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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "e65d2ddd",
+   "metadata": {
+    "origin_pos": 0
+   },
+   "source": [
+    "# 线性神经网络\n",
+    ":label:`chap_linear`\n",
+    "\n",
+    "在介绍深度神经网络之前，我们需要了解神经网络训练的基础知识。\n",
+    "本章我们将介绍神经网络的整个训练过程，\n",
+    "包括：定义简单的神经网络架构、数据处理、指定损失函数和如何训练模型。\n",
+    "为了更容易学习，我们将从经典算法————*线性*神经网络开始，介绍神经网络的基础知识。\n",
+    "经典统计学习技术中的线性回归和softmax回归可以视为线性神经网络，\n",
+    "这些知识将为本书其他部分中更复杂的技术奠定基础。\n",
+    "\n",
+    ":begin_tab:toc\n",
+    " - [linear-regression](linear-regression.ipynb)\n",
+    " - [linear-regression-scratch](linear-regression-scratch.ipynb)\n",
+    " - [linear-regression-concise](linear-regression-concise.ipynb)\n",
+    " - [softmax-regression](softmax-regression.ipynb)\n",
+    " - [image-classification-dataset](image-classification-dataset.ipynb)\n",
+    " - [softmax-regression-scratch](softmax-regression-scratch.ipynb)\n",
+    " - [softmax-regression-concise](softmax-regression-concise.ipynb)\n",
+    ":end_tab:\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  },
+  "required_libs": []
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}