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编写于
10月 22, 2019
作者:
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ShusenTang
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@@ -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
)
...
...
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