add content to README

README.md

@@ -11,6 +11,60 @@ Dive into Deep Learning with PyTorch code.
 ## 面向人群
 本项目面向对深度学习感兴趣，尤其是想使用PyTorch进行深度学习的童鞋。本项目并不要求你有任何深度学习或者机器学习的背景知识，你只需了解基础的数学和编程，如基础的线性代数、微分和概率，以及基础的Python编程。
 
+## 目录
+### [1. 深度学习简介](https://github.com/ShusenTang/Dive-into-DL-PyTorch/blob/master/docs/chapter01_DL-intro/deep-learning-intro.md)
+### 2. 预备知识
+[2.1 环境配置](https://github.com/ShusenTang/Dive-into-DL-PyTorch/blob/master/docs/chapter02_prerequisite/2.1_install.md)
+[2.2 数据操作](https://github.com/ShusenTang/Dive-into-DL-PyTorch/blob/master/docs/chapter02_prerequisite/2.2_tensor.md)
+[2.3 自动求梯度](https://github.com/ShusenTang/Dive-into-DL-PyTorch/blob/master/docs/chapter02_prerequisite/2.3_autograd.md)
+### 3. 深度学习基础
+[3.1 线性回归](https://github.com/ShusenTang/Dive-into-DL-PyTorch/blob/master/docs/chapter03_DL-basics/3.1_linear-regression.md)
+[3.2 线性回归的从零开始实现](https://github.com/ShusenTang/Dive-into-DL-PyTorch/blob/master/docs/chapter03_DL-basics/3.2_linear-regression-scratch.md)
+[3.3 线性回归的简洁实现](https://github.com/ShusenTang/Dive-into-DL-PyTorch/blob/master/docs/chapter03_DL-basics/3.3_linear-regression-pytorch.md)
+[3.4 softmax回归](https://github.com/ShusenTang/Dive-into-DL-PyTorch/blob/master/docs/chapter03_DL-basics/3.4_softmax-regression.md)
+[3.5 图像分类数据集（Fashion-MNIST）](https://github.com/ShusenTang/Dive-into-DL-PyTorch/blob/master/docs/chapter03_DL-basics/3.5_fashion-mnist.md)
+[3.6 softmax回归的从零开始实现](https://github.com/ShusenTang/Dive-into-DL-PyTorch/blob/master/docs/chapter03_DL-basics/3.6_softmax-regression-scratch.md)
+[3.7 softmax回归的简洁实现](https://github.com/ShusenTang/Dive-into-DL-PyTorch/blob/master/docs/chapter03_DL-basics/3.7_softmax-regression-pytorch.md)
+[3.8 多层感知机](https://github.com/ShusenTang/Dive-into-DL-PyTorch/blob/master/docs/chapter03_DL-basics/3.8_mlp.md)
+[3.9 多层感知机的从零开始实现](https://github.com/ShusenTang/Dive-into-DL-PyTorch/blob/master/docs/chapter03_DL-basics/3.9_mlp-scratch.md)
+[3.10 多层感知机的简洁实现](https://github.com/ShusenTang/Dive-into-DL-PyTorch/blob/master/docs/chapter03_DL-basics/3.10_mlp-pytorch.md)
+[3.11 模型选择、欠拟合和过拟合](https://github.com/ShusenTang/Dive-into-DL-PyTorch/blob/master/docs/chapter03_DL-basics/3.11_underfit-overfit.md)
+[3.12 权重衰减](https://github.com/ShusenTang/Dive-into-DL-PyTorch/blob/master/docs/chapter03_DL-basics/3.12_weight-decay.md)
+[3.13 丢弃法](https://github.com/ShusenTang/Dive-into-DL-PyTorch/blob/master/docs/chapter03_DL-basics/3.13_dropout.md)
+[3.14 正向传播、反向传播和计算图](https://github.com/ShusenTang/Dive-into-DL-PyTorch/blob/master/docs/chapter03_DL-basics/3.14_backprop.md)
+[3.15 数值稳定性和模型初始化](https://github.com/ShusenTang/Dive-into-DL-PyTorch/blob/master/docs/chapter03_DL-basics/3.15_numerical-stability-and-init.md)
+[3.16 实战Kaggle比赛：房价预测](https://github.com/ShusenTang/Dive-into-DL-PyTorch/blob/master/docs/chapter03_DL-basics/3.16_kaggle-house-price.md)
+### 4. 深度学习计算
+[4.1 模型构造](https://github.com/ShusenTang/Dive-into-DL-PyTorch/blob/master/docs/chapter04_DL_computation/4.1_model-construction.md)
+[4.2 模型参数的访问、初始化和共享](https://github.com/ShusenTang/Dive-into-DL-PyTorch/blob/master/docs/chapter04_DL_computation/4.2_parameters.md)
+[4.3 模型参数的延后初始化](https://github.com/ShusenTang/Dive-into-DL-PyTorch/blob/master/docs/chapter04_DL_computation/4.3_deferred-init.md)
+[4.4 自定义层](https://github.com/ShusenTang/Dive-into-DL-PyTorch/blob/master/docs/chapter04_DL_computation/4.4_custom-layer.md)
+[4.5 读取和存储](https://github.com/ShusenTang/Dive-into-DL-PyTorch/blob/master/docs/chapter04_DL_computation/4.5_read-write.md)
+[4.6 GPU计算](https://github.com/ShusenTang/Dive-into-DL-PyTorch/blob/master/docs/chapter04_DL_computation/4.6_use-gpu.md)
+### 5. 卷积神经网络
+[5.1 二维卷积层](https://github.com/ShusenTang/Dive-into-DL-PyTorch/blob/master/docs/chapter05_CNN/5.1_conv-layer.md)
+[5.2 填充和步幅](https://github.com/ShusenTang/Dive-into-DL-PyTorch/blob/master/docs/chapter05_CNN/5.2_padding-and-strides.md)
+[5.3 多输入通道和多输出通道](https://github.com/ShusenTang/Dive-into-DL-PyTorch/blob/master/docs/chapter05_CNN/5.3_channels.md)
+[5.4 池化层](https://github.com/ShusenTang/Dive-into-DL-PyTorch/blob/master/docs/chapter05_CNN/5.4_pooling.md)
+[5.5 卷积神经网络（LeNet）](https://github.com/ShusenTang/Dive-into-DL-PyTorch/blob/master/docs/chapter05_CNN/5.5_lenet.md)
+[5.6 深度卷积神经网络（AlexNet）](https://github.com/ShusenTang/Dive-into-DL-PyTorch/blob/master/docs/chapter05_CNN/5.6_alexnet.md)
+[5.7 使用重复元素的网络（VGG）](https://github.com/ShusenTang/Dive-into-DL-PyTorch/blob/master/docs/chapter05_CNN/5.7_vgg.md)
+[5.8 网络中的网络（NiN）](https://github.com/ShusenTang/Dive-into-DL-PyTorch/blob/master/docs/chapter05_CNN/5.8_nin.md)
+[5.9 含并行连结的网络（GoogLeNet）](https://github.com/ShusenTang/Dive-into-DL-PyTorch/blob/master/docs/chapter05_CNN/5.9_googlenet.md)
+[5.10 批量归一化](https://github.com/ShusenTang/Dive-into-DL-PyTorch/blob/master/docs/chapter05_CNN/5.10_batch-norm.md)
+[5.11 残差网络（ResNet）](https://github.com/ShusenTang/Dive-into-DL-PyTorch/blob/master/docs/chapter05_CNN/5.11_resnet.md)
+[5.12 稠密连接网络（DenseNet）](https://github.com/ShusenTang/Dive-into-DL-PyTorch/blob/master/docs/chapter05_CNN/5.12_densenet.md)
+### 6. 循环神经网络
+[6.1 语言模型](https://github.com/ShusenTang/Dive-into-DL-PyTorch/blob/master/docs/chapter06_RNN/6.1_lang-model.md)
+[6.2 循环神经网络](https://github.com/ShusenTang/Dive-into-DL-PyTorch/blob/master/docs/chapter06_RNN/6.2_rnn.md)
+[6.3 语言模型数据集（周杰伦专辑歌词）](https://github.com/ShusenTang/Dive-into-DL-PyTorch/blob/master/docs/chapter06_RNN/6.3_lang-model-dataset.md)
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+持续更新中......
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 ## 原书地址
 ### 中文版[动手学深度学习](https://zh.d2l.ai/)
 项目地址: https://github.com/d2l-ai/d2l-zh