update README content

docs/README.md

@@ -18,101 +18,94 @@ There are some differences between the [Chinese](https://zh.d2l.ai/) and [Englis
 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)    
-[6.4 循环神经网络的从零开始实现](https://github.com/ShusenTang/Dive-into-DL-PyTorch/blob/master/docs/chapter06_RNN/6.4_rnn-scratch.md)             
-[6.5 循环神经网络的简洁实现](https://github.com/ShusenTang/Dive-into-DL-PyTorch/blob/master/docs/chapter06_RNN/6.5_rnn-pytorch.md)              
-[6.6 通过时间反向传播](https://github.com/ShusenTang/Dive-into-DL-PyTorch/blob/master/docs/chapter06_RNN/6.6_bptt.md)     
-[6.7 门控循环单元（GRU）](https://github.com/ShusenTang/Dive-into-DL-PyTorch/blob/master/docs/chapter06_RNN/6.7_gru.md)              
-[6.8 长短期记忆（LSTM）](https://github.com/ShusenTang/Dive-into-DL-PyTorch/blob/master/docs/chapter06_RNN/6.8_lstm.md)      
-[6.9 深度循环神经网络](https://github.com/ShusenTang/Dive-into-DL-PyTorch/blob/master/docs/chapter06_RNN/6.9_deep-rnn.md)              
-[6.10 双向循环神经网络](https://github.com/ShusenTang/Dive-into-DL-PyTorch/blob/master/docs/chapter06_RNN/6.10_bi-rnn.md)   
-
-### 7. 优化算法
-[7.1 优化与深度学习](https://github.com/ShusenTang/Dive-into-DL-PyTorch/blob/master/docs/chapter07_optimization/7.1_optimization-intro.md)              
-[7.2 梯度下降和随机梯度下降](https://github.com/ShusenTang/Dive-into-DL-PyTorch/blob/master/docs/chapter07_optimization/7.2_gd-sgd.md)       
-[7.3 小批量随机梯度下降](https://github.com/ShusenTang/Dive-into-DL-PyTorch/blob/master/docs/chapter07_optimization/7.3_minibatch-sgd.md)         
-[7.4 动量法](https://github.com/ShusenTang/Dive-into-DL-PyTorch/blob/master/docs/chapter07_optimization/7.4_momentum.md)      
-[7.5 AdaGrad算法](https://github.com/ShusenTang/Dive-into-DL-PyTorch/blob/master/docs/chapter07_optimization/7.5_adagrad.md)     
-[7.6 RMSProp算法](https://github.com/ShusenTang/Dive-into-DL-PyTorch/blob/master/docs/chapter07_optimization/7.6_rmsprop.md)       
-[7.7 AdaDelta算法](https://github.com/ShusenTang/Dive-into-DL-PyTorch/blob/master/docs/chapter07_optimization/7.7_adadelta.md)      
-[7.8 Adam算法](https://github.com/ShusenTang/Dive-into-DL-PyTorch/blob/master/docs/chapter07_optimization/7.8_adam.md)  
-
-### 8. 计算性能
-[8.1 命令式和符号式混合编程](https://github.com/ShusenTang/Dive-into-DL-PyTorch/blob/master/docs/chapter08_computational-performance/8.1_hybridize.md)      
-[8.2 异步计算](https://github.com/ShusenTang/Dive-into-DL-PyTorch/blob/master/docs/chapter08_computational-performance/8.2_async-computation.md)         
-[8.3 自动并行计算](https://github.com/ShusenTang/Dive-into-DL-PyTorch/blob/master/docs/chapter08_computational-performance/8.3_auto-parallelism.md)    
-[8.4 多GPU计算](https://github.com/ShusenTang/Dive-into-DL-PyTorch/blob/master/docs/chapter08_computational-performance/8.4_multiple-gpus.md)     
-
-### 9. 计算机视觉
-[9.1 图像增广](https://github.com/ShusenTang/Dive-into-DL-PyTorch/blob/master/docs/chapter09_computer-vision/9.1_image-augmentation.md)  
-[9.2 微调](https://github.com/ShusenTang/Dive-into-DL-PyTorch/blob/master/docs/chapter09_computer-vision/9.2_fine-tuning.md)     
-[9.3 目标检测和边界框](https://github.com/ShusenTang/Dive-into-DL-PyTorch/blob/master/docs/chapter09_computer-vision/9.3_bounding-box.md)           
-[9.4 锚框](https://github.com/ShusenTang/Dive-into-DL-PyTorch/blob/master/docs/chapter09_computer-vision/9.4_anchor.md)          
-
-
-待更新...
-
-
-### 10. 自然语言处理
-[10.1 词嵌入（word2vec）](https://github.com/ShusenTang/Dive-into-DL-PyTorch/blob/master/docs/chapter10_natural-language-processing/10.1_word2vec.md)      
-[10.2 近似训练](https://github.com/ShusenTang/Dive-into-DL-PyTorch/blob/master/docs/chapter10_natural-language-processing/10.2_approx-training.md)          
-[10.3 word2vec的实现](https://github.com/ShusenTang/Dive-into-DL-PyTorch/blob/master/docs/chapter10_natural-language-processing/10.3_word2vec-pytorch.md)                       
-[10.4 子词嵌入（fastText）](https://github.com/ShusenTang/Dive-into-DL-PyTorch/blob/master/docs/chapter10_natural-language-processing/10.4_fasttext.md)              
-[10.5 全局向量的词嵌入（GloVe）](https://github.com/ShusenTang/Dive-into-DL-PyTorch/blob/master/docs/chapter10_natural-language-processing/10.5_glove.md)           
-[10.6 求近义词和类比词](https://github.com/ShusenTang/Dive-into-DL-PyTorch/blob/master/docs/chapter10_natural-language-processing/10.6_similarity-analogy.md)                   
-[10.7 文本情感分类：使用循环神经网络](https://github.com/ShusenTang/Dive-into-DL-PyTorch/blob/master/docs/chapter10_natural-language-processing/10.7_sentiment-analysis-rnn.md)                        
-[10.8 文本情感分类：使用卷积神经网络（textCNN）](https://github.com/ShusenTang/Dive-into-DL-PyTorch/blob/master/docs/chapter10_natural-language-processing/10.8_sentiment-analysis-cnn.md)                   
-[10.9 编码器—解码器（seq2seq）](https://github.com/ShusenTang/Dive-into-DL-PyTorch/blob/master/docs/chapter10_natural-language-processing/10.9_seq2seq.md)                   
-[10.10 束搜索](https://github.com/ShusenTang/Dive-into-DL-PyTorch/blob/master/docs/chapter10_natural-language-processing/10.10_beam-search.md)                             
-[10.11 注意力机制](https://github.com/ShusenTang/Dive-into-DL-PyTorch/blob/master/docs/chapter10_natural-language-processing/10.11_attention.md)                         
-[10.12 机器翻译](https://github.com/ShusenTang/Dive-into-DL-PyTorch/blob/master/docs/chapter10_natural-language-processing/10.12_machine-translation.md)
-
-
+* [简介]()
+* [阅读指南](read_guide.md)
+* [1. 深度学习简介](chapter01_DL-intro/deep-learning-intro.md)
+* 2\. 预备知识
+   * [2.1 环境配置](chapter02_prerequisite/2.1_install.md)
+   * [2.2 数据操作](chapter02_prerequisite/2.2_tensor.md)
+   * [2.3 自动求梯度](chapter02_prerequisite/2.3_autograd.md)
+* 3\. 深度学习基础
+   * [3.1 线性回归](chapter03_DL-basics/3.1_linear-regression.md)
+   * [3.2 线性回归的从零开始实现](chapter03_DL-basics/3.2_linear-regression-scratch.md)
+   * [3.3 线性回归的简洁实现](chapter03_DL-basics/3.3_linear-regression-pytorch.md)
+   * [3.4 softmax回归](chapter03_DL-basics/3.4_softmax-regression.md)
+   * [3.5 图像分类数据集（Fashion-MNIST）](chapter03_DL-basics/3.5_fashion-mnist.md)
+   * [3.6 softmax回归的从零开始实现](chapter03_DL-basics/3.6_softmax-regression-scratch.md)
+   * [3.7 softmax回归的简洁实现](chapter03_DL-basics/3.7_softmax-regression-pytorch.md)
+   * [3.8 多层感知机](chapter03_DL-basics/3.8_mlp.md)
+   * [3.9 多层感知机的从零开始实现](chapter03_DL-basics/3.9_mlp-scratch.md)
+   * [3.10 多层感知机的简洁实现](chapter03_DL-basics/3.10_mlp-pytorch.md)
+   * [3.11 模型选择、欠拟合和过拟合](chapter03_DL-basics/3.11_underfit-overfit.md)
+   * [3.12 权重衰减](chapter03_DL-basics/3.12_weight-decay.md)
+   * [3.13 丢弃法](chapter03_DL-basics/3.13_dropout.md)
+   * [3.14 正向传播、反向传播和计算图](chapter03_DL-basics/3.14_backprop.md)
+   * [3.15 数值稳定性和模型初始化](chapter03_DL-basics/3.15_numerical-stability-and-init.md)
+   * [3.16 实战Kaggle比赛：房价预测](chapter03_DL-basics/3.16_kaggle-house-price.md)
+* 4\. 深度学习计算
+   * [4.1 模型构造](chapter04_DL_computation/4.1_model-construction.md)
+   * [4.2 模型参数的访问、初始化和共享](chapter04_DL_computation/4.2_parameters.md)
+   * [4.3 模型参数的延后初始化](chapter04_DL_computation/4.3_deferred-init.md)
+   * [4.4 自定义层](chapter04_DL_computation/4.4_custom-layer.md)
+   * [4.5 读取和存储](chapter04_DL_computation/4.5_read-write.md)
+   * [4.6 GPU计算](chapter04_DL_computation/4.6_use-gpu.md)
+* 5\. 卷积神经网络
+   * [5.1 二维卷积层](chapter05_CNN/5.1_conv-layer.md)
+   * [5.2 填充和步幅](chapter05_CNN/5.2_padding-and-strides.md)
+   * [5.3 多输入通道和多输出通道](chapter05_CNN/5.3_channels.md)
+   * [5.4 池化层](chapter05_CNN/5.4_pooling.md)
+   * [5.5 卷积神经网络（LeNet）](chapter05_CNN/5.5_lenet.md)
+   * [5.6 深度卷积神经网络（AlexNet）](chapter05_CNN/5.6_alexnet.md)
+   * [5.7 使用重复元素的网络（VGG）](chapter05_CNN/5.7_vgg.md)
+   * [5.8 网络中的网络（NiN）](chapter05_CNN/5.8_nin.md)
+   * [5.9 含并行连结的网络（GoogLeNet）](chapter05_CNN/5.9_googlenet.md)
+   * [5.10 批量归一化](chapter05_CNN/5.10_batch-norm.md)
+   * [5.11 残差网络（ResNet）](chapter05_CNN/5.11_resnet.md)
+   * [5.12 稠密连接网络（DenseNet）](chapter05_CNN/5.12_densenet.md)
+* 6\. 循环神经网络
+   * [6.1 语言模型](chapter06_RNN/6.1_lang-model.md)
+   * [6.2 循环神经网络](chapter06_RNN/6.2_rnn.md)
+   * [6.3 语言模型数据集（周杰伦专辑歌词）](chapter06_RNN/6.3_lang-model-dataset.md)
+   * [6.4 循环神经网络的从零开始实现](chapter06_RNN/6.4_rnn-scratch.md)
+   * [6.5 循环神经网络的简洁实现](chapter06_RNN/6.5_rnn-pytorch.md)
+   * [6.6 通过时间反向传播](chapter06_RNN/6.6_bptt.md)
+   * [6.7 门控循环单元（GRU）](chapter06_RNN/6.7_gru.md)
+   * [6.8 长短期记忆（LSTM）](chapter06_RNN/6.8_lstm.md)
+   * [6.9 深度循环神经网络](chapter06_RNN/6.9_deep-rnn.md)
+   * [6.10 双向循环神经网络](chapter06_RNN/6.10_bi-rnn.md)
+* 7\. 优化算法
+   * [7.1 优化与深度学习](chapter07_optimization/7.1_optimization-intro.md)
+   * [7.2 梯度下降和随机梯度下降](chapter07_optimization/7.2_gd-sgd.md)
+   * [7.3 小批量随机梯度下降](chapter07_optimization/7.3_minibatch-sgd.md)
+   * [7.4 动量法](chapter07_optimization/7.4_momentum.md)
+   * [7.5 AdaGrad算法](chapter07_optimization/7.5_adagrad.md)
+   * [7.6 RMSProp算法](chapter07_optimization/7.6_rmsprop.md)
+   * [7.7 AdaDelta算法](chapter07_optimization/7.7_adadelta.md)
+   * [7.8 Adam算法](chapter07_optimization/7.8_adam.md)
+* 8\. 计算性能
+   * [8.1 命令式和符号式混合编程](chapter08_computational-performance/8.1_hybridize.md)
+   * [8.2 异步计算](chapter08_computational-performance/8.2_async-computation.md)
+   * [8.3 自动并行计算](chapter08_computational-performance/8.3_auto-parallelism.md)
+   * [8.4 多GPU计算](chapter08_computational-performance/8.4_multiple-gpus.md)
+* 9\. 计算机视觉
+   * [9.1 图像增广](chapter09_computer-vision/9.1_image-augmentation.md)
+   * [9.2 微调](chapter09_computer-vision/9.2_fine-tuning.md)
+   * [9.3 目标检测和边界框](chapter09_computer-vision/9.3_bounding-box.md)
+   * [9.4 锚框](chapter09_computer-vision/9.4_anchor.md)
+   * 待更新...
+* 10\. 自然语言处理
+   * [10.1 词嵌入（word2vec）](chapter10_natural-language-processing/10.1_word2vec.md)
+   * [10.2 近似训练](chapter10_natural-language-processing/10.2_approx-training.md)
+   * [10.3 word2vec的实现](chapter10_natural-language-processing/10.3_word2vec-pytorch.md)
+   * [10.4 子词嵌入（fastText）](chapter10_natural-language-processing/10.4_fasttext.md)
+   * [10.5 全局向量的词嵌入（GloVe）](chapter10_natural-language-processing/10.5_glove.md)
+   * [10.6 求近义词和类比词](chapter10_natural-language-processing/10.6_similarity-analogy.md)
+   * [10.7 文本情感分类：使用循环神经网络](chapter10_natural-language-processing/10.7_sentiment-analysis-rnn.md)
+   * [10.8 文本情感分类：使用卷积神经网络（textCNN）](chapter10_natural-language-processing/10.8_sentiment-analysis-cnn.md)
+   * [10.9 编码器—解码器（seq2seq）](chapter10_natural-language-processing/10.9_seq2seq.md)
+   * [10.10 束搜索](chapter10_natural-language-processing/10.10_beam-search.md)
+   * [10.11 注意力机制](chapter10_natural-language-processing/10.11_attention.md)
+   * [10.12 机器翻译](chapter10_natural-language-processing/10.12_machine-translation.md)