5YtnOrXLWpBMlJc5a3SvKy9uHjh07+4MRAPTu3Q+iKOLAgbxaGwepHRSQ6gFfgoN8PqhyHWCVSg5px49Be9utwKuvAkVFkCBBEkWodu2EcdwYSDNnApIEnS5w2Yi5cB4oe4A23PpOaXdbP5YB466dhoCRlE1wqO4SD+N0liYwRD5QLHEc4BWh9LhhYkToLMVwlVhhNluCs/7UarhGjATj9V6u4F6e2w2AgeueewMu22wOqFRKqFTB+zCJdLi1oCAfzZtnBVwzGAxo0CAjqFFmKJs3b0KvXt3Qr98tmD59Ck6cOF4zAyUxQUt29QTPC9BoBP/SWkUJDv7DrQ47pBEjALMZkkoV8K97X28izbKlcP3tb9BMnhTwbMmYGhhoFJz8+vJnXXztsn08HnizWsTgU1efw+GESiWf4alOgoOk10PSpsjNBiNgSguhpk5/AtJ3eZBEL1RqDaShQ+EaPQbea1oF3M+PHgPVpo1QHP8Dot4gN9Qr/TVmnE4wDgeETp3gGjQ44HW+FhUWSwlYloVKJVeoSLTsOavVAr0+eN/DYDDAYon8a9mjx61o3TobjRs3wZkzp7F69UpMnPgQVq78oN43wktUNEOqR2w2HpIk+VO1w5H71RjBcRycK98DCgshKZWhl5pU8nIW9/rrENzugGe7BwyU/8d3sl/BQtJoA2dKoijPGnxZTx45U891191V+5DlMMXF0Ly3EqmDBiCt3Q1I69IJujmzwP30Y1Sv91Vw0GjUUFTUtC4SjoM7dwgYtzvsTJGRJLDFxWAuXYK4YwdE0QtJoQDjdECzdg2MI++Ecu+egNdIDRrA+u5KCDd1BOviwV4qAlt0EeylIjBeAe4+fWF9e2nIpAer1Y6hQ4dg9uzZpeWG4rs8F2vTps1A//4D0LZtewwYMNjfKG/durVxHhkJhwJSPeJLcOA4DhpN8L5IqNpz3Ecfyd9kw/9RkTgOipMFcH67t/TZcnBx3TEcUno6mJIS/w9hyWCQn+X1+gOVZDTKwc7tBmMpgdAmGy5fMKsG9sRxGIcPQ8r8Z6E4dgyM2wOmpBjqjz+G8b57oHnnraiew/PyLKGiQF7hc0beBTEtDez5C5eDNAAGDBSQs9vgcskHXNPSAJ0O0GggGQyQTCYwJSXQT3kMzLlzAc8VmzWD5cOPYflgHZyPjofr3vvgnDgZJZ99DtvSZXLTvTA6deqE/fvzsHfvt3FfngvFYDDCbg8+yGu1WmE0Giv1rIyMDNx4YzscO3Y0VsMjMUYBqZ4JleAQtnMrAPZioXzyP5LSkjNiYaG/RQXDMJAyMmB79XVIBiOY4mL5cKUkQjQYAMjVBMBx8hmkS0VgHHYIbdvBtnQZEKrVeuU+KAzjH4WiIB9SqglSerq8bGZMlVOuRQkpixdD9fVXUT0uFgkOYsuWsL+0EGJ6GtgLF8BcvAjWaoXiUhFw7jzgdkPSaOQAXR7DyEHJfAma9Z+G/L7Q4SY4Z8yCfd5zcD7+BLzXXR9xPFqtBnPnzoVarcaCBQtw6lRhwnVuzczMCtorstlsKCq6iMzMrLiMidQcCkj1TNkEh+JiM+bN+xfeemsJOI6DxWILqrQgpqWHLaYZ8FCGgZSa6t9n8SU4eHrcAsvHn8B1192QVCp5lqJQQOjUEfz9Y+AaMBBCx45w5Q6B9Z1lsHz8KcQmV1T7c6q+2QRF/v8gpZqCEwlKD+TCK0CzfFn4ZIsyYpXg4LnlVlg+/gyeqdPAZmaCUangveJKOEeOlINRpJ5bpSWXVF9/XeX3BwKz5668sinGjXsYFy5cwIoVy6r13JrQtevNOHjwAKzWy6n3O3ZsBcuy6Ny5a6WedfFiIQ4f/gnXX9861sMkMUIHY+shl8uFzz9fh+XLl8PlciE3dwhmz/6/kPdq3n4TuvnPBSU0lMXwPMQrm8L83X6gdDlQr9cFVYdgrBYwFwoBrUbuUluDhxMND4yGcs+3kBpkhL+J58G43Sj5dD282dmh7xEE+cAqx4FJT4MpzVStCg7lD7f6Ordy3x+A8d57IOl1kZsSWq0Q09JRnHeg0u9dtgp72cOtPO/CAw/cg7Nnz2D16g+RlSAJJUDgwdgxYx5EYeEFLFmyGP375wQcjJ069e84d+4sPvroCwDAli2bsG/fHnTr1h0ZGQ1x5swprFmzChaLBStWrMGVVzaN0yeKLzoYSxLKd9/twauvLsTp06fQqFEjzJw5E127dg97v2vk3Uh5/TUwFiskdYig5PEAYOB8dLz/B2m4FhWSwQjJULl1/6piz56N+IMdAKBUAg4H2IuFKJ9zyBQVQfPBWqg//DfYoiIAgPeaVnCPHQvNg+PAKxSVquAQ7nCrj5SWJs/kKuiSC0GA1KBB1O/rEyl7Tq1W4/HHZ2LGjKk4duxoQgUko9GI1157G4sXv4w5c55ESooOubnD8OijEwPu83q9Ab8fV1zRFBcvFuL111+B1WqFwWBAhw6d8PDD4+ttMEoGNEOqR7Zt24Knn54DhUKBu+++D5MmTUJGRhpsNnvECg7c3r0wjhsDxuGQU719VRdKf6Dxd90F+yuLAxIffFlbNpsjqIJDbTCOvBPcj4cgpUf44e12g3E6YFn7bwidOvsvswUFMI4dAzY/X+6eqlLJn9d3eLdvX3jfW4UST3QByV97DnJR2pBVxCUJxjuGgjt8GFJ6WugHiSKYkhI4Zs4GP35CVO8d3BoifO05s/kSUlNNYCMksJDklugzJPqTV49cfXVLDBs2AqtWrcPEiVMBcFFVcBC6d0fJho3gR90PaLVgRBEMAKFDB1jfeCMoGAGAIHgDEhxqm7v/7fL/RNj/Yux2iE2aQGjb7vJFrxeGiRPA5v8PkkEv7zVpNIBWCyk1FZJWC2bLViifnVdhgkP5zq0hD7f6B8PIRVU5hZz8UZ4ogikuhpTeAK47hlfw6QMTVaLNnktLS6dgROKKZkj1nEbDwWDQgudd0bWo4HmwRRchabQVLh0xDIO0NCPcbk/M219UhCkshGng7WDMZkhp6cFLjTwPxmGHY8asgNmGcvcuGMY9AEmj8Z+xCnq2zQZGo4F0+DDMrDKoRUX55Tm73RFdpXVJgvb116B96w3AIwAqpXwOyeORl+rS02F96x0InbtEfEyiHW4liYNmSCSh8bwAj0eu4MBxURz81GggNm0W1T6GJF0+VBrVs2NIatgQtoWL5GKtl4rkWYfbLQeiS5fAOB1wDxwU1OpB9c0meVYVJhgBgJSSAslmAzZ941+K84lUe65CDAPn1GmwrlwF9+05cnV0UYKUmgr+0fEo+eyLiMEoEWrPEVIdlNRAYLPxMJl0QUkIsRAuwaE2eHreBssHH0Lz3gqoNn8DhncCDAvvtdeCH3U/XHffE5RAwJjNFaeBsyzAsPAUFpaWYnKDYRAye65K4+7eA57uPQCXS+6bpNNFrIFXPnvO4XBT51aSlCggkZi2qAjFZnOEbVFR07xt2sC+cBEcly6BvXAekkYLsXnzsJUnpPT00PX2yhJFQBLh0hug8IpITdWDZdmQ2XPVolZDqqCRHC3PkbqEluwIgNi0qAgn3gkOgBxovNddDzErK2IZJPeAgQCr8NfUC4VxOCAZDGBzcsCyDFiWhdPpim0wqgAtz5G6iAISAVC9FhXRKF/BIVF5ut0MITsbjMMRWKncx+UCIwhgRo+G7qqmcLk8cLncUKuVtRJsq5I9R0iyoCy7JLNnz24sX/42/vqrAI0aNcHo0WMxaNCQiK85e/YMRo4Mvqd162wsW7Yq4JrJpIVSyQVVWYiFcBUcEg176hQMY8dAceIEwEA+h4TS1hAsC2bAAHiWvwu7V5Q7t7IMTKbUalVwiAYtz5HqSvQsO9pDSiI///wT5s6dgcGDh2Lq1Cfxww/fY8GCZ5GSkoJevfpW+Prx4yehffuO/q9TQhQwrasJDpUhNmsGyyefQf3ROmjW/Rvs+fMAw4Dp1AnSuHGwDcqFy3V5Sc/XokKn04Ln3ZWq4BCN4MOtPM2ISJ1EASmJrF79Llq3boMZM+S6cx06dMTp06ewYsXSqAJSs2ZXITv7hoj31OUEh7AkCdz+/eB+/QVgWQjt2kFo2w78hIkQJk6CXhSg0GjAK5Ry9pwYvKjA8y5/sC0piU2wpew5Ut9QQEoSbrcbhw4dxMSJUwKu9+3bH1u3foOzZ8/giiuujMl72e0uqNUcUlK0cLncMf0BWDbBQX52fH+6Knfvgu6f/4Di+B/yBUmSg1L2DRAXL4b6lh5y9pzdAYGPfLhXDrYGqNWq8BUZokTLc6Q+oqSGJHH69CkIgoDmzbMCrmdmyoUwy/eMCeWVVxbg1ls7Y/DgfnjxxedgsZSEvK++JDgot2yBcfQoKP74HZJCAUmplPeLWBbcfw9DNSQX9q3bo86ei0WLinhkzxUU5GPatIno27cHhgy5HW+99Ro8EbIMfSRJwpo1qzB8+CD07t0d48ePw5Ej/62xcZK6j2ZIScJqtQAA9PrATUlDafVsi8US9rVKpQrDho1Aly5dodcb8OuvR/D++yvx22+/Yvny98GFqC7N8wI0GsG/tBbLJARfBQe9XhfzZ0eN56F/Yirg8fhba/iDCMvKB1EdDqgnT4Jz956IqeJlORxOqFQqpKRoKpXgEK/lOYvFgilTJuCqq5pj/vyXUVh4AW+8sRg8zwe0dwhl7drVWLlyKSZMmIyWLVth/fpP8MQTk/Heex+gadNmNTtwUidRQIojX+fLilS3XH5GRgamT5/t/7p9+5vQokVLzJw5Dbt27UCfPv3CjK/uJjio/rMBbFERJKUSYFgwDMAA8P3891U1V/zvTyj37oHnllujem5VEhziuTz35ZefweGw4/nnX4bRmApAbuWwaNGLGDPmQWRkNAz5OpfLhbVr38M999yPu+8eBQBo27Y97r13ONatWxvw542QaFFAiqMdO7bixRefq/C+Dz741D8TstsDK0H7Zk7GUG2vI+jWrTu0Wi2OHTsaNiDV5QQHZV4ewLJgypTkkYDAPS2WBUQR3P68qAMSEH2CQyJkz+Xl7UPHjp39wQgAevfuh4ULX8CBA3kYODA35OuOHDkMu92O3r0vJ9MolUr07NkLu3btqPFxk7qJAlIc5eYOQ27usKjudbvd4DgOBQX56NKlm/+6b+8oMzMr9gNE3U1wYMXAmYv81uXen2HkpTyh8kEiUoJDImXPFRTkB51jMxgMaNAgI+K+pO97ofY0z59fB5eLh1qtCX4hIRFQUkOSUKlU6NChI3bu3BZwffv2LcjKalHpDLu9e7+F0+nE9de3jnhfXUtwYBgGen0K1Ddkyx9OkkoDYYhoIIqA1wtvy2sq/T6+BIeSEjOczst7SVqtBmlpRqjVKrjdAsxmB+z2+KVyW62WoH1JQA5KkfYlrVYLVCoV1OVq7RkMBkiSBKs1cc+ZkcRFM6Qk8sADD2PKlPFYuHABevfuix9//AFbtmzCM8+8EHBfz55dkJMzCHPmPAUAWLJkMViWRZs22dDrDTh69BesWbMK113XGrfccluF71tXEhzKdm61Db0DKU8/LbekCNf3yOOBlJoK16DBVXo/u92B4cOHIyurBZYuXQaDQU+HWwmJgAJSEmnbth3mz38Jy5e/jQ0bvkTjxk0wa9Y/AtbxAXlTWizTKbVFixZYv/5TfPXVevA8j4YNG2Hw4CF46KHxITPsQknmBAeOUwS3hjClgZ04CdpXF0NyuwGl8nJ1b0mSi6syDBwzZwMhKlpEQ5KAbt26YdOmTdi2bQvuvPPOhDvcajAYg/YlAcBqtUbclzQYjHC73XC5XAGzJKvVCoZhYDAkdokakpgoICWZHj16okePnhHv2bPnYMDXgwcPw+DBw6r1vsmY4FC+c2v580SO6TMBjwDtO2/LHVp9+0osC3BKOGbNAj92XJXfX6vVYM6cOdi9ezcWLlyIjh27Q6fTV/djxVRmZlbQXpEv+zPSvqTveydPFqBVq2v91wsK8tG4cRPaPyJVQntIJGrJ1KIiqs6tLAvH3H/AvGcfnI89BnefvnD36w/H9Jkw5+2Hc+Lk8D2RIih7uLVhw0Z45JEJMJvNWLr0zWp/rljr2vVmHDx4IGDPZ8eOrWBZFp07dw37uuzsG6HT6bBjx1b/NUEQsHv3DnTt2r1Gx0zqLqr2TSpFo+FgMGjB8y7YbJFL6VQWwzBISzPC7fZU+dkhl+dqaX2sfPac0+mBw+GCxyNg7Nj7cPJkPlasWINWrf5WK+OJhsViwejRd+Gqq5pjzJgHUVh4AUuWLEb//jkBB2OnTv07zp07i48++sJ/bc2aVXjvvWWYMOExtGx5DT7//BMcOLCfDsYmMKr2TeqURE1wqGh5rqZFOtzKcRyefHIWpk2biGPHfkuogGQ0GvHaa29j8eKXMWfOk0hJ0SE3dxgefXRiwH1erzfokO/99z8AQMKHH65FcbEZ11xzLRYtWkLBiFQZzZBIpXEcA5NJB6/XWyNJCCaT/K+4aJ9dNnvObndWu7BpZQQfbnWFzZ6zWEqg1xvARlmGiJBYS/QZEv3NIJXmS3DgOA4aTeiU6eqw2Rylz1ZHvI/jFDCZDNDptHC5PDCbLbUWjKrSudVoTK2zwejQoYPo0aNjyCoNmzdvQo8eHXHkyOE4jIwkk7r5t4PUuHgmOPgOt5pMRkgSUFJihd3uqLW9okQ73JoI2re/CY0aNcaWLRuDvrdly0Y0bdoM2dk3xmFkJJlQQCJVEq8KDlFlz9WQeLSGSBYMw+D22wdi3749sNkun2sym804cCAP/fsPiOPoSLKggESqjOcFeDxyggPHKSp+QSX4Ehx8z0625bn6KCdnENxuN3buvJwKvn37Zni9Xtx++8A4jowkCwpIpFpsNh6SJEGvj/0siefdEAQBRqOelueSQGZmFq6/vjU2b97kv7Z58ya0aXMDmjW7Ko4jI8mCAhKplppMcNBo1GBZFizLlhYqpeW5RJeTMwg//XQIFy6cx+nTp/DLL/+l5ToSNQpIpNpineBQfnmO511QqZQxqeBQEVqeq54+fW4Hy7LYuvUbbN68ERzHoU+f/vEeFkkSdDCWVJsvwcFg0EKnS6lylYVwh1sZhoFKpYROp415dYiy4tm5ta4wmUzo2vVmfPPNRrjdLnTp0g0mkynewyJJgmZIJCaqm+AQKXuufIJDrNHyXGzl5AzCiRN/4K+/TqJ/f0pmINGjGRKJmaq0qIi29lxNtKhIpM6tdUn37rfCYDBCkkT06BF963dCKCCRmKlMi4qq1J6LZYsKWp6rOQzDQKFQoHv324I6yhISCQUkElN2uwtqNYeUFC1cLnfI2UbZ2nNWqz3q80RlKzjIz678VCa49lz8Orfu2bMby5e/jb/+KkCjRk0wevRYDBo0JOJrzp49g5Ejg+9p3Toby5atqqGRVs633+5EcbEZOTmD4j0UkmQoIJGYipTgEIvWEHa7s0oJDom2PPfzzz9h7twZGDx4KKZOfRI//PA9Fix4FikpKejVq2+Frx8/fhLat+/o/zqlil1tY+mXX47gxIk/sGrVu7j22r+hffub4j0kkmQoIJGYK9+i4vfff0dGRgNcfXWLareGqEqLikRcnlu9+l20bt0GM2b8HwCgQ4eOOH36FFasWBpVQGrW7CpkZ99Q08OslC+++BSbN2/ENddci7lzn473cEgSoiw7UiNsNh6FhYV4/vnnMHr0KDz77LyY1Z7zVXCoqDpEombPud1uHDp0MCjw9O3bH/n5/8PZs2fiNLLqmTv3X9i1az9WrFiDq6++Jt7DIUmIAhKJOUEQsG7dOuTk5ODLL79EmzZtMGHCxJjWnovUoiLRD7eePn0KgiCgefOsgOuZmS0AAAUF+RU+45VXFuDWWztj8OB+ePHF52CxlNTASAmpXbRkR2Lq5Ml8/POfc3DixB8wGlPxzDPP4M4774TFYovpfo0vwcFsLoIoMtDpdAASc3muPKvVAgDQ6wObpRkMRgByW/FwlEoVhg0bgS5dukKvN+DXX4/g/fdX4rfffsXy5e+D4+ivNEle9KeXxNT27Vvx55/HMWTIHRg/fhIaN86AUqmsVgWHcOx2J+699140bNgQK1a8B6PRELfsOZvNhqKiixXed+WVTav1PhkZGZg+fbb/6/btb0KLFi0xc+Y07Nq1A3369KvW8wmJJwpIJKZGjx6H3NxhaNAgA0BwgkMsi6NKkoTu3Xvgiy8+x8aNG3DffffFLXtux46tePHF5yq874MPPvXPhOx2W8D3fDMno9FYqffu1q07tFotjh07SgGJJDUKSCSmFAqFPxj5VKWCQzS0Wg1mzpyB7du3YfHiV9Gly61ITTXF7PmVkZs7DLm5w6K61+12g+M4FBTko0uXbv7rvr2jzMys2A+QkCRASQ2kxsW6RUXZ7DmTKQ1///tkWK0WvPnmazEYbc1TqVTo0KEjdu7cFnB9+/YtyMpqgSuuuLJSz9u791s4nU5cf33rWA6TkFpHMyRSK6Kp4FCRcIdbBw4cis8/X4///OdrDBkyPOHO54TywAMPY8qU8Vi4cAF69+6LH3/8AVu2bMIzz7wQcF/Pnl2QkzMIc+Y8BQBYsmQxWJZFmzbZ0OsNOHr0F6xZswrXXdcat9xyWxw+CSGxQwGJ1IrqtqiIlD2nUCjw5JOz8dhjj+L48d+TIiC1bdsO8+e/hOXL38aGDV+iceMmmDXrH+jdO/BsktfrhShezhJs0aIF1q//FF99tR48z6Nhw0YYPHgIHnpoPGXYkaTHSFHWbiksjN3aP6m/TCYtlEoOxcWWqBIcgmvPucJmz9lsNqSkpIBlaSWakFAaNjRUfFMc0d9cUqtsNh6SJFVYZaEqh1v1ej0FI0KSGM3xSYDvv8/Dhg1f49dfj+DMmdMYPnwknnhiVlSvtdlsWLJkEXbv3glBENClS1dMmzYTGRmXs+6iaVGRDIdbCSGxR/+cJAHy8r7D8eN/oF27DkGVBCry1FNz8P33+zF9+hw8/fSzOHmyANOnT4EgBM5q7HYXRFFESooWDHP5eqLWniOE1A6aIZEAkyZNxWOPPQ4AOHToYNSvO3LkMA4c+A6LFr2Bzp27AgCaN8/EqFEjgyoIlE9wcDj4hGoNQQiJD5ohkQBV3YPJy9sHvd6ATp26+K81b56FVq2uRV7e3qD7eV6AxyNXcEhLM0KtVsHtFmA2O2C3UzAipD6igERioqAgH82bZ4IpuwYHuYJ1uOrVvgQHSQItzxFCaMmOxIbVagm552QwGMJWrxYECcXFDggCBSFCCAWkOq8yVaiVSmUtjCgQBSNCiA8FpDquMlWoq1PU02Aw4sKF80HXrVZrpatXE0LqJwpIdVxlqlBXR2ZmFg4ePABJkgL2kQoK8tGyJbWzJoRUjJIaSEx07XozrFYLDh484L928mQB/vjjGLp27R7HkRFCkgXNkEiAc+fO4ujRXwAAPM/j9OnT2LFjKwCgV6/LhT/LV6HOzr4RnTt3wwsvzMPkyY9DpVJh+fK30LJlK/Ts2av2PwhqvuoEISS2KCCRAIcOHcTzzz/j/3r//n3Yv38fAGDPnssHZctXoQaAefNewJIli/DSS/Ph9XrRuXMXPP74zLhVoS5bdSJcpl84Tz01B/n5f2L69DlQq1VYtuwtTJ8+Be+++z5V1SakhlC1b1JniaLoP+g7YkQubr65R1QzpCNHDmPChAcDqk6cPJmPUaNG4l//ep7ahJOkRdW+CYmT2qo6QQiJDQpIhJRTlaoThJDqo4BESDlVqTpBCKk+2p0lSSPRq04QQqqHAhJJGlR1gpC6jQISSRpUdYKQuo32kAgph6pOEBIfNEMidVZdqjpBSH1AAYnUWXWp6gQh9QFVaiCEkHqCKjUQQgghUaCARAghJCFQQCKEEJIQKCARQghJCBSQCCGEJAQKSIQQQhICBSRCCCEJgQISIYSQhEABiRBCSEKggEQIISQhUEAihBCSECggEUIISQgUkAghhCQECkiEEEISAgUkQgghCSHqfkiEEEJITaIZEiGEkIRAAYkQQkhCoIBECCEkIVBAIoQQkhAoIBFCCEkIFJAIIYQkBApIhBBCEgIFJEIIIQmBAhIhhJCE8P9AHe666RG/cAAAAABJRU5ErkJggg=="}}]}}}, "version_major": 2, "version_minor": 0}
 </script>
 
 
diff --git a/ml-fundamentals/build-a-web-app-to-use-a-machine-learning-model.html b/ml-fundamentals/build-a-web-app-to-use-a-machine-learning-model.html
index ee1424a85d..1f0a925895 100644
--- a/ml-fundamentals/build-a-web-app-to-use-a-machine-learning-model.html
+++ b/ml-fundamentals/build-a-web-app-to-use-a-machine-learning-model.html
@@ -2054,7 +2054,7 @@ <h2><span class="section-number">13.4. </span>Exercise - build your model<a clas
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>              precision    recall  f1-score   support
 
            0       1.00      1.00      1.00        41
-           1       0.86      0.31      0.45       250
+           1       0.85      0.31      0.45       250
            2       1.00      1.00      1.00         8
            3       1.00      1.00      1.00       131
            4       0.96      1.00      0.98      4743
@@ -2064,7 +2064,7 @@ <h2><span class="section-number">13.4. </span>Exercise - build your model<a clas
 weighted avg       0.96      0.96      0.96      5173
 
 Predicted labels:  [4 4 4 ... 3 4 4]
-Accuracy:  0.9640440750048328
+Accuracy:  0.9638507635801276
 </pre></div>
 </div>
 </div>
diff --git a/ml-fundamentals/classification/more-classifiers.html b/ml-fundamentals/classification/more-classifiers.html
index 5112f1f320..ac5585357a 100644
--- a/ml-fundamentals/classification/more-classifiers.html
+++ b/ml-fundamentals/classification/more-classifiers.html
@@ -2220,7 +2220,7 @@ <h2><span class="section-number">14.2.4. </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 0.8065054211843202
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Accuracy is 0.8023352793994996
 </pre></div>
 </div>
 </div>
@@ -2243,10 +2243,10 @@ <h2><span class="section-number">14.2.4. </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;barley&#39;, &#39;bean&#39;, &#39;buckwheat&#39;, &#39;carrot&#39;, &#39;katsuobushi&#39;, &#39;scallion&#39;,
-       &#39;seaweed&#39;, &#39;soy_sauce&#39;, &#39;soybean&#39;],
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>ingredients: Index([&#39;bell_pepper&#39;, &#39;buckwheat&#39;, &#39;cane_molasses&#39;, &#39;carrot&#39;, &#39;cilantro&#39;,
+       &#39;lime&#39;, &#39;peanut&#39;, &#39;sesame_oil&#39;, &#39;soy_sauce&#39;, &#39;vinegar&#39;],
       dtype=&#39;object&#39;)
-cuisine: japanese
+cuisine: thai
 </pre></div>
 </div>
 </div>
@@ -2296,24 +2296,24 @@ <h2><span class="section-number">14.2.4. </span>Exercise - apply logistic regres
   </thead>
   <tbody>
     <tr>
-      <th>japanese</th>
-      <td>0.991627</td>
-    </tr>
-    <tr>
-      <th>korean</th>
-      <td>0.004940</td>
+      <th>thai</th>
+      <td>0.442010</td>
     </tr>
     <tr>
       <th>chinese</th>
-      <td>0.002909</td>
+      <td>0.312328</td>
     </tr>
     <tr>
-      <th>thai</th>
-      <td>0.000457</td>
+      <th>japanese</th>
+      <td>0.240609</td>
     </tr>
     <tr>
       <th>indian</th>
-      <td>0.000067</td>
+      <td>0.004452</td>
+    </tr>
+    <tr>
+      <th>korean</th>
+      <td>0.000601</td>
     </tr>
   </tbody>
 </table>
@@ -2334,15 +2334,15 @@ <h2><span class="section-number">14.2.4. </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       0.76      0.75      0.76       236
-      indian       0.88      0.87      0.87       233
-    japanese       0.74      0.74      0.74       227
-      korean       0.84      0.83      0.83       259
-        thai       0.80      0.84      0.82       244
+     chinese       0.71      0.69      0.70       238
+      indian       0.92      0.91      0.91       216
+    japanese       0.79      0.78      0.79       261
+      korean       0.83      0.79      0.81       247
+        thai       0.77      0.86      0.81       237
 
-    accuracy                           0.81      1199
-   macro avg       0.81      0.81      0.81      1199
-weighted avg       0.81      0.81      0.81      1199
+    accuracy                           0.80      1199
+   macro avg       0.81      0.80      0.80      1199
+weighted avg       0.80      0.80      0.80      1199
 </pre></div>
 </div>
 </div>
diff --git a/ml-fundamentals/classification/yet-other-classifiers.html b/ml-fundamentals/classification/yet-other-classifiers.html
index 5d4f35d018..9481700872 100644
--- a/ml-fundamentals/classification/yet-other-classifiers.html
+++ b/ml-fundamentals/classification/yet-other-classifiers.html
@@ -1875,18 +1875,18 @@ <h3><span class="section-number">14.3.4.1. </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: 79.9% 
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Accuracy (train) for Linear SVC: 80.1% 
               precision    recall  f1-score   support
 
-     chinese       0.69      0.75      0.72       230
-      indian       0.91      0.91      0.91       252
-    japanese       0.77      0.77      0.77       222
-      korean       0.84      0.74      0.78       244
-        thai       0.79      0.82      0.81       251
+     chinese       0.66      0.74      0.70       229
+      indian       0.88      0.92      0.90       232
+    japanese       0.83      0.78      0.81       253
+      korean       0.87      0.76      0.81       236
+        thai       0.78      0.80      0.79       249
 
     accuracy                           0.80      1199
    macro avg       0.80      0.80      0.80      1199
-weighted avg       0.80      0.80      0.80      1199
+weighted avg       0.81      0.80      0.80      1199
 </pre></div>
 </div>
 </div>
@@ -1910,31 +1910,33 @@ <h3><span class="section-number">14.3.5.1. </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: 79.9% 
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Accuracy (train) for Linear SVC: 80.1% 
               precision    recall  f1-score   support
 
-     chinese       0.69      0.75      0.72       230
-      indian       0.91      0.91      0.91       252
-    japanese       0.77      0.77      0.77       222
-      korean       0.84      0.74      0.78       244
-        thai       0.79      0.82      0.81       251
+     chinese       0.66      0.74      0.70       229
+      indian       0.88      0.92      0.90       232
+    japanese       0.83      0.78      0.81       253
+      korean       0.87      0.76      0.81       236
+        thai       0.78      0.80      0.79       249
 
     accuracy                           0.80      1199
    macro avg       0.80      0.80      0.80      1199
-weighted avg       0.80      0.80      0.80      1199
+weighted avg       0.81      0.80      0.80      1199
 
-Accuracy (train) for KNN classifier: 72.6% 
-              precision    recall  f1-score   support
+Accuracy (train) for KNN classifier: 74.6% 
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>              precision    recall  f1-score   support
 
-     chinese       0.63      0.71      0.67       230
-      indian       0.87      0.79      0.83       252
-    japanese       0.63      0.86      0.73       222
-      korean       0.92      0.54      0.68       244
-        thai       0.70      0.73      0.72       251
+     chinese       0.67      0.73      0.70       229
+      indian       0.85      0.81      0.83       232
+    japanese       0.66      0.85      0.74       253
+      korean       0.89      0.58      0.71       236
+        thai       0.74      0.75      0.74       249
 
-    accuracy                           0.73      1199
-   macro avg       0.75      0.73      0.72      1199
-weighted avg       0.75      0.73      0.73      1199
+    accuracy                           0.75      1199
+   macro avg       0.76      0.74      0.75      1199
+weighted avg       0.76      0.75      0.75      1199
 </pre></div>
 </div>
 </div>
@@ -1962,41 +1964,41 @@ <h3><span class="section-number">14.3.6.1. </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: 79.9% 
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Accuracy (train) for Linear SVC: 80.1% 
               precision    recall  f1-score   support
 
-     chinese       0.69      0.75      0.72       230
-      indian       0.91      0.91      0.91       252
-    japanese       0.77      0.77      0.77       222
-      korean       0.84      0.74      0.78       244
-        thai       0.79      0.82      0.81       251
+     chinese       0.66      0.74      0.70       229
+      indian       0.88      0.92      0.90       232
+    japanese       0.83      0.78      0.81       253
+      korean       0.87      0.76      0.81       236
+        thai       0.78      0.80      0.79       249
 
     accuracy                           0.80      1199
    macro avg       0.80      0.80      0.80      1199
-weighted avg       0.80      0.80      0.80      1199
+weighted avg       0.81      0.80      0.80      1199
 
-Accuracy (train) for KNN classifier: 72.6% 
+Accuracy (train) for KNN classifier: 74.6% 
               precision    recall  f1-score   support
 
-     chinese       0.63      0.71      0.67       230
-      indian       0.87      0.79      0.83       252
-    japanese       0.63      0.86      0.73       222
-      korean       0.92      0.54      0.68       244
-        thai       0.70      0.73      0.72       251
+     chinese       0.67      0.73      0.70       229
+      indian       0.85      0.81      0.83       232
+    japanese       0.66      0.85      0.74       253
+      korean       0.89      0.58      0.71       236
+        thai       0.74      0.75      0.74       249
 
-    accuracy                           0.73      1199
-   macro avg       0.75      0.73      0.72      1199
-weighted avg       0.75      0.73      0.73      1199
+    accuracy                           0.75      1199
+   macro avg       0.76      0.74      0.75      1199
+weighted avg       0.76      0.75      0.75      1199
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Accuracy (train) for SVC: 83.9% 
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Accuracy (train) for SVC: 83.8% 
               precision    recall  f1-score   support
 
-     chinese       0.76      0.79      0.78       230
-      indian       0.91      0.91      0.91       252
-    japanese       0.83      0.82      0.83       222
-      korean       0.91      0.80      0.85       244
-        thai       0.79      0.87      0.83       251
+     chinese       0.74      0.76      0.75       229
+      indian       0.91      0.93      0.92       232
+    japanese       0.86      0.82      0.84       253
+      korean       0.90      0.81      0.85       236
+        thai       0.79      0.87      0.83       249
 
     accuracy                           0.84      1199
    macro avg       0.84      0.84      0.84      1199
@@ -2025,73 +2027,73 @@ <h2><span class="section-number">14.3.7. </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: 79.9% 
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Accuracy (train) for Linear SVC: 80.1% 
               precision    recall  f1-score   support
 
-     chinese       0.69      0.75      0.72       230
-      indian       0.91      0.91      0.91       252
-    japanese       0.77      0.77      0.77       222
-      korean       0.84      0.74      0.78       244
-        thai       0.79      0.82      0.81       251
+     chinese       0.66      0.74      0.70       229
+      indian       0.88      0.92      0.90       232
+    japanese       0.83      0.78      0.81       253
+      korean       0.87      0.76      0.81       236
+        thai       0.78      0.80      0.79       249
 
     accuracy                           0.80      1199
    macro avg       0.80      0.80      0.80      1199
-weighted avg       0.80      0.80      0.80      1199
+weighted avg       0.81      0.80      0.80      1199
 
-Accuracy (train) for KNN classifier: 72.6% 
+Accuracy (train) for KNN classifier: 74.6% 
               precision    recall  f1-score   support
 
-     chinese       0.63      0.71      0.67       230
-      indian       0.87      0.79      0.83       252
-    japanese       0.63      0.86      0.73       222
-      korean       0.92      0.54      0.68       244
-        thai       0.70      0.73      0.72       251
+     chinese       0.67      0.73      0.70       229
+      indian       0.85      0.81      0.83       232
+    japanese       0.66      0.85      0.74       253
+      korean       0.89      0.58      0.71       236
+        thai       0.74      0.75      0.74       249
 
-    accuracy                           0.73      1199
-   macro avg       0.75      0.73      0.72      1199
-weighted avg       0.75      0.73      0.73      1199
+    accuracy                           0.75      1199
+   macro avg       0.76      0.74      0.75      1199
+weighted avg       0.76      0.75      0.75      1199
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Accuracy (train) for SVC: 83.9% 
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Accuracy (train) for SVC: 83.8% 
               precision    recall  f1-score   support
 
-     chinese       0.76      0.79      0.78       230
-      indian       0.91      0.91      0.91       252
-    japanese       0.83      0.82      0.83       222
-      korean       0.91      0.80      0.85       244
-        thai       0.79      0.87      0.83       251
+     chinese       0.74      0.76      0.75       229
+      indian       0.91      0.93      0.92       232
+    japanese       0.86      0.82      0.84       253
+      korean       0.90      0.81      0.85       236
+        thai       0.79      0.87      0.83       249
 
     accuracy                           0.84      1199
    macro avg       0.84      0.84      0.84      1199
 weighted avg       0.84      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 RFST: 85.2% 
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Accuracy (train) for RFST: 85.9% 
               precision    recall  f1-score   support
 
-     chinese       0.80      0.82      0.81       230
-      indian       0.93      0.90      0.92       252
-    japanese       0.85      0.83      0.84       222
-      korean       0.87      0.81      0.84       244
-        thai       0.81      0.89      0.85       251
+     chinese       0.82      0.80      0.81       229
+      indian       0.92      0.94      0.93       232
+    japanese       0.85      0.86      0.85       253
+      korean       0.89      0.82      0.86       236
+        thai       0.82      0.87      0.84       249
 
-    accuracy                           0.85      1199
-   macro avg       0.85      0.85      0.85      1199
-weighted avg       0.85      0.85      0.85      1199
+    accuracy                           0.86      1199
+   macro avg       0.86      0.86      0.86      1199
+weighted avg       0.86      0.86      0.86      1199
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Accuracy (train) for ADA: 71.5% 
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Accuracy (train) for ADA: 70.2% 
               precision    recall  f1-score   support
 
-     chinese       0.67      0.49      0.56       230
-      indian       0.89      0.81      0.85       252
-    japanese       0.64      0.68      0.66       222
-      korean       0.66      0.79      0.72       244
-        thai       0.72      0.78      0.75       251
+     chinese       0.60      0.54      0.57       229
+      indian       0.87      0.87      0.87       232
+    japanese       0.68      0.57      0.62       253
+      korean       0.66      0.76      0.70       236
+        thai       0.70      0.78      0.74       249
 
-    accuracy                           0.71      1199
-   macro avg       0.72      0.71      0.71      1199
-weighted avg       0.72      0.71      0.71      1199
+    accuracy                           0.70      1199
+   macro avg       0.70      0.70      0.70      1199
+weighted avg       0.70      0.70      0.70      1199
 </pre></div>
 </div>
 </div>
diff --git a/ml-fundamentals/parameter-optimization/gradient-descent.html b/ml-fundamentals/parameter-optimization/gradient-descent.html
index 749b1c3705..16d24a04e2 100644
--- a/ml-fundamentals/parameter-optimization/gradient-descent.html
+++ b/ml-fundamentals/parameter-optimization/gradient-descent.html
@@ -2065,19 +2065,19 @@ <h3><span class="section-number">15.2.5.1. </span>Linear regression With gradien
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>{&#39;lr&#39;: 0.0003, &#39;n_iters&#39;: 5000, &#39;weights&#39;: array([ 0.45351552,  0.21147914,  0.01485353,  0.08785205,  0.14878446,
-        0.12828472,  0.12230661, -0.00201684]), &#39;bias&#39;: 0.02192744125483242}
-0    -1.952323
-1    -1.731284
-2    -2.137262
-3    -1.628383
-4    -2.072147
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>{&#39;lr&#39;: 0.0003, &#39;n_iters&#39;: 5000, &#39;weights&#39;: array([ 4.52878699e-01,  2.15398482e-01, -2.58152276e-03,  9.64105213e-02,
+        1.33677087e-01,  1.29817624e-01,  1.19847164e-01, -4.44435098e-04]), &#39;bias&#39;: 0.020373585739686062}
+0    -1.061494
+1    -0.703395
+2    -0.856630
+3    -0.600482
+4    -0.973848
         ...   
-92    0.318140
-93    0.371237
-94    1.132971
-95    1.159795
-96    0.760089
+92    1.439704
+93    1.105060
+94    2.054375
+95    2.224784
+96    1.926531
 Name: lpsa, Length: 97, dtype: float64
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diff --git a/ml-fundamentals/regression/linear-and-polynomial-regression.html b/ml-fundamentals/regression/linear-and-polynomial-regression.html
index b058901c84..f8d4246adb 100644
--- a/ml-fundamentals/regression/linear-and-polynomial-regression.html
+++ b/ml-fundamentals/regression/linear-and-polynomial-regression.html
@@ -2262,7 +2262,7 @@ <h2><span class="section-number">12.3.3. </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 0x7f740b095b80&gt;
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;matplotlib.collections.PathCollection at 0x7fc3d7815b50&gt;
 </pre></div>
 </div>
 <img alt="../../_images/linear-and-polynomial-regression_12_1.png" src="../../_images/linear-and-polynomial-regression_12_1.png" />
@@ -2448,7 +2448,7 @@ <h2><span class="section-number">12.3.5. </span>Simple Linear Regression<a class
 </div>
 </div>
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-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[&lt;matplotlib.lines.Line2D at 0x7f73fa378c70&gt;]
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[&lt;matplotlib.lines.Line2D at 0x7fc3c6b07e20&gt;]
 </pre></div>
 </div>
 <img alt="../../_images/linear-and-polynomial-regression_34_1.png" src="../../_images/linear-and-polynomial-regression_34_1.png" />
@@ -2694,12 +2694,7 @@ <h2><span class="section-number">12.3.8. </span>Putting it all together<a class=
 </div>
 <div class="cell_output docutils container">
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Mean error: 2.23 (8.28%)
-</pre></div>
-</div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>
-</pre></div>
-</div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Model determination:  0.9652659492293556
+Model determination:  0.9652659492293556
 </pre></div>
 </div>
 </div>
diff --git a/ml-fundamentals/regression/logistic-regression.html b/ml-fundamentals/regression/logistic-regression.html
index ba7990a36d..fd812b6dfa 100644
--- a/ml-fundamentals/regression/logistic-regression.html
+++ b/ml-fundamentals/regression/logistic-regression.html
@@ -2141,7 +2141,7 @@ <h3><span class="section-number">12.4.5.1. </span>Visualization - side-by-side g
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-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;seaborn.axisgrid.PairGrid at 0x7f93dee0aac0&gt;
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;seaborn.axisgrid.PairGrid at 0x7f6fd09218b0&gt;
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 </div>
 <img alt="../../_images/logistic-regression_19_1.png" src="../../_images/logistic-regression_19_1.png" />
@@ -2200,7 +2200,7 @@ <h3><span class="section-number">12.4.5.3. </span>Violin plot<a class="headerlin
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;seaborn.axisgrid.FacetGrid at 0x7f93cbc52d30&gt;
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;seaborn.axisgrid.FacetGrid at 0x7f6fbe4fcd60&gt;
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 </div>
 <img alt="../../_images/logistic-regression_24_1.png" src="../../_images/logistic-regression_24_1.png" />
diff --git a/searchindex.js b/searchindex.js
index 8f432eb06c..f80743e6ac 100644
--- a/searchindex.js
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7,138,139,141,142,143,144,146,147,148,149,150,152,153,157,158,160,161,162,165,168,171,174,175,177,178,179,180,183,184,186,187,189,191,194,195,198,199,200,201,202],AND:[92,93,108,119,121,178,179,180],AS:[22,25,45,47,48,92,93,162,178,179,180],And:[31,32,40,41,43,48,49,50,52,56,58,62,68,74,76,78,80,92,100,102,104,112,116,119,126,131,132,135,138,141,142,143,144,145,149,158,160,170,175,179,183,187,191,194,201],As:[1,3,7,8,33,34,36,40,41,43,47,48,49,50,51,52,53,54,56,57,58,59,60,61,68,74,78,80,82,83,99,100,106,109,112,114,116,117,119,122,124,134,135,136,141,142,143,149,152,153,157,158,159,160,161,165,168,169,173,174,175,178,179,180,184,186,189,198,200,201],At:[28,40,48,50,56,59,68,75,80,106,116,119,124,132,142,143,145,149,153,154,159,168,175,177,178,179,186,198,199],BE:[92,93,178,179,180],BUT:[92,93,178,179,180],BY:[101,143],Be:[85,91,104,108,119,162],Being:[43,62,101,104,122],But:[33,38,40,43,48,49,50,52,53,56,57,58,59,61,64,68,74,80,100,104,109,112,124,129,131,139,141,142,143,148,152,153,154,156,158,159,160,161,168,174,177,178,179,180,191,195],By:[7,18,29,40,41,46,49,52,53,54,57,59,68,74,76,77,78,80,99,101,109,114,117,119,122,124,130,139,141,142,144,147,149,150,157,160,165,168,170,173,174,178,189],FOR:[92,93,178,179,180],For:[7,19,29,30,31,32,35,36,38,39,40,41,43,45,46,47,48,49,50,51,54,59,60,61,64,66,67,68,69,71,72,74,75,84,85,86,88,89,90,91,100,101,102,111,112,113,114,116,117,119,120,121,122,126,131,132,135,137,139,141,142,143,144,145,147,148,149,150,152,153,154,157,158,159,160,161,162,165,166,167,168,169,170,173,174,175,177,178,179,180,181,191,195,198,199,201],IN:[25,82,92,93,178,179,180],IS:[22,45,47,48,54,92,93,97,147,178,179,180],IT:[54,99,142],If:[1,7,14,16,18,29,33,34,35,39,40,41,43,45,48,49,50,51,52,53,54,57,58,59,60,62,64,66,68,69,74,76,77,80,82,92,93,95,100,101,103,104,106,108,109,110,111,112,113,114,116,117,118,119,120,121,122,124,126,130,131,132,134,136,138,141,142,143,145,147,149,152,153,154,156,158,159,160,161,162,165,167,168,170,173,174,175,177,178,179,180,187,193,195,198,200,201,202],In:[1,3,7,8,9,11,12,13,14,16,18,19,21,24,28,29,30,31,32,33,36,39,40,41,43,45,46,47,48,49,50,52,53,54,57,58,59,60,61,62,64,66,68,69,71,74,75,76,77,78,80,82,83,88,89,90,92,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,124,125,126,127,128,130,131,132,133,134,135,137,139,141,142,143,144,145,147,148,149,150,152,153,154,155,156,157,158,159,160,161,162,164,165,166,167,168,170,171,173,174,175,176,177,178,179,180,181,184,186,187,188,193,195,196,197,198,199,201,202],Is:[50,93,97,99,103,106,111,112,113,130,142,143,147,162,168,174,175,202],It:[0,1,2,3,4,5,7,8,9,10,11,13,14,15,16,17,18,19,20,23,24,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,46,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,71,72,74,76,77,78,80,82,83,84,85,86,88,89,90,91,99,100,101,102,103,104,106,108,109,110,111,112,113,114,116,117,118,119,120,121,122,127,128,129,130,131,132,133,134,135,136,137,138,139,141,142,143,144,145,146,147,148,149,150,152,153,154,155,156,157,158,159,160,161,162,165,166,167,168,170,171,173,175,177,178,179,180,182,183,189,193,194,195,196,198,199,201],Its:[131,152,157,174],NEAR:[61,74],NO:[92,93,178,179,180],NOT:[82,92,93,119,133,178,179,180],Near:[109,185],No:[7,20,29,33,50,54,56,64,82,88,93,98,100,101,110,132,149,158,160,166,170,178,179,182,185],Not:[7,40,43,49,52,54,56,68,80,101,111,117,118,136,153,166,173,174,179,194,201],OF:[22,45,47,48,92,93,133,178,179,180],ON:187,ONE:7,OR:[22,45,47,48,92,93,119,178,179,180],Of:[50,101,102,104,114,160,180,181],On:[49,50,52,57,58,59,60,61,66,68,74,80,101,104,106,143,149,152,153,156,157,158,160,161,162,168,173,177,178,184,194],One:[1,7,11,28,40,43,49,50,52,53,54,55,57,58,59,66,83,98,103,104,106,108,110,114,116,119,127,131,137,143,148,150,152,158,161,168,173,175,178,179,180,184,185,186,191,193,198,201],Or:[32,40,50,58,74,102,104,119,131,132,143,147,150,168,178,179,194,198,201],Such:[1,7,30,40,43,49,50,54,116,143,144,173,178,200],THAT:82,THE:[92,93,178,179,180],TO:[54,92,93,138,178,179,180],That:[31,32,40,43,48,49,50,52,57,61,62,68,74,80,104,109,116,119,124,132,148,150,153,154,160,162,168,170,174,179,180,198],The:[0,3,5,6,7,8,12,13,14,15,16,18,19,24,25,26,28,29,30,31,32,33,34,35,37,39,40,41,45,46,47,48,49,50,52,54,55,56,57,58,59,60,61,62,63,64,65,66,68,71,74,76,78,80,83,84,90,91,92,93,99,102,103,104,106,108,109,110,112,113,114,115,116,118,119,120,121,122,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,147,148,149,150,152,153,154,155,156,157,158,161,162,165,166,168,169,170,171,173,174,180,181,183,184,185,189,190,192,193,194,195,196,197,198,199,202],Then:[7,31,45,47,50,56,66,77,82,101,104,116,119,127,130,136,138,143,144,145,148,149,150,152,153,157,161,162,173,177,178,179,180,194],There:[0,1,3,7,18,28,29,30,32,34,36,37,39,40,41,43,46,47,49,50,51,54,57,59,60,62,64,68,72,74,80,85,100,101,104,106,108,109,110,113,114,116,117,118,119,120,124,126,127,128,131,134,136,138,139,141,142,143,144,145,147,148,150,152,153,156,160,161,162,164,165,167,168,170,173,174,176,177,178,179,180,198,199,200,201],These:[7,30,31,39,41,45,49,54,56,59,60,74,75,77,78,99,101,104,106,109,112,113,118,119,121,122,134,142,148,152,153,157,159,160,161,178,180,182,184,186,187,189,200],To:[0,1,7,14,18,22,29,33,34,36,40,41,45,46,48,49,50,52,54,58,61,62,66,68,74,75,76,82,92,99,100,101,102,104,109,110,111,112,114,116,117,119,120,122,124,129,130,131,134,135,136,139,140,141,142,143,144,145,149,150,153,154,156,157,158,160,161,162,165,168,170,173,174,175,177,178,179,180,184,185,187,190,193,194,198,200,201,202],WITH:[92,93,178,179,180],Will:[145,174,180],With:[7,40,41,43,47,50,56,60,61,62,84,99,103,104,108,109,111,112,113,114,118,119,121,122,124,131,142,143,150,152,153,158,160,161,168,171,178,187,194,199],_0:153,_1:150,_2:150,_:[18,29,31,33,37,41,51,56,81,126,127,130,132,133,134,135,136,138,139,150,153,160,170,178,179,189,195,196,201],____:[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__:178,__annotations__:[178,200],__builtins__:178,__cached__:178,__call__:[63,65,128,130,135],__class__:[37,178],__dict__:[170,196,197],__doc__:[178,200],__eq__:92,__file__:178,__finalize__:[120,121],__future__:37,__get__:120,__getitem__:[121,122],__init__:[29,30,31,33,35,36,37,43,55,63,65,81,82,94,120,128,130,132,133,134,135,136,137,154,170,178,195,196,200],__iter__:[33,133],__len__:33,__loader__:178,__main__:[35,132,158,161,162],__mul__:92,__name__:[35,37,132,161,162,178],__operators__:136,__package__:178,__repr__:55,__spec__:178,__str__:92,__sub__:92,__truediv__:92,__version__:[41,132,160,200],_aspp:135,_attach:[118,187],_bin:54,_branch:135,_build_model:35,_bunch:[57,58],_caller:120,_check_indexing_error:122,_concaten:120,_consolidate_inplac:121,_constructor:121,_conv_block:135,_conv_bn_relu:135,_conv_relu:135,_data:121,_decor:120,_deeplabv3:135,_deprecate_mismatched_index:121,_deprecated_arg:121,_engin:122,_etag:[118,187],_fcn_16:135,_fcn_32:135,_fcn_8:135,_format_argument_list:120,_fuse_bn_tensor:134,_get_axi:121,_get_block_manager_axi:121,_get_comb_axi:120,_get_concat_axi:120,_get_join_info:120,_get_list_axi:121,_get_new_ax:120,_get_result_dim:120,_get_slice_axi:121,_get_valu:[121,122],_get_values_for_loc:122,_getbool_axi:121,_getitem_axi:121,_getitem_lowerdim:121,_getitem_tupl:121,_i:[77,78,150,160],_identity_block:135,_ilocindex:121,_index:56,_indicator_pre_merg:120,_info_axi:120,_invalid_index:121,_is_copi:121,_is_scalar_access:121,_items_overlap_with_suffix:120,_j:[150,160],_k:132,_kmean:161,_label:57,_left:120,_lib:[120,122],_locationindex:121,_locindex:121,_m:132,_make_concat_multiindex:120,_make_stag:134,_maybe_cast_for_get_loc:121,_maybe_cast_slice_bound:121,_maybe_check_integr:120,_merge_doc:120,_merge_typ:120,_mergeoper:120,_method:186,_mgr:[120,121],_novalu:186,_other:120,_pad_1x1_to_3x3_tensor:134,_recognized_scalar:121,_reindex_and_concat:120,_rid:[118,187],_right:120,_sec_1:93,_segnet:135,_self:[118,187],_sigmoid:[81,170,196],_skip:3,_slice:121,_static:[121,122],_subplot:74,_sum:186,_t:[118,187],_t_sne:[161,193],_take:121,_take_with_is_copi:121,_takeabl:121,_valid_typ:121,_validate_integ:121,_validate_kei:121,_validate_tuple_index:121,_valu:122,_vendor:132,a0958ad901d7:118,a0:[120,186],a10:122,a1:[119,120,186],a1gkdhua8we2lilmxcctgfiycqfttwx6tljchvsbz6sfau8wquo8541xaz2myyziork:59,a21453:179,a23:[178,200],a2:[119,120,186],a3:120,a3z5kdkfn3tbq:59,a4:120,a5:120,a7yia1n5fo6efhugqfis3dhueyjsa:59,a_:[82,161],a_dict:179,a_i:[82,149],a_list:179,a_n:152,aaaaaa:[158,191],aafter:159,aaron:[29,50,78,133,171],ab:[50,63,65,77,92,93,121,129,133,137,160,178,179,201],abadi:133,abbrevi:[124,130],abc:[93,121,122,179,186,202],abcd:[7,117,121,186],abcdef:121,abcmous:[112,183],abil:[43,52,54,68,76,77,80,108,131,141,152,158,162,168,178,180,191,198],abl:[3,7,10,11,14,16,20,31,40,49,50,52,53,54,57,61,62,74,76,101,104,110,112,114,118,119,122,131,139,142,144,147,153,156,159,161,162,165,173,174,175,177,183,193,196,197,200],abnorm:29,abnorml:66,abo:38,aboslut:159,about:[1,4,7,11,12,13,15,16,17,18,19,22,23,26,28,29,31,40,41,43,46,47,48,49,50,52,53,54,57,58,59,60,61,62,66,68,77,78,80,83,84,90,99,100,101,102,103,104,106,108,109,110,112,114,115,116,117,118,119,120,122,124,125,131,132,135,137,139,140,141,142,143,144,146,147,148,149,152,153,154,156,157,159,160,161,162,164,166,167,168,169,170,171,173,175,176,177,178,179,180,181,183,184,187,195,198,200,202],abov:[0,1,7,11,14,19,26,29,32,36,40,43,45,46,47,48,49,50,51,52,53,54,57,58,59,60,64,66,68,74,80,92,93,104,108,110,114,116,119,120,121,122,124,127,130,131,132,133,134,135,137,139,141,142,143,144,147,148,149,150,152,156,157,159,160,161,165,168,169,173,174,175,176,177,178,179,180,185,194,195],above_cutoff:160,abracadabra:179,abraham:202,abs_vector:[179,201],absenc:[54,161,193],absent:[121,170],absolut:[47,76,78,83,92,116,119,143,152,156,159,178,179,180,201],absolute_error:77,absolute_percentage_error:77,abund:[110,185],ac:160,academ:[112,115,140,183],academi:200,acc:[33,39,47,49,52,57,127,138,199],acc_and_loss:127,acceler:[100,110,111,143,173,185],acceleromet:[116,161],accept:[16,40,57,68,80,83,100,103,106,112,119,121,122,134,143,153,157,168,178,184,198,199],access:[6,14,16,38,41,68,74,80,99,101,102,104,106,108,112,118,121,141,144,145,162,171,173,178,179,183,184,186,194,200,201],accessor:122,accident:153,acclaim:157,accommod:[7,36,47,117,179],accompani:[116,143,173],accomplish:[88,143,153,161,168,193,198],accord:[18,45,50,54,63,65,99,108,109,110,111,116,119,121,134,141,144,147,149,152,157,161,162,166,168,173,174,175,177,193],accordingli:[41,55,127,147,178,200],account:[0,6,8,14,16,50,76,92,101,102,112,116,118,119,145,149,153,173,174,183,186],accumul:[1,50,92,106,139,150,168,179,198],accur:[15,32,33,41,50,54,59,68,69,77,80,90,101,106,112,113,116,131,135,137,141,144,149,156,159,161,165,168,173,174,184],accuraci:[29,33,34,39,40,48,49,50,51,52,54,56,57,60,64,68,69,76,77,78,80,82,83,84,102,112,117,127,135,138,143,144,145,148,149,150,152,153,155,156,158,160,162,166,167,173,174,181,183,189,193],accuracy_metr:127,accuracy_scor:[29,30,39,49,50,51,52,56,57,59,60,68,80,83,152,157,161,162,166,167,174,193,196,197],achiev:[32,33,40,48,50,54,56,59,103,106,119,132,134,135,141,142,143,144,145,152,153,154,155,157,158,159,160,178],achitectur:133,aci_servic:[9,100],aci_service_nam:[9,100],aciconfig:[9,100],acid:48,aciwebservic:[9,100],acm:[112,183],acoust:[146,147,148],acquir:[6,103,106,143,184],acquisit:[3,102,106,114,139,181,183],acro:162,acronym:109,across:[33,43,47,54,68,80,102,112,114,116,119,120,122,124,127,139,141,142,143,153,157,160,178,179,181,183],act:[3,14,22,24,35,53,62,93,104,112,119,122,127,128,132,137,168,173,186,198],act_greedi:35,act_valu:35,action:[0,7,35,40,45,46,92,93,99,103,104,112,114,117,118,119,142,144,162,166,168,178,179,180,183,198],action_prob:132,action_s:35,actions_count:35,activ:[0,29,30,32,33,34,35,36,39,40,41,43,44,45,47,48,56,57,62,82,112,126,127,128,130,132,133,134,135,136,137,138,144,153,159,160,165,183,188,189,194,199],activateion:136,activespac:187,actor:178,actual:[7,38,40,43,46,47,48,50,51,52,56,57,59,60,66,68,74,75,76,77,78,80,82,83,84,92,101,111,113,114,116,117,118,119,121,122,127,130,131,139,141,147,153,155,157,159,160,165,168,171,173,174,178,180,182,184,188,195,198],actual_result:[3,14,22,24,53,93],actual_valu:[38,77],acut:152,ad:[1,7,18,22,29,32,36,38,41,43,45,48,50,52,54,59,64,68,76,80,92,93,111,113,116,118,128,130,134,140,142,143,152,153,154,156,158,161,168,170,173,179,195,201],ada:167,adaboost:[153,167],adaboost_clf:49,adaboostclassifi:[49,56,166,167],adagradoptim:143,adam:[29,30,31,32,33,34,35,36,37,38,39,40,41,42,44,45,47,48,62,126,127,130,133,135,136,189],adamax:36,adamharlei:[127,188],adamoptim:[129,143],adapt:[47,58,62,99,112,134,139,143,153,171,183,199],adaptiveaveragepooling2d:134,add:[1,7,9,14,17,18,30,31,32,33,34,35,36,38,39,41,42,43,44,45,46,47,50,52,54,61,62,63,65,66,76,92,93,108,110,113,116,118,120,124,126,129,130,134,135,136,137,142,152,153,154,156,157,158,159,162,167,168,173,174,175,177,178,179,180,187,189,191,194,195,198,199,201],add_1:136,add_:31,add_artist:[110,185],add_ax:161,add_legend:147,add_selectbox:194,add_slid:194,add_subplot:[35,37,47,75,132],add_trick:178,add_weight:134,addit:[1,7,18,23,32,41,46,54,59,64,66,74,75,76,92,103,104,106,108,112,113,116,117,118,119,124,132,134,135,136,137,139,143,145,149,153,154,156,157,158,160,170,173,179,180,186,187,201,202],addition:[31,117,119,124,142,145,147,149,153,158,187,191],additon:32,address:[75,76,90,103,104,106,112,139,141,142,145,149,156,168,178,183,184],adel:152,adequ:[4,5,6,8,10,11,13,16,17,19,20,21,23,24,26,27,28,69,71,72,85,88,89,90,91,116,143],adher:[48,106,142],adjac:[161,178],adject:[179,201],adjunct:168,adjust:[29,33,36,45,55,57,58,75,134,142,143,152,153,158,161,167],adjusted_mutual_info_scor:161,adjusted_rand_scor:161,admin2:144,admin:187,administr:[145,180],admonit:[120,121,122],adobe_premier:38,adolesc:178,adopt:[59,85,102,112,142,143,149,153,181,183,202],adult:[178,200],advanc:[35,43,66,102,112,116,122,123,125,140,142,143,145,149,161,168,179,187,190],advantag:[40,50,54,68,80,122,134,142,145,152,155,156,158,179],advent:[49,131,142],advers:28,adversari:[36,145,190],advertis:104,advic:143,advis:[7,46,53,58,61,117,152],advoc:112,ae5:161,ae:[31,130],aebf:[118,187],aeroplan:7,aerospik:187,aesthet:22,afb:162,affect:[7,17,33,39,49,52,54,56,58,68,76,80,82,102,104,106,112,116,117,132,134,143,150,156,158,159,170,178,181,183,191],affer:138,affin:[82,147,161],affinity_matrix_:160,affinitypropag:161,afford:[7,74,117,168],african:[112,145,183],afro:[147,148],afropop:[147,148],after:[0,7,14,29,32,33,34,35,36,39,40,41,47,48,49,50,51,54,55,56,57,60,62,64,74,76,82,104,108,114,116,117,119,124,126,127,132,134,135,136,138,139,142,143,144,147,148,150,152,153,156,157,161,162,167,168,173,175,178,179,180,194,196,197,199,200,201],afterward:[32,119],ag:[9,18,22,50,51,74,88,92,93,100,101,114,116,118,11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67,179,201],command:[47,51,101,112,113,124,132,156,177,179,180,187,201],comment:[45,48,50,104,112,113,128,162,177,183,200],commerc:147,commerci:104,commiss:[17,23],commit:[0,112],committe:62,commom:[60,68,80],common:[7,31,33,40,45,46,47,48,50,54,56,59,62,66,76,92,99,103,104,106,112,113,119,120,122,124,127,128,136,137,139,141,142,143,144,147,148,153,156,158,159,160,161,165,175,176,177,178,180,182,186,201],common_el:179,common_norm:[109,185],common_runtim:29,commonest:47,commonli:[41,54,61,62,68,76,80,99,122,131,141,142,143,144,157,159,161,168,178,199],commun:[28,43,103,105,106,112,114,131,140,142,144,153,168,180,189],comp:138,compact:127,compani:[6,104,113,141,145,153,179,180],company_s:56,company_typ:56,companyx:179,compar:[14,18,21,31,33,41,47,48,50,51,54,60,61,63,64,65,68,74,77,80,90,92,93,101,108,109,111,114,116,119,121,126,130,134,143,144,145,149,150,152,157,161,166,168,174,179,198,201],comparis:[63,65],comparison:[8,14,48,50,92,106,111,113,116,118,143,149,153,158,166,178],compat:[15,55,101,119,121,129,132,136,138,140],compatible_format:200,compel:78,compens:[157,160],compet:153,competit:[131,139,145,153,156,157],compexifi:50,compil:[1,7,29,30,32,34,35,38,39,42,44,45,47,48,62,127,135,142,180,186,189],compilaton:40,complaint:[104,112,183],compleletli:[68,80],complementari:139,complet:[1,8,21,24,34,40,41,49,50,52,53,56,57,58,62,68,69,75,80,101,106,108,112,114,116,119,120,122,127,130,134,137,138,139,141,143,145,157,159,160,161,168,173,174,175,178,179,180,182,193,200,201],completeness_scor:161,complex32:119,complex:[0,1,31,32,33,49,57,58,60,61,63,64,65,66,68,76,78,80,111,114,119,124,131,134,137,139,140,141,142,143,145,149,155,158,159,160,165,168,178,180,188,189,191,192,199,202],complex_numb:178,complex_number_1:[179,201],complex_number_2:[179,201],complex_number__1:201,complexnumb:178,complexnumberwithconstructor:178,compli:112,complianc:[22,45,47,48,112,183],compliant:112,complic:[36,50,82,114,119,141,142,143,153,156,159,160,175,189],compon:[75,76,101,102,108,118,126,132,139,141,142,143,144,149,153,156,158,180,181],components_:[160,161,193],compos:[36,37,61,74,82,126,141,142,153,161,195],compose_greet_func:178,compose_greet_func_with_closur:178,composit:[119,152],compound:[179,186,201],compound_stmt:178,comprehend:49,comprehens:[93,108,122,143,173],compress:[29,30,31,106,126,131,134],compris:[39,101,142],compromis:[7,117],comput:[3,7,18,22,29,32,33,36,40,41,43,46,47,49,50,53,54,58,59,66,75,76,77,81,82,84,99,102,103,106,114,116,117,118,120,121,122,126,129,131,133,134,137,138,140,141,142,143,145,149,150,152,153,157,158,159,161,165,168,170,174,177,179,181,182,183,194,195,196,198,201,202],computation:[33,36,50,56,119,121,131,134,139,155],computationn:33,compute_reciproc:186,compute_target:[9,100],con:[7,47,56,101,112,162],concat:[22,30,36,38,42,54,56,66,130,134,135,136,137,139,165,178,179],concat_axi:120,concat_index:120,concaten:[34,38,55,76,119,120,130,135,137,175,179,189,195,196,197,201],concatenated_str:178,concav:130,conceiv:[144,178],concentr:147,concept:[3,18,29,31,47,50,59,75,76,77,101,102,114,116,118,119,122,127,134,140,141,142,143,144,145,150,153,157,158,159,166,174,177,179,188,201],conceptu:153,concern:[7,47,54,58,59,74,106,109,112,117,141,142,145,153,173,185,198],concis:[119,149,168,178,179,201],conclud:[56,59,75,102,108,116,143,150,161],conclus:[24,50,103,112,114,173],concret:[145,161,168,169,193],concurr:[84,100,101],conda:[0,109],condens:132,condit:[3,22,31,39,40,45,47,48,50,54,93,102,112,119,130,134,143,150,152,153,161,173,174,175,178,179,180,200,201],condition2:54,condorcet:149,conduct:[56,100,112,161,183],conf:18,conf_matrix:[52,57],confer:[104,108,120],confid:[40,41,48,68,80,111,134,143,144,147,149,153],config:[9,38,50,66,136,139,149,152,161,193,194],configur:[10,41,45,47,99,101,138,141,142,143,168,175,176,178],confirm:[14,30,45,47,48,59,103,106,112,160,173,183,184],conflict:[93,104,112,122],conform:[113,122,141,143],confus:[7,34,40,50,52,57,60,68,80,83,104,117,119,150,152,157,159,165,178],confusingli:160,confusion_matrix:[34,39,40,51,52,57,59,60,68,80,83,84,166,167,170,174,196,197],confusion_mtx:34,congratul:[100,101,173,174,177,180],conjug:92,conjunct:114,connect:[6,30,32,33,41,43,45,48,62,82,92,93,102,104,112,116,132,134,135,138,143,159,169,178,179,180,189],connectionist:78,connecttimeouterror:132,conquer:153,consciou:7,consecut:[14,32,40,49,157],consent:[112,183],consequ:[28,102,112,132,183],conserv:[109,185],conservationstatu:[109,185],consid:[1,3,7,8,11,14,18,22,24,29,36,39,40,41,45,46,49,50,53,56,62,64,75,76,93,101,103,104,106,108,113,114,115,116,117,126,127,132,134,136,139,142,143,144,145,147,149,150,152,153,154,157,158,159,160,161,162,168,174,177,178,179,191,193,200,201],consider:[59,101,106,112,114,115,122,153,156,160,183,184],consist:[0,1,3,8,15,30,33,34,36,41,45,49,50,52,54,59,88,106,112,117,119,126,134,135,141,142,143,144,145,150,159,161,168,173,175,179,183,184,187,198],consol:122,consolid:75,conspiraci:62,constant:[50,63,65,75,78,119,128,129,130,132,133,134,138,143,153,157,169,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class=\"section-number\">40. </span>Self-paced assignments","<span class=\"section-number\">40.22. </span>Analyzing COVID-19 papers","<span class=\"section-number\">40.27. </span>Analyzing data","<span class=\"section-number\">40.9. </span>Analyzing text about Data Science","<span class=\"section-number\">40.13. </span>Apply your skills","<span class=\"section-number\">40.16. </span>Build your own custom vis","<span class=\"section-number\">40.17. </span>Classifying datasets","<span class=\"section-number\">40.26. </span>Data preparation","<span class=\"section-number\">40.24. </span>Data processing in Python","<span class=\"section-number\">40.41. </span>Data Science in the cloud: The \u201cAzure ML SDK\u201d way","<span class=\"section-number\">40.40. </span>Data Science project using Azure ML SDK","<span class=\"section-number\">40.10. </span>Data Science scenarios","<span class=\"section-number\">40.20. </span>Displaying airport data","<span class=\"section-number\">40.15. </span>Dive into the beehive","<span class=\"section-number\">40.23. </span>Estimation of COVID-19 pandemic","<span class=\"section-number\">40.25. </span>Evaluating data from a form","<span class=\"section-number\">40.36. </span>Explore a planetary computer dataset","<span class=\"section-number\">40.37. </span>Exploring for answers","<span class=\"section-number\">40.19. </span>Introduction to probability and statistics","<span class=\"section-number\">40.12. </span>Lines, scatters and bars","<span class=\"section-number\">40.39. </span>Low code/no code Data Science project on Azure ML","<span class=\"section-number\">40.38. </span>Market research","<span class=\"section-number\">40.29. </span>Matplotlib applied","<span class=\"section-number\">40.28. </span>NYC taxi data in winter and summer","<span class=\"section-number\">40.18. </span>Small diabetes study","<span class=\"section-number\">40.21. </span>Soda profits","<span class=\"section-number\">40.35. </span>Tell a story","<span class=\"section-number\">40.14. </span>Try it in Excel","<span class=\"section-number\">40.11. </span>Write a data ethics case study","<span class=\"section-number\">40.101. </span>Intro to Autoencoders","<span class=\"section-number\">40.102. </span>Base/Denoising Autoencoder &amp; Dimension Reduction","<span class=\"section-number\">40.103. </span>Fun with Variational Autoencoders","<span class=\"section-number\">40.90. </span>How to choose cnn architecture mnist","<span class=\"section-number\">40.94. </span>Object Recognition in Images using CNN","<span class=\"section-number\">40.92. </span>Sign Language Digits Classification with CNN","<span class=\"section-number\">40.112. </span>DQN On Foreign Exchange Market","<span class=\"section-number\">40.113. </span>Art by gan","<span class=\"section-number\">40.115. </span>Generative Adversarial Networks (GANs)","<span class=\"section-number\">40.97. </span>Bitcoin LSTM Model with Tweet Volume and Sentiment","<span class=\"section-number\">40.107. </span>NN Classify 15 Fruits Assignment","<span class=\"section-number\">40.106. </span>Neural Networks for Classification with TensorFlow","<span class=\"section-number\">40.116. </span>Basic classification: Classify images of clothing","<span class=\"section-number\">40.99. </span>Google Stock Price Prediction RNN","<span class=\"section-number\">40.95. </span>Intro to TensorFlow for Deep Learning","<span class=\"section-number\">40.104. </span>Time Series Forecasting Assignment","<span class=\"section-number\">40.83. </span>Counterintuitive Challenges in ML Debugging","<span class=\"section-number\">40.82. </span>Data engineering","<span class=\"section-number\">40.84. </span>Case Study: Debugging in Classification","<span class=\"section-number\">40.85. </span>Case Study: Debugging in Regression","<span class=\"section-number\">40.75. </span>Beyond random forests: more ensemble models","<span class=\"section-number\">40.76. </span>Decision trees","<span class=\"section-number\">40.80. </span>Random Forest Classifier with Feature Importance","<span class=\"section-number\">40.74. </span>Random forests for classification","<span class=\"section-number\">40.73. </span>Random forests intro and regression","<span class=\"section-number\">40.79. </span>Boosting with tuning","<span class=\"section-number\">40.78. </span>Gradient boosting","<span class=\"section-number\">40.77. </span>Hyperparameter tuning gradient boosting","<span class=\"section-number\">40.67. </span>Decision Trees - Classification","<span class=\"section-number\">40.66. </span>Decision Trees - Intro and Regression","<span class=\"section-number\">40.63. </span>Kernel method assignment 1","<span class=\"section-number\">40.65. </span>Support Vector Machines (SVM) - Classification","<span class=\"section-number\">40.64. </span>Support Vector Machines (SVM) - Intro and SVM for Regression","<span class=\"section-number\">40.70. </span>Dropout and Batch Normalization","<span class=\"section-number\">40.71. </span>Lasso and Ridge Regression","<span class=\"section-number\">40.69. </span>Learning Curve To Identify Overfit &amp; Underfit","<span class=\"section-number\">40.68. </span>Model selection assignment 1","<span class=\"section-number\">40.72. </span>Regularized Linear Models","<span class=\"section-number\">40.88. </span>Build Classification Model","<span class=\"section-number\">40.87. </span>Build classification models","<span class=\"section-number\">40.57. </span>Create a regression model","<span class=\"section-number\">40.61. </span>Delicious asian and indian cuisines","<span class=\"section-number\">40.62. </span>Explore classification methods","<span class=\"section-number\">40.55. </span>Exploring visualizations","<span class=\"section-number\">40.58. </span>Linear and polynomial regression","<span class=\"section-number\">40.45. </span>Linear regression - California Housing","<span class=\"section-number\">40.48. </span>Gradient descent","<span class=\"section-number\">40.49. </span>Linear Regression Implementation from Scratch","<span class=\"section-number\">40.46. </span>Linear Regression Metrics","<span class=\"section-number\">40.47. </span>Loss Function","<span class=\"section-number\">40.54. </span>Managing data","<span class=\"section-number\">40.50. </span>ML logistic regression - assignment 1","<span class=\"section-number\">40.51. </span>ML logistic regression - assignment 2","<span class=\"section-number\">40.52. </span>ML neural network - Assignment 1","<span class=\"section-number\">40.42. </span>Machine Learning overview - assignment 1","<span class=\"section-number\">40.43. </span>Machine Learning overview - assignment 2","<span class=\"section-number\">40.89. </span>Parameter play","<span class=\"section-number\">40.60. </span>Pumpkin varieties and color","<span class=\"section-number\">40.53. </span>Regression tools","<span class=\"section-number\">40.44. </span>Regression with Scikit-learn","<span class=\"section-number\">40.59. </span>Retrying some regression","<span class=\"section-number\">40.86. </span>Study the solvers","<span class=\"section-number\">40.56. </span>Try a different model","<span class=\"section-number\">40.8. </span>Python programming advanced","<span class=\"section-number\">40.7. </span>Python programming basics","<span class=\"section-number\">40.6. </span>Python programming introduction","<span class=\"section-number\">40.5. </span>Project Plan\u200b Template","<span class=\"section-number\">40.3. </span>First assignment","<span class=\"section-number\">40.4. </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\">5.4.3. </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\">5.4. </span>Pandas","<span class=\"section-number\">5.1. </span>Relational databases","<span class=\"section-number\">5. </span>Working with data","<span class=\"section-number\">27. </span>Autoencoder","<span class=\"section-number\">24. </span>Convolutional Neural Networks","<span class=\"section-number\">24.3.1.2. </span>Deepdream in TensorFlow","<span class=\"section-number\">24.3.1.1. </span>Stylenet / Neural-Style","<span class=\"section-number\">33. </span>Diffusion Model","<span class=\"section-number\">23. </span>Intro to Deep Learning","<span class=\"section-number\">30. </span>Deep Q-learning","<span class=\"section-number\">25. </span>Generative adversarial networks","<span class=\"section-number\">31. </span>Image classification","<span class=\"section-number\">32. </span>Image segmentation","<span class=\"section-number\">28. </span>Long-short term memory","<span class=\"section-number\">34. </span>Object detection","<span class=\"section-number\">26. </span>Recurrent Neural Networks","<span class=\"section-number\">29. </span>Time series","Learn AI together, for free","<span class=\"section-number\">37. </span>Data engineering","<span class=\"section-number\">39. </span>Model deployment","<span class=\"section-number\">38. </span>Model training &amp; evaluation","<span class=\"section-number\">35. </span>Overview","<span class=\"section-number\">36. </span>Problem framing","<span class=\"section-number\">16. </span>Clustering models for Machine Learning","<span class=\"section-number\">16.1. </span>Introduction to clustering","<span class=\"section-number\">16.2. </span>K-Means clustering","<span class=\"section-number\">17.1. </span>Bagging","<span class=\"section-number\">17.3. </span>Feature importance","<span class=\"section-number\">17. </span>Getting started with ensemble learning","<span class=\"section-number\">17.2. </span>Random forest","<span class=\"section-number\">18.1. </span>Gradient Boosting","<span class=\"section-number\">18.2. </span>Gradient boosting example","<span class=\"section-number\">18. </span>Introduction to Gradient Boosting","<span class=\"section-number\">18.3. </span>XGBoost","<span class=\"section-number\">18.4. </span>XGBoost + k-fold CV + Feature Importance","<span class=\"section-number\">20. </span>Kernel method","<span class=\"section-number\">22. </span>Model selection","<span class=\"section-number\">19. </span>Unsupervised learning","<span class=\"section-number\">21. </span>Unsupervised learning: PCA and clustering","<span class=\"section-number\">13. </span>Build a web app to use a Machine Learning model","<span class=\"section-number\">14.4. </span>Applied Machine Learning : build a web app","<span class=\"section-number\">14. </span>Getting started with classification","<span class=\"section-number\">14.1. </span>Introduction to classification","<span class=\"section-number\">14.2. </span>More classifiers","<span class=\"section-number\">14.3. </span>Yet other classifiers","<span class=\"section-number\">10. </span>Machine Learning overview","<span class=\"section-number\">11. </span>Summary of machine learning fundamentals","<span class=\"section-number\">15.2. </span>Gradient descent","<span class=\"section-number\">15.1. </span>Loss function","<span class=\"section-number\">15. </span>parameter-optimization","<span class=\"section-number\">12.3. </span>Linear and polynomial regression","<span class=\"section-number\">12.4. </span>Logistic regression","<span class=\"section-number\">12.2. </span>Managing data","<span class=\"section-number\">12. </span>Regression models for Machine Learning","<span class=\"section-number\">12.1. </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\">41.10. </span>Data Science in real world","<span class=\"section-number\">41.9. </span>Data Science in the cloud","<span class=\"section-number\">41.4. </span>Data Science introduction","<span class=\"section-number\">41.8. </span>Data Science lifecycle","<span class=\"section-number\">41.7. </span>Data visualization","<span class=\"section-number\">41.6. </span>NumPy and Pandas","<span class=\"section-number\">41.5. </span>Relational vs. non-relational database","<span class=\"section-number\">41.20. </span>Convolutional Neural Network","<span class=\"section-number\">41.21. </span>Generative Adversarial Network","<span class=\"section-number\">41. </span>Slides","<span class=\"section-number\">41.18. </span>Kernel method","<span class=\"section-number\">41.19. </span>Model Selection","<span class=\"section-number\">41.17. </span>Unsupervised learning","<span class=\"section-number\">41.16. </span>Build an machine learning web application","<span class=\"section-number\">41.12. </span>Linear Regression","<span class=\"section-number\">41.13. </span>Logistic Regression","<span class=\"section-number\">41.14. </span>Logistic Regression","<span class=\"section-number\">41.11. </span>Machine Learning overview","<span class=\"section-number\">41.15. </span>Neural Network","<span class=\"section-number\">41.3. </span>Python programming advanced","<span class=\"section-number\">41.2. </span>Python programming basics","<span class=\"section-number\">41.1. </span>Python programming introduction"],titleterms:{"0":59,"1":[3,24,30,32,43,49,50,52,53,54,56,57,58,59,60,61,65,68,74,75,80,82,83,104,113,119,134,161,181,182,183,184,185,186,187,188,189,191,195,197,201,202],"10":[40,56,127,134],"100":[51,59,134],"1000":[59,134],"11":56,"12":56,"13":56,"1300131294":158,"15":39,"19":[1,8,14,129],"1d":119,"2":[3,24,30,32,43,44,49,50,51,52,53,54,56,57,58,60,61,68,75,80,81,84,104,113,119,161,181,182,183,184,185,186,187,188,189,191,195,197,201,202],"2d":[30,119,191],"3":[3,24,32,39,43,49,50,52,53,54,56,57,58,60,61,68,74,80,104,119,181,182,183,184,185,186,188,189,195,201,202],"3d":[30,83,108,191],"4":[3,24,32,43,49,50,51,52,53,54,56,57,58,60,61,68,80,104,161,181,182,183,184,185,186,187,201,202],"5":[24,32,43,49,50,53,54,56,57,58,60,61,68,80,83,104,134,161,181,182,183,184,185,187,201,202],"50":56,"500":56,"6":[43,50,52,53,54,56,57,58,60,61,68,80,181,182,183,184,185],"7":[43,50,52,53,56,57,58,60,61,181,184,185],"8":[52,56,184],"9":56,"90":158,"abstract":170,"boolean":[119,179,180,201,202],"break":[92,178,200],"case":[28,45,47,48,50,104,112,175,191],"class":[35,39,47,50,51,63,65,92,171,178,200],"default":[51,59,129,178],"do":[47,114,174,175,179,183,191],"final":[49,74,75,170],"float":[7,179,201],"function":[43,51,59,75,76,78,82,92,93,119,131,132,153,170,171,178,180,186,196,197,199,200],"import":[9,29,33,37,41,44,49,51,52,53,54,56,57,58,59,60,61,64,66,83,117,129,130,143,150,157,177,178,195,196,197,199],"long":136,"new":[56,94,119,122,169,196,197],"null":[7,59,186],"public":37,"return":[94,132,178],"short":136,"static":158,"true":59,"try":[0,27,48,68,75,80,91,178,200],"while":[92,178,200],A:[31,131,159,166,167,173],And:168,At:[46,170,171],But:175,By:153,For:92,Is:145,It:[124,174,187],NOT:174,Not:168,On:35,One:[74,75,199],That:196,The:[9,36,43,51,53,75,77,82,100,101,117,124,159,160,167,175,177,178,179,182,186,187,188,191,200,201],There:175,To:[64,159],With:[34,139,170],about:[3,33,111,165,174,185],absolut:[77,171],acceler:160,access:[93,119,122],accuraci:[32,41,47,59,134,157,161,199],acknowledg:[1,2,3,4,5,7,8,9,10,11,12,13,14,15,16,17,18,19,20,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,46,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,71,72,74,80,82,83,84,85,86,88,89,90,91,92,93,99,100,101,102,103,104,106,108,109,110,111,112,113,114,116,117,118,119,120,121,122,124,126,127,128,129,130,132,133,134,135,136,137,138,139,140,143,147,148,149,150,152,153,154,156,157,158,162,165,166,167,168,173,174,175,177,178,179,180,188,191,193,194,195,196,199],ackowledg:189,action:132,activ:131,actual:104,ad:[30,31,47,62,66,119,159],adaboost:49,adam:199,add:[48,119,143],addit:122,advanc:[32,92,119,120,178,200],adversari:[37,133,189],after:[30,159,192],ag:24,against:48,agent:[35,132],agglom:[160,161],aggreg:[119,120,183,186],ahead:82,ai:[36,41,140,193],airport:12,aka:64,algorithm:[50,54,132,144,147,152,153,157,160,169],align:122,all:[24,47,74,111,124,173,185,187],an:[9,31,64,66,100,101,104,118,119,162,188,194],anagram:179,analysi:[8,38,49,51,52,53,54,56,57,58,59,60,61,74,77,99,103,161,168,193,198],analyz:[1,2,3,18,103,119,175,184],anchor:[39,51],anim:108,ann:44,annot:[178,200],anomal:47,anomali:29,anoth:37,answer:17,ap:158,api:[34,43],app:[162,163],append:[93,186],appli:[4,22,47,112,163,166,167,183],applic:[50,126,161,194,198],approach:[51,104,124,166,187],ar:179,arbitrari:178,architectur:[32,127,131,188,199],argument:[129,178,200],arithmet:[122,179,201],arrai:[119,122,186],art:36,artifici:198,artwork:36,ascend:[93,94],asian:70,ask:175,assert:51,assign:[0,12,25,39,44,59,65,80,81,82,83,84,96,97,119,120,122,161,197],assist:135,attribut:[59,119,122,186],auc:[56,59],audienc:104,augment:[32,34,130],author:177,autoencod:[29,30,31,126],automl:[9,100,101,143],avail:119,averag:[92,175],avoid:[108,159],axi:119,azur:[9,10,20,100,101,118],b:[75,170],babylonian:93,background:130,backprop:82,bag:[49,149,152,157],balanc:165,bar:[19,22,185],base:[30,92,121,157],basebal:18,baselin:[47,48,168,198],basi:59,basic:[29,32,41,43,93,112,119,122,132,179,180,183,186,201,202],batch:[33,62,160],beehiv:13,begin:104,behind:55,best:[9,32,37,100,145],beta:130,better:[166,174],between:[24,54,56,68,74,80,119,152,171,183,186],beyond:[40,49,158,191],bi:54,bia:[64,152,159,192],bibliographi:[25,170,171],big:199,binai:54,binari:[40,171,174],binder:0,bird:[109,185],bit:[31,119],bitcoin:38,blend:54,bmi:24,boost:[49,54,55,56,153,154,155,157],bootstrap:149,bound:130,boundari:[158,160,191],boxplot:[24,83],bp:24,brain:188,broadcast:[119,186],bug:48,build:[5,29,31,32,36,41,50,51,67,68,80,108,148,162,163,173,174,175,185,194],c:59,cach:194,calcul:[47,92,93,179],california:74,call:200,callabl:121,callback:40,can:[50,56,114,183],candid:56,capac:159,captur:[106,184],cardin:51,cast:[179,201],catalog:141,catboost:54,categor:[7,51,54,57,68,74,80,173,195],categori:93,categorical_crossentropi:199,caus:45,central:[18,116],centroid:160,chain:122,challeng:[1,14,22,45,112,119,131,134,137,183],chang:[34,54],changin:34,channel:104,chart:[108,111,185,19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109,110,112,113,114,116,117,118,119,120,121,122,124,126,127,132,133,134,135,137,138,139,141,142,143,144,146,147,148,149,150,152,153,157,158,160,161,162,165,168,171,174,175,177,178,179,180,183,184,186,187,189,191,194,195,198,199,200,201,202],AND:[92,93,108,119,121,178,179,180],AS:[22,25,45,47,48,92,93,162,178,179,180],And:[31,32,40,41,43,48,49,50,52,56,58,62,68,74,76,78,80,92,100,102,104,112,116,119,126,131,132,135,138,141,142,143,144,145,149,158,160,170,175,179,183,187,191,194,201],As:[1,3,7,8,33,34,36,40,41,43,47,48,49,50,51,52,53,54,56,57,58,59,60,61,68,74,78,80,82,83,99,100,106,109,112,114,116,117,119,122,124,134,135,136,141,142,143,149,152,153,157,158,159,160,161,165,168,169,173,174,175,178,179,180,184,186,189,198,200,201],At:[28,40,48,50,56,59,68,75,80,106,116,119,124,132,142,143,145,149,153,154,159,168,175,177,178,179,186,198,199],BE:[92,93,178,179,180],BUT:[92,93,178,179,180],BY:[101,143],Be:[85,91,104,108,119,162],Being:[43,62,101,104,122],But:[33,38,40,43,48,49,50,52,53,56,57,58,59,61,64,68,74,80,100,104,109,112,124,129,131,139,141,142,143,148,152,153,154,156,158,159,160,161,168,174,177,178,179,180,191,195],By:[7,18,29,40,41,46,49,52,53,54,57,59,68,74,76,77,78,80,99,101,109,114,117,119,122,124,130,139,141,142,144,147,149,150,157,160,165,168,170,173,174,178,189],FOR:[92,93,178,179,180],For:[7,19,29,30,31,32,35,36,38,39,40,41,43,45,46,47,48,49,50,51,54,59,60,61,64,66,67,68,69,71,72,74,75,84,85,86,88,89,90,91,100,101,102,111,112,113,114,116,117,119,120,121,122,126,131,132,135,137,139,141,142,143,144,145,147,148,149,150,152,153,154,157,158,159,160,161,162,165,166,167,168,169,170,173,174,175,177,178,179,180,181,191,195,198,199,201],IN:[25,82,92,93,178,179,180],IS:[22,45,47,48,54,92,93,97,147,178,179,180],IT:[54,99,142],If:[1,7,14,16,18,29,33,34,35,39,40,41,43,45,48,49,50,51,52,53,54,57,58,59,60,62,64,66,68,69,74,76,77,80,82,92,93,95,100,101,103,104,106,108,109,110,111,112,113,114,116,117,118,119,120,121,122,124,126,130,131,132,134,136,138,141,142,143,145,147,149,152,153,154,156,158,159,160,161,162,165,167,168,170,173,174,175,177,178,179,180,187,193,195,198,200,201,202],In:[1,3,7,8,9,11,12,13,14,16,18,19,21,24,28,29,30,31,32,33,36,39,40,41,43,45,46,47,48,49,50,52,53,54,57,58,59,60,61,62,64,66,68,69,71,74,75,76,77,78,80,82,83,88,89,90,92,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,124,125,126,127,128,130,131,132,133,134,135,137,139,141,142,143,144,145,147,148,149,150,152,153,154,155,156,157,158,159,160,161,162,164,165,166,167,168,170,171,173,174,175,176,177,178,179,180,181,184,186,187,188,193,195,196,197,198,199,201,202],Is:[50,93,97,99,103,106,111,112,113,130,142,143,147,162,168,174,175,202],It:[0,1,2,3,4,5,7,8,9,10,11,13,14,15,16,17,18,19,20,23,24,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,46,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,71,72,74,76,77,78,80,82,83,84,85,86,88,89,90,91,99,100,101,102,103,104,106,108,109,110,111,112,113,114,116,117,118,119,120,121,122,127,128,129,130,131,132,133,134,135,136,137,138,139,141,142,143,144,145,146,147,148,149,150,152,153,154,155,156,157,158,159,160,161,162,165,166,167,168,170,171,173,175,177,178,179,180,182,183,189,193,194,195,196,198,199,201],Its:[131,152,157,174],NEAR:[61,74],NO:[92,93,178,179,180],NOT:[82,92,93,119,133,178,179,180],Near:[109,185],No:[7,20,29,33,50,54,56,64,82,88,93,98,100,101,110,132,149,158,160,166,170,178,179,182,185],Not:[7,40,43,49,52,54,56,68,80,101,111,117,118,136,153,166,173,174,179,194,201],OF:[22,45,47,48,92,93,133,178,179,180],ON:187,ONE:7,OR:[22,45,47,48,92,93,119,178,179,180],Of:[50,101,102,104,114,160,180,181],On:[49,50,52,57,58,59,60,61,66,68,74,80,101,104,106,143,149,152,153,156,157,158,160,161,162,168,173,177,178,184,194],One:[1,7,11,28,40,43,49,50,52,53,54,55,57,58,59,66,83,98,103,104,106,108,110,114,116,119,127,131,137,143,148,150,152,158,161,168,173,175,178,179,180,184,185,186,191,193,198,201],Or:[32,40,50,58,74,102,104,119,131,132,143,147,150,168,178,179,194,198,201],Such:[1,7,30,40,43,49,50,54,116,143,144,173,178,200],THAT:82,THE:[92,93,178,179,180],TO:[54,92,93,138,178,179,180],That:[31,32,40,43,48,49,50,52,57,61,62,68,74,80,104,109,116,119,124,132,148,150,153,154,160,162,168,170,174,179,180,198],The:[0,3,5,6,7,8,12,13,14,15,16,18,19,24,25,26,28,29,30,31,32,33,34,35,37,39,40,41,45,46,47,48,49,50,52,54,55,56,57,58,59,60,61,62,63,64,65,66,68,71,74,76,78,80,83,84,90,91,92,93,99,102,103,104,106,108,109,110,112,113,114,115,116,118,119,120,121,122,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,147,148,149,150,152,153,154,155,156,157,158,161,162,165,166,168,169,170,171,173,174,180,181,183,184,185,189,190,192,193,194,195,196,197,198,199,202],Then:[7,31,45,47,50,56,66,77,82,101,104,116,119,127,130,136,138,143,144,145,148,149,150,152,153,157,161,162,173,177,178,179,180,194],There:[0,1,3,7,18,28,29,30,32,34,36,37,39,40,41,43,46,47,49,50,51,54,57,59,60,62,64,68,72,74,80,85,100,101,104,106,108,109,110,113,114,116,117,118,119,120,124,126,127,128,131,134,136,138,139,141,142,143,144,145,147,148,150,152,153,156,160,161,162,164,165,167,168,170,173,174,176,177,178,179,180,198,199,200,201],These:[7,30,31,39,41,45,49,54,56,59,60,74,75,77,78,99,101,104,106,109,112,113,118,119,121,122,134,142,148,152,153,157,159,160,161,178,180,182,184,186,187,189,200],To:[0,1,7,14,18,22,29,33,34,36,40,41,45,46,48,49,50,52,54,58,61,62,66,68,74,75,76,82,92,99,100,101,102,104,109,110,111,112,114,116,117,119,120,122,124,129,130,131,134,135,136,139,140,141,142,143,144,145,149,150,153,154,156,157,158,160,161,162,165,168,170,173,174,175,177,178,179,180,184,185,187,190,193,194,198,200,201,202],WITH:[92,93,178,179,180],Will:[145,174,180],With:[7,40,41,43,47,50,56,60,61,62,84,99,103,104,108,109,111,112,113,114,118,119,121,122,124,131,142,143,150,152,153,158,160,161,168,171,178,187,194,199],_0:153,_1:150,_2:150,_:[18,29,31,33,37,41,51,56,81,126,127,130,132,133,134,135,136,138,139,150,153,160,170,178,179,189,195,196,201],____:[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__:178,__annotations__:[178,200],__builtins__:178,__cached__:178,__call__:[63,65,128,130,135],__class__:[37,178],__dict__:[170,196,197],__doc__:[178,200],__eq__:92,__file__:178,__finalize__:[120,121],__future__:37,__get__:120,__getitem__:[121,122],__init__:[29,30,31,33,35,36,37,43,55,63,65,81,82,94,120,128,130,132,133,134,135,136,137,154,170,178,195,196,200],__iter__:[33,133],__len__:33,__loader__:178,__main__:[35,132,158,161,162],__mul__:92,__name__:[35,37,132,161,162,178],__operators__:136,__package__:178,__repr__:55,__spec__:178,__str__:92,__sub__:92,__truediv__:92,__version__:[41,132,160,200],_aspp:135,_attach:[118,187],_bin:54,_branch:135,_build_model:35,_bunch:[57,58],_caller:120,_check_indexing_error:122,_concaten:120,_consolidate_inplac:121,_constructor:121,_conv_block:135,_conv_bn_relu:135,_conv_relu:135,_data:121,_decor:120,_deeplabv3:135,_deprecate_mismatched_index:121,_deprecated_arg:121,_engin:122,_etag:[118,187],_fcn_16:135,_fcn_32:135,_fcn_8:135,_format_argument_list:120,_fuse_bn_tensor:134,_get_axi:121,_get_block_manager_axi:121,_get_comb_axi:120,_get_concat_axi:120,_get_join_info:120,_get_list_axi:121,_get_new_ax:120,_get_result_dim:120,_get_slice_axi:121,_get_valu:[121,122],_get_values_for_loc:122,_getbool_axi:121,_getitem_axi:121,_getitem_lowerdim:121,_getitem_tupl:121,_i:[77,78,150,160],_identity_block:135,_ilocindex:121,_index:56,_indicator_pre_merg:120,_info_axi:120,_invalid_index:121,_is_copi:121,_is_scalar_access:121,_items_overlap_with_suffix:120,_j:[150,160],_k:132,_kmean:161,_label:57,_left:120,_lib:[120,122],_locationindex:121,_locindex:121,_m:132,_make_concat_multiindex:120,_make_stag:134,_maybe_cast_for_get_loc:121,_maybe_cast_slice_bound:121,_maybe_check_integr:120,_merge_doc:120,_merge_typ:120,_mergeoper:120,_method:186,_mgr:[120,121],_novalu:186,_other:120,_pad_1x1_to_3x3_tensor:134,_recognized_scalar:121,_reindex_and_concat:120,_rid:[118,187],_right:120,_sec_1:93,_segnet:135,_self:[118,187],_sigmoid:[81,170,196],_skip:3,_slice:121,_static:[121,122],_subplot:74,_sum:186,_t:[118,187],_t_sne:[161,193],_take:121,_take_with_is_copi:121,_takeabl:121,_valid_typ:121,_validate_integ:121,_validate_kei:121,_validate_tuple_index:121,_valu:122,_vendor:132,a0958ad901d7:118,a0:[120,186],a10:122,a1:[119,120,186],a1gkdhua8we2lilmxcctgfiycqfttwx6tljchvsbz6sfau8wquo8541xaz2myyziork:59,a21453:179,a23:[178,200],a2:[119,120,186],a3:120,a3z5kdkfn3tbq:59,a4:120,a5:120,a7yia1n5fo6efhugqfis3dhueyjsa:59,a_:[82,161],a_dict:179,a_i:[82,149],a_list:179,a_n:152,aaaaaa:[158,191],aafter:159,aaron:[29,50,78,133,171],ab:[50,63,65,77,92,93,121,129,133,137,160,178,179,201],abadi:133,abbrevi:[124,130],abc:[93,121,122,179,186,202],abcd:[7,117,121,186],abcdef:121,abcmous:[112,183],abil:[43,52,54,68,76,77,80,108,131,141,152,158,162,168,178,180,191,198],abl:[3,7,10,11,14,16,20,31,40,49,50,52,53,54,57,61,62,74,76,101,104,110,112,114,118,119,122,131,139,142,144,147,153,156,159,161,162,165,173,174,175,177,183,193,196,197,200],abnorm:29,abnorml:66,abo:38,aboslut:159,about:[1,4,7,11,12,13,15,16,17,18,19,22,23,26,28,29,31,40,41,43,46,47,48,49,50,52,53,54,57,58,59,60,61,62,66,68,77,78,80,83,84,90,99,100,101,102,103,104,106,108,109,110,112,114,115,116,117,118,119,120,122,124,125,131,132,135,137,139,140,141,142,143,144,146,147,148,149,152,153,154,156,157,159,160,161,162,164,166,167,168,169,170,171,173,175,176,177,178,179,180,181,183,184,187,195,198,200,202],abov:[0,1,7,11,14,19,26,29,32,36,40,43,45,46,47,48,49,50,51,52,53,54,57,58,59,60,64,66,68,74,80,92,93,104,108,110,114,116,119,120,121,122,124,127,130,131,132,133,134,135,137,139,141,142,143,144,147,148,149,150,152,156,157,159,160,161,165,168,169,173,174,175,176,177,178,179,180,185,194,195],above_cutoff:160,abracadabra:179,abraham:202,abs_vector:[179,201],absenc:[54,161,193],absent:[121,170],absolut:[47,76,78,83,92,116,119,143,152,156,159,178,179,180,201],absolute_error:77,absolute_percentage_error:77,abund:[110,185],ac:160,academ:[112,115,140,183],academi:200,acc:[33,39,47,49,52,57,127,138,199],acc_and_loss:127,acceler:[100,110,111,143,173,185],acceleromet:[116,161],accept:[16,40,57,68,80,83,100,103,106,112,119,121,122,134,143,153,157,168,178,184,198,199],access:[6,14,16,38,41,68,74,80,99,101,102,104,106,108,112,118,121,141,144,145,162,171,173,178,179,183,184,186,194,200,201],accessor:122,accident:153,acclaim:157,accommod:[7,36,47,117,179],accompani:[116,143,173],accomplish:[88,143,153,161,168,193,198],accord:[18,45,50,54,63,65,99,108,109,110,111,116,119,121,134,141,144,147,149,152,157,161,162,166,168,173,174,175,177,193],accordingli:[41,55,127,147,178,200],account:[0,6,8,14,16,50,76,92,101,102,112,116,118,119,145,149,153,173,174,183,186],accumul:[1,50,92,106,139,150,168,179,198],accur:[15,32,33,41,50,54,59,68,69,77,80,90,101,106,112,113,116,131,135,137,141,144,149,156,159,161,165,168,173,174,184],accuraci:[29,33,34,39,40,48,49,50,51,52,54,56,57,60,64,68,69,76,77,78,80,82,83,84,102,112,117,127,135,138,143,144,145,148,149,150,152,153,155,156,158,160,162,166,167,173,174,181,183,189,193],accuracy_metr:127,accuracy_scor:[29,30,39,49,50,51,52,56,57,59,60,68,80,83,152,157,161,162,166,167,174,193,196,197],achiev:[32,33,40,48,50,54,56,59,103,106,119,132,134,135,141,142,143,144,145,152,153,154,155,157,158,159,160,178],achitectur:133,aci_servic:[9,100],aci_service_nam:[9,100],aciconfig:[9,100],acid:48,aciwebservic:[9,100],acm:[112,183],acoust:[146,147,148],acquir:[6,103,106,143,184],acquisit:[3,102,106,114,139,181,183],acro:162,acronym:109,across:[33,43,47,54,68,80,102,112,114,116,119,120,122,124,127,139,141,142,143,153,157,160,178,179,181,183],act:[3,14,22,24,35,53,62,93,104,112,119,122,127,128,132,137,168,173,186,198],act_greedi:35,act_valu:35,action:[0,7,35,40,45,46,92,93,99,103,104,112,114,117,118,119,142,144,162,166,168,178,179,180,183,198],action_prob:132,action_s:35,actions_count:35,activ:[0,29,30,32,33,34,35,36,39,40,41,43,44,45,47,48,56,57,62,82,112,126,127,128,130,132,133,134,135,136,137,138,144,153,159,160,165,183,188,189,194,199],activateion:136,activespac:187,actor:178,actual:[7,38,40,43,46,47,48,50,51,52,56,57,59,60,66,68,74,75,76,77,78,80,82,83,84,92,101,111,113,114,116,117,118,119,121,122,127,130,131,139,141,147,153,155,157,159,160,165,168,171,173,174,178,180,182,184,188,195,198],actual_result:[3,14,22,24,53,93],actual_valu:[38,77],acut:152,ad:[1,7,18,22,29,32,36,38,41,43,45,48,50,52,54,59,64,68,76,80,92,93,111,113,116,118,128,130,134,140,142,143,152,153,154,156,158,161,168,170,173,179,195,201],ada:167,adaboost:[153,167],adaboost_clf:49,adaboostclassifi:[49,56,166,167],adagradoptim:143,adam:[29,30,31,32,33,34,35,36,37,38,39,40,41,42,44,45,47,48,62,126,127,130,133,135,136,189],adamax:36,adamharlei:[127,188],adamoptim:[129,143],adapt:[47,58,62,99,112,134,139,143,153,171,183,199],adaptiveaveragepooling2d:134,add:[1,7,9,14,17,18,30,31,32,33,34,35,36,38,39,41,42,43,44,45,46,47,50,52,54,61,62,63,65,66,76,92,93,108,110,113,116,118,120,124,126,129,130,134,135,136,137,142,152,153,154,156,157,158,159,162,167,168,173,174,175,177,178,179,180,187,189,191,194,195,198,199,201],add_1:136,add_:31,add_artist:[110,185],add_ax:161,add_legend:147,add_selectbox:194,add_slid:194,add_subplot:[35,37,47,75,132],add_trick:178,add_weight:134,addit:[1,7,18,23,32,41,46,54,59,64,66,74,75,76,92,103,104,106,108,112,113,116,117,118,119,124,132,134,135,136,137,139,143,145,149,153,154,156,157,158,160,170,173,179,180,186,187,201,202],addition:[31,117,119,124,142,145,147,149,153,158,187,191],additon:32,address:[75,76,90,103,104,106,112,139,141,142,145,149,156,168,178,183,184],adel:152,adequ:[4,5,6,8,10,11,13,16,17,19,20,21,23,24,26,27,28,69,71,72,85,88,89,90,91,116,143],adher:[48,106,142],adjac:[161,178],adject:[179,201],adjunct:168,adjust:[29,33,36,45,55,57,58,75,134,142,143,152,153,158,161,167],adjusted_mutual_info_scor:161,adjusted_rand_scor:161,admin2:144,admin:187,administr:[145,180],admonit:[120,121,122],adobe_premier:38,adolesc:178,adopt:[59,85,102,112,142,143,149,153,181,183,202],adult:[178,200],advanc:[35,43,66,102,112,116,122,123,125,140,142,143,145,149,161,168,179,187,190],advantag:[40,50,54,68,80,122,134,142,145,152,155,156,158,179],advent:[49,131,142],advers:28,adversari:[36,145,190],advertis:104,advic:143,advis:[7,46,53,58,61,117,152],advoc:112,ae5:161,ae:[31,130],aebf:[118,187],aeroplan:7,aerospik:187,aesthet:22,afb:162,affect:[7,17,33,39,49,52,54,56,58,68,76,80,82,102,104,106,112,116,117,132,134,143,150,156,158,159,170,178,181,183,191],affer:138,affin:[82,147,161],affinity_matrix_:160,affinitypropag:161,afford:[7,74,117,168],african:[112,145,183],afro:[147,148],afropop:[147,148],after:[0,7,14,29,32,33,34,35,36,39,40,41,47,48,49,50,51,54,55,56,57,60,62,64,74,76,82,104,108,114,116,117,119,124,126,127,132,134,135,136,138,139,142,143,144,147,148,150,152,153,156,157,161,162,167,168,173,175,178,179,180,194,196,197,199,20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4,147,149,152,154,157,159,161,168,170,173,178,179,180,186,191,193,194],comedi:180,comfort:[7,46,54,117,177],comma:[93,119,167,179,201],command:[47,51,101,112,113,124,132,156,177,179,180,187,201],comment:[45,48,50,104,112,113,128,162,177,183,200],commerc:147,commerci:104,commiss:[17,23],commit:[0,112],committe:62,commom:[60,68,80],common:[7,31,33,40,45,46,47,48,50,54,56,59,62,66,76,92,99,103,104,106,112,113,119,120,122,124,127,128,136,137,139,141,142,143,144,147,148,153,156,158,159,160,161,165,175,176,177,178,180,182,186,201],common_el:179,common_norm:[109,185],common_runtim:29,commonest:47,commonli:[41,54,61,62,68,76,80,99,122,131,141,142,143,144,157,159,161,168,178,199],commun:[28,43,103,105,106,112,114,131,140,142,144,153,168,180,189],comp:138,compact:127,compani:[6,104,113,141,145,153,179,180],company_s:56,company_typ:56,companyx:179,compar:[14,18,21,31,33,41,47,48,50,51,54,60,61,63,64,65,68,74,77,80,90,92,93,101,108,109,111,114,116,119,121,126,130,134,143,144,145,149,150,152,157,161,166,168,174,179,198,201],comparis:[63,65],comparison:[8,14,48,50,92,106,111,113,116,118,143,149,153,158,166,178],compat:[15,55,101,119,121,129,132,136,138,140],compatible_format:200,compel:78,compens:[157,160],compet:153,competit:[131,139,145,153,156,157],compexifi:50,compil:[1,7,29,30,32,34,35,38,39,42,44,45,47,48,62,127,135,142,180,186,189],compilaton:40,complaint:[104,112,183],compleletli:[68,80],complementari:139,complet:[1,8,21,24,34,40,41,49,50,52,53,56,57,58,62,68,69,75,80,101,106,108,112,114,116,119,120,122,127,130,134,137,138,139,141,143,145,157,159,160,161,168,173,174,175,178,179,180,182,193,200,201],completeness_scor:161,complex32:119,complex:[0,1,31,32,33,49,57,58,60,61,63,64,65,66,68,76,78,80,111,114,119,124,131,134,137,139,140,141,142,143,145,149,155,158,159,160,165,168,178,180,188,189,191,192,199,202],complex_numb:178,complex_number_1:[179,201],complex_number_2:[179,201],complex_number__1:201,complexnumb:178,complexnumberwithconstructor:178,compli:112,complianc:[22,45,47,48,112,183],compliant:112,complic:[36,50,82,114,119,141,142,143,153,156,159,160,175,189],compon:[75,76,101,102,108,118,126,132,139,141,142,143,144,149,153,156,158,180,181],components_:[160,161,193],compos:[36,37,61,74,82,126,141,142,153,161,195],compose_greet_func:178,compose_greet_func_with_closur:178,composit:[119,152],compound:[179,186,201],compound_stmt:178,comprehend:49,comprehens:[93,108,122,143,173],compress:[29,30,31,106,126,131,134],compris:[39,101,142],compromis:[7,117],comput:[3,7,18,22,29,32,33,36,40,41,43,46,47,49,50,53,54,58,59,66,75,76,77,81,82,84,99,102,103,106,114,116,117,118,120,121,122,126,129,131,133,134,137,138,140,141,142,143,145,149,150,152,153,157,158,159,161,165,168,170,174,177,179,181,182,183,194,195,196,198,201,202],computation:[33,36,50,56,119,121,131,134,139,155],computationn:33,compute_reciproc:186,compute_target:[9,100],con:[7,47,56,101,112,162],concat:[22,30,36,38,42,54,56,66,130,134,135,136,137,139,165,178,179],concat_axi:120,concat_index:120,concaten:[34,38,55,76,119,120,130,135,137,175,179,189,195,196,197,201],concatenated_str:178,concav:130,conceiv:[144,178],concentr:147,concept:[3,18,29,31,47,50,59,75,76,77,101,102,114,116,118,119,122,127,134,140,141,142,143,144,145,150,153,157,158,159,166,174,177,179,188,201],conceptu:153,concern:[7,47,54,58,59,74,106,109,112,117,141,142,145,153,173,185,198],concis:[119,149,168,178,179,201],conclud:[56,59,75,102,108,116,143,150,161],conclus:[24,50,103,112,114,173],concret:[145,161,168,169,193],concurr:[84,100,101],conda:[0,109],condens:132,condit:[3,22,31,39,40,45,47,48,50,54,93,102,112,119,130,134,143,150,152,153,161,173,174,175,178,179,180,200,201],condition2:54,condorcet:149,conduct:[56,100,112,161,183],conf:18,conf_matrix:[52,57],confer:[104,108,120],confid:[40,41,48,68,80,111,134,143,144,147,149,153],config:[9,38,50,66,136,139,149,152,161,193,194],configur:[10,41,45,47,99,101,138,141,142,143,168,175,176,178],confirm:[14,30,45,47,48,59,103,106,112,160,173,183,184],conflict:[93,104,112,122],conform:[113,122,141,143],confus:[7,34,40,50,52,57,60,68,80,83,104,117,119,150,152,157,159,165,178],confusingli:160,confusion_matrix:[34,39,40,51,52,57,59,60,68,80,83,84,166,167,170,174,196,197],confusion_mtx:34,congratul:[100,101,173,174,177,180],conjug:92,conjunct:114,connect:[6,30,32,33,41,43,45,48,62,82,92,93,102,104,112,116,132,134,135,138,143,159,169,178,179,180,189],connectionist:78,connecttimeouterror:132,conquer:153,consciou:7,consecut:[14,32,40,49,157],consent:[112,183],consequ:[28,102,112,132,183],conserv:[109,185],conservationstatu:[109,185],consid:[1,3,7,8,11,14,18,22,24,29,36,39,40,41,45,46,49,50,53,56,62,64,75,76,93,101,103,104,106,108,113,114,115,116,117,126,127,132,134,136,139,142,143,144,145,147,149,150,152,153,154,157,158,159,160,161,162,168,174,177,178,179,191,193,200,201],consider:[59,101,106,112,114,115,122,153,156,160,183,184],consist:[0,1,3,8,15,30,33,34,36,41,45,49,50,52,54,59,88,106,112,117,119,126,134,135,141,142,143,144,145,150,159,161,168,173,175,179,183,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class=\"section-number\">40. </span>Self-paced assignments","<span class=\"section-number\">40.22. </span>Analyzing COVID-19 papers","<span class=\"section-number\">40.27. </span>Analyzing data","<span class=\"section-number\">40.9. </span>Analyzing text about Data Science","<span class=\"section-number\">40.13. </span>Apply your skills","<span class=\"section-number\">40.16. </span>Build your own custom vis","<span class=\"section-number\">40.17. </span>Classifying datasets","<span class=\"section-number\">40.26. </span>Data preparation","<span class=\"section-number\">40.24. </span>Data processing in Python","<span class=\"section-number\">40.41. </span>Data Science in the cloud: The \u201cAzure ML SDK\u201d way","<span class=\"section-number\">40.40. </span>Data Science project using Azure ML SDK","<span class=\"section-number\">40.10. </span>Data Science scenarios","<span class=\"section-number\">40.20. </span>Displaying airport data","<span class=\"section-number\">40.15. </span>Dive into the beehive","<span class=\"section-number\">40.23. </span>Estimation of COVID-19 pandemic","<span class=\"section-number\">40.25. </span>Evaluating data from a form","<span class=\"section-number\">40.36. </span>Explore a planetary computer dataset","<span class=\"section-number\">40.37. </span>Exploring for answers","<span class=\"section-number\">40.19. </span>Introduction to probability and statistics","<span class=\"section-number\">40.12. </span>Lines, scatters and bars","<span class=\"section-number\">40.39. </span>Low code/no code Data Science project on Azure ML","<span class=\"section-number\">40.38. </span>Market research","<span class=\"section-number\">40.29. </span>Matplotlib applied","<span class=\"section-number\">40.28. </span>NYC taxi data in winter and summer","<span class=\"section-number\">40.18. </span>Small diabetes study","<span class=\"section-number\">40.21. </span>Soda profits","<span class=\"section-number\">40.35. </span>Tell a story","<span class=\"section-number\">40.14. </span>Try it in Excel","<span class=\"section-number\">40.11. </span>Write a data ethics case study","<span class=\"section-number\">40.101. </span>Intro to Autoencoders","<span class=\"section-number\">40.102. </span>Base/Denoising Autoencoder &amp; Dimension Reduction","<span class=\"section-number\">40.103. </span>Fun with Variational Autoencoders","<span class=\"section-number\">40.90. </span>How to choose cnn architecture mnist","<span class=\"section-number\">40.94. </span>Object Recognition in Images using CNN","<span class=\"section-number\">40.92. </span>Sign Language Digits Classification with CNN","<span class=\"section-number\">40.112. </span>DQN On Foreign Exchange Market","<span class=\"section-number\">40.113. </span>Art by gan","<span class=\"section-number\">40.115. </span>Generative Adversarial Networks (GANs)","<span class=\"section-number\">40.97. </span>Bitcoin LSTM Model with Tweet Volume and Sentiment","<span class=\"section-number\">40.107. </span>NN Classify 15 Fruits Assignment","<span class=\"section-number\">40.106. </span>Neural Networks for Classification with TensorFlow","<span class=\"section-number\">40.116. </span>Basic classification: Classify images of clothing","<span class=\"section-number\">40.99. </span>Google Stock Price Prediction RNN","<span class=\"section-number\">40.95. </span>Intro to TensorFlow for Deep Learning","<span class=\"section-number\">40.104. </span>Time Series Forecasting Assignment","<span class=\"section-number\">40.83. </span>Counterintuitive Challenges in ML Debugging","<span class=\"section-number\">40.82. </span>Data engineering","<span class=\"section-number\">40.84. </span>Case Study: Debugging in Classification","<span class=\"section-number\">40.85. </span>Case Study: Debugging in Regression","<span class=\"section-number\">40.75. </span>Beyond random forests: more ensemble models","<span class=\"section-number\">40.76. </span>Decision trees","<span class=\"section-number\">40.80. </span>Random Forest Classifier with Feature Importance","<span class=\"section-number\">40.74. </span>Random forests for classification","<span class=\"section-number\">40.73. </span>Random forests intro and regression","<span class=\"section-number\">40.79. </span>Boosting with tuning","<span class=\"section-number\">40.78. </span>Gradient boosting","<span class=\"section-number\">40.77. </span>Hyperparameter tuning gradient boosting","<span class=\"section-number\">40.67. </span>Decision Trees - Classification","<span class=\"section-number\">40.66. </span>Decision Trees - Intro and Regression","<span class=\"section-number\">40.63. </span>Kernel method assignment 1","<span class=\"section-number\">40.65. </span>Support Vector Machines (SVM) - Classification","<span class=\"section-number\">40.64. </span>Support Vector Machines (SVM) - Intro and SVM for Regression","<span class=\"section-number\">40.70. </span>Dropout and Batch Normalization","<span class=\"section-number\">40.71. </span>Lasso and Ridge Regression","<span class=\"section-number\">40.69. </span>Learning Curve To Identify Overfit &amp; Underfit","<span class=\"section-number\">40.68. </span>Model selection assignment 1","<span class=\"section-number\">40.72. </span>Regularized Linear Models","<span class=\"section-number\">40.88. </span>Build Classification Model","<span class=\"section-number\">40.87. </span>Build classification models","<span class=\"section-number\">40.57. </span>Create a regression model","<span class=\"section-number\">40.61. </span>Delicious asian and indian cuisines","<span class=\"section-number\">40.62. </span>Explore classification methods","<span class=\"section-number\">40.55. </span>Exploring visualizations","<span class=\"section-number\">40.58. </span>Linear and polynomial regression","<span class=\"section-number\">40.45. </span>Linear regression - California Housing","<span class=\"section-number\">40.48. </span>Gradient descent","<span class=\"section-number\">40.49. </span>Linear Regression Implementation from Scratch","<span class=\"section-number\">40.46. </span>Linear Regression Metrics","<span class=\"section-number\">40.47. </span>Loss Function","<span class=\"section-number\">40.54. </span>Managing data","<span class=\"section-number\">40.50. </span>ML logistic regression - assignment 1","<span class=\"section-number\">40.51. </span>ML logistic regression - assignment 2","<span class=\"section-number\">40.52. </span>ML neural network - Assignment 1","<span class=\"section-number\">40.42. </span>Machine Learning overview - assignment 1","<span class=\"section-number\">40.43. </span>Machine Learning overview - assignment 2","<span class=\"section-number\">40.89. </span>Parameter play","<span class=\"section-number\">40.60. </span>Pumpkin varieties and color","<span class=\"section-number\">40.53. </span>Regression tools","<span class=\"section-number\">40.44. </span>Regression with Scikit-learn","<span class=\"section-number\">40.59. </span>Retrying some regression","<span class=\"section-number\">40.86. </span>Study the solvers","<span class=\"section-number\">40.56. </span>Try a different model","<span class=\"section-number\">40.8. </span>Python programming advanced","<span class=\"section-number\">40.7. </span>Python programming basics","<span class=\"section-number\">40.6. </span>Python programming introduction","<span class=\"section-number\">40.5. </span>Project Plan\u200b Template","<span class=\"section-number\">40.3. </span>First assignment","<span class=\"section-number\">40.4. </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\">5.4.3. </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\">5.4. </span>Pandas","<span class=\"section-number\">5.1. </span>Relational databases","<span class=\"section-number\">5. </span>Working with data","<span class=\"section-number\">27. </span>Autoencoder","<span class=\"section-number\">24. </span>Convolutional Neural Networks","<span class=\"section-number\">24.3.1.2. </span>Deepdream in TensorFlow","<span class=\"section-number\">24.3.1.1. </span>Stylenet / Neural-Style","<span class=\"section-number\">33. </span>Diffusion Model","<span class=\"section-number\">23. </span>Intro to Deep Learning","<span class=\"section-number\">30. </span>Deep Q-learning","<span class=\"section-number\">25. </span>Generative adversarial networks","<span class=\"section-number\">31. </span>Image classification","<span class=\"section-number\">32. </span>Image segmentation","<span class=\"section-number\">28. </span>Long-short term memory","<span class=\"section-number\">34. </span>Object detection","<span class=\"section-number\">26. </span>Recurrent Neural Networks","<span class=\"section-number\">29. </span>Time series","Learn AI together, for free","<span class=\"section-number\">37. </span>Data engineering","<span class=\"section-number\">39. </span>Model deployment","<span class=\"section-number\">38. </span>Model training &amp; evaluation","<span class=\"section-number\">35. </span>Overview","<span class=\"section-number\">36. </span>Problem framing","<span class=\"section-number\">16. </span>Clustering models for Machine Learning","<span class=\"section-number\">16.1. </span>Introduction to clustering","<span class=\"section-number\">16.2. </span>K-Means clustering","<span class=\"section-number\">17.1. </span>Bagging","<span class=\"section-number\">17.3. </span>Feature importance","<span class=\"section-number\">17. </span>Getting started with ensemble learning","<span class=\"section-number\">17.2. </span>Random forest","<span class=\"section-number\">18.1. </span>Gradient Boosting","<span class=\"section-number\">18.2. </span>Gradient boosting example","<span class=\"section-number\">18. </span>Introduction to Gradient Boosting","<span class=\"section-number\">18.3. </span>XGBoost","<span class=\"section-number\">18.4. </span>XGBoost + k-fold CV + Feature Importance","<span class=\"section-number\">20. </span>Kernel method","<span class=\"section-number\">22. </span>Model selection","<span class=\"section-number\">19. </span>Unsupervised learning","<span class=\"section-number\">21. </span>Unsupervised learning: PCA and clustering","<span class=\"section-number\">13. </span>Build a web app to use a Machine Learning model","<span class=\"section-number\">14.4. </span>Applied Machine Learning : build a web app","<span class=\"section-number\">14. </span>Getting started with classification","<span class=\"section-number\">14.1. </span>Introduction to classification","<span class=\"section-number\">14.2. </span>More classifiers","<span class=\"section-number\">14.3. </span>Yet other classifiers","<span class=\"section-number\">10. </span>Machine Learning overview","<span class=\"section-number\">11. </span>Summary of machine learning fundamentals","<span class=\"section-number\">15.2. </span>Gradient descent","<span class=\"section-number\">15.1. </span>Loss function","<span class=\"section-number\">15. </span>parameter-optimization","<span class=\"section-number\">12.3. </span>Linear and polynomial regression","<span class=\"section-number\">12.4. </span>Logistic regression","<span class=\"section-number\">12.2. </span>Managing data","<span class=\"section-number\">12. </span>Regression models for Machine Learning","<span class=\"section-number\">12.1. </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\">41.10. </span>Data Science in real world","<span class=\"section-number\">41.9. </span>Data Science in the cloud","<span class=\"section-number\">41.4. </span>Data Science introduction","<span class=\"section-number\">41.8. </span>Data Science lifecycle","<span class=\"section-number\">41.7. </span>Data visualization","<span class=\"section-number\">41.6. </span>NumPy and Pandas","<span class=\"section-number\">41.5. </span>Relational vs. non-relational database","<span class=\"section-number\">41.20. </span>Convolutional Neural Network","<span class=\"section-number\">41.21. </span>Generative Adversarial Network","<span class=\"section-number\">41. </span>Slides","<span class=\"section-number\">41.18. </span>Kernel method","<span class=\"section-number\">41.19. </span>Model Selection","<span class=\"section-number\">41.17. </span>Unsupervised learning","<span class=\"section-number\">41.16. </span>Build an machine learning web application","<span class=\"section-number\">41.12. </span>Linear Regression","<span class=\"section-number\">41.13. </span>Logistic Regression","<span class=\"section-number\">41.14. </span>Logistic Regression","<span class=\"section-number\">41.11. </span>Machine Learning overview","<span class=\"section-number\">41.15. </span>Neural Network","<span class=\"section-number\">41.3. </span>Python programming advanced","<span class=\"section-number\">41.2. </span>Python programming basics","<span class=\"section-number\">41.1. </span>Python programming introduction"],titleterms:{"0":59,"1":[3,24,30,32,43,49,50,52,53,54,56,57,58,59,60,61,65,68,74,75,80,82,83,104,113,119,134,161,181,182,183,184,185,186,187,188,189,191,195,197,201,202],"10":[40,56,127,134],"100":[51,59,134],"1000":[59,134],"11":56,"12":56,"13":56,"1300131294":158,"15":39,"19":[1,8,14,129],"1d":119,"2":[3,24,30,32,43,44,49,50,51,52,53,54,56,57,58,60,61,68,75,80,81,84,104,113,119,161,181,182,183,184,185,186,187,188,189,191,195,197,201,202],"2d":[30,119,191],"3":[3,24,32,39,43,49,50,52,53,54,56,57,58,60,61,68,74,80,104,119,181,182,183,184,185,186,188,189,195,201,202],"3d":[30,83,108,191],"4":[3,24,32,43,49,50,51,52,53,54,56,57,58,60,61,68,80,104,161,181,182,183,184,185,186,187,201,202],"5":[24,32,43,49,50,53,54,56,57,58,60,61,68,80,83,104,134,161,181,182,183,184,185,187,201,202],"50":56,"500":56,"6":[43,50,52,53,54,56,57,58,60,61,68,80,181,182,183,184,185],"7":[43,50,52,53,56,57,58,60,61,181,184,185],"8":[52,56,184],"9":56,"90":158,"abstract":170,"boolean":[119,179,180,201,202],"break":[92,178,200],"case":[28,45,47,48,50,104,112,175,191],"class":[35,39,47,50,51,63,65,92,171,178,200],"default":[51,59,129,178],"do":[47,114,174,175,179,183,191],"final":[49,74,75,170],"float":[7,179,201],"function":[43,51,59,75,76,78,82,92,93,119,131,132,153,170,171,178,180,186,196,197,199,200],"import":[9,29,33,37,41,44,49,51,52,53,54,56,57,58,59,60,61,64,66,83,117,129,130,143,150,157,177,178,195,196,197,199],"long":136,"new":[56,94,119,122,169,196,197],"null":[7,59,186],"public":37,"return":[94,132,178],"short":136,"static":158,"true":59,"try":[0,27,48,68,75,80,91,178,200],"while":[92,178,200],A:[31,131,159,166,167,173],And:168,At:[46,170,171],But:175,By:153,For:92,Is:145,It:[124,174,187],NOT:174,Not:168,On:35,One:[74,75,199],That:196,The:[9,36,43,51,53,75,77,82,100,101,117,124,159,160,167,175,177,178,179,182,186,187,188,191,200,201],There:175,To:[64,159],With:[34,139,170],about:[3,33,111,165,174,185],absolut:[77,171],acceler:160,access:[93,119,122],accuraci:[32,41,47,59,134,157,161,199],acknowledg:[1,2,3,4,5,7,8,9,10,11,12,13,14,15,16,17,18,19,20,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,46,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,71,72,74,80,82,83,84,85,86,88,89,90,91,92,93,99,100,101,102,103,104,106,108,109,110,111,112,113,114,116,117,118,119,120,121,122,124,126,127,128,129,130,132,133,134,135,136,137,138,139,140,143,147,148,149,150,152,153,154,156,157,158,162,165,166,167,168,173,174,175,177,178,179,180,188,191,193,194,195,196,199],ackowledg:189,action:132,activ:131,actual:104,ad:[30,31,47,62,66,119,159],adaboost:49,adam:199,add:[48,119,143],addit:122,advanc:[32,92,119,120,178,200],adversari:[37,133,189],after:[30,159,192],ag:24,against:48,agent:[35,132],agglom:[160,161],aggreg:[119,120,183,186],ahead:82,ai:[36,41,140,193],airport:12,aka:64,algorithm:[50,54,132,144,147,152,153,157,160,169],align:122,all:[24,47,74,111,124,173,185,187],an:[9,31,64,66,100,101,104,118,119,162,188,194],anagram:179,analysi:[8,38,49,51,52,53,54,56,57,58,59,60,61,74,77,99,103,161,168,193,198],analyz:[1,2,3,18,103,119,175,184],anchor:[39,51],anim:108,ann:44,annot:[178,200],anomal:47,anomali:29,anoth:37,answer:17,ap:158,api:[34,43],app:[162,163],append:[93,186],appli:[4,22,47,112,163,166,167,183],applic:[50,126,161,194,198],approach:[51,104,124,166,187],ar:179,arbitrari:178,architectur:[32,127,131,188,199],argument:[129,178,200],arithmet:[122,179,201],arrai:[119,122,186],art:36,artifici:198,artwork:36,ascend:[93,94],asian:70,ask:175,assert:51,assign:[0,12,25,39,44,59,65,80,81,82,83,84,96,97,119,120,122,161,197],assist:135,attribut:[59,119,122,186],auc:[56,59],audienc:104,augment:[32,34,130],author:177,autoencod:[29,30,31,126],automl:[9,100,101,143],avail:119,averag:[92,175],avoid:[108,159],axi:119,azur:[9,10,20,100,101,118],b:[75,170],babylonian:93,background:130,backprop:82,bag:[49,149,152,157],balanc:165,bar:[19,22,185],base:[30,92,121,157],basebal:18,baselin:[47,48,168,198],basi:59,basic:[29,32,41,43,93,112,119,122,132,179,180,183,186,201,202],batch:[33,62,160],beehiv:13,begin:104,behind:55,best:[9,32,37,100,145],beta:130,better:[166,174],between:[24,54,56,68,74,80,119,152,171,183,186],beyond:[40,49,158,191],bi:54,bia:[64,152,159,192],bibliographi:[25,170,171],big:199,binai:54,binari:[40,171,174],binder:0,bird:[109,185],bit:[31,119],bitcoin:38,blend:54,bmi:24,boost:[49,54,55,56,153,154,155,157],bootstrap:149,bound:130,boundari:[158,160,191],boxplot:[24,83],bp:24,brain:188,broadcast:[119,186],bug:48,build:[5,29,31,32,36,41,50,51,67,68,80,108,148,162,163,173,174,175,185,194],c:59,cach:194,calcul:[47,92,93,179],california:74,call:200,callabl:121,callback:40,can:[50,56,114,183],candid:56,capac:159,captur:[106,184],cardin:51,cast:[179,201],catalog:141,catboost:54,categor:[7,51,54,57,68,74,80,173,195],categori:93,categorical_crossentropi:199,caus:45,central:[18,116],centroid:160,chain:122,challeng:[1,14,22,45,112,119,131,134,137,183],chang:[34,54],changin:34,channel:104,chart:[108,111,185,19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