From 9012f17a99767276d273991645311567d6740290 Mon Sep 17 00:00:00 2001
From: ZHANG Jing <jing3jour@gmail.com>
Date: Wed, 4 Sep 2024 17:19:59 +0200
Subject: [PATCH] update

---
 deep_learning_from_scratch.ipynb | 18 +++++++-----------
 1 file changed, 7 insertions(+), 11 deletions(-)

diff --git a/deep_learning_from_scratch.ipynb b/deep_learning_from_scratch.ipynb
index 0256e41..c4671eb 100644
--- a/deep_learning_from_scratch.ipynb
+++ b/deep_learning_from_scratch.ipynb
@@ -229,7 +229,8 @@
     "A first look at neural network diagram    \n",
     "![A neural network](./figures/dlscratch_nn.png)    \n",
     "Review a perceptron:    \n",
-    "![A perceptron](./figures/dlscratch_perceptronbias.png) $\n",
+    "![A perceptron](./figures/dlscratch_perceptronbias.png)     \n",
+    "$\n",
     "y = \n",
     "\\begin{cases} \n",
     "0 & (b+ w_1 x_1 + w_2 x_2 \\leq 0), \\\\\n",
@@ -437,13 +438,12 @@
    "metadata": {},
    "source": [
     "### A 3-layer neural networks\n",
-    "\n",
-    "![From input to 1st layer](./figures/dlscratch_nn3layer1.png)\n",
     "From input to 1st layer    \n",
-    "![From 1st to 2nd layer](./figures/dlscratch_nn3layer2.png)\n",
+    "![From input to 1st layer](./figures/dlscratch_nn3layer1.png)    \n",
     "From 1st to 2nd layer    \n",
-    "![From 2nd to output layer](./figures/dlscratch_nn3layer3.png)\n",
-    "From 2nd to output layer"
+    "![From 1st to 2nd layer](./figures/dlscratch_nn3layer2.png)    \n",
+    "From 2nd to output layer    \n",
+    "![From 2nd to output layer](./figures/dlscratch_nn3layer3.png)\n"
    ]
   },
   {
@@ -593,8 +593,6 @@
     }
    ],
    "source": [
-    "import sys, os\n",
-    "sys.path.append(os.pardir)  # Settings for importing files from a parent directory\n",
     "import numpy as np\n",
     "from dataset.mnist import load_mnist\n",
     "\n",
@@ -797,7 +795,7 @@
    "metadata": {},
    "source": [
     "## Learning neural networks\n",
-    "The \"learning\" mentioned here refers to the process of automatically obtaining the optimal weight parameters from the training data.\n",
+    "The \"learning\" mentioned here refers to the process of automatically obtaining the optimal weight parameters from the training data.    \n",
     "![From human to machine learning to deep learning](./figures/dlscratch_humanmldl.png)"
    ]
   },
@@ -918,8 +916,6 @@
     }
    ],
    "source": [
-    "import sys, os\n",
-    "sys.path.append(os.pardir)\n",
     "import numpy as np\n",
     "from dataset.mnist import load_mnist\n",
     "\n",