李沐，亚马逊 AI 主任科学家，名声在外！半年前，由李沐、Aston Zhang 等人合力打造的《动手学深度学习》正式上线，免费供大家阅读。这是一本面向中文读者的能运行、可讨论的深度学习教科书！
之前，红色石头就分享过这份资源，再次附上：
在线预览地址：
GitHub 项目地址：
https://github.com/d2l-ai/d2l-zh
课程视频地址：
https://space.bilibili.com/209599371/channel/detail?cid=23541
我们知道，作为 MXNet 的作者之一，李沐的这本《动手学深度学习》也是使用 MXNet 框架写成的。但是很多入坑机器学习的萌新们使用的却是 PyTorch。如果有教材对应的 PyTorch 实现代码就更好了！
撒花！今天就给大家带来这本书的 PyTorch 实现源码。最近，来自印度理工学院的数据科学小组，把《动手学深度学习》从 MXNet “翻译”成了 PyTorch，经过 3 个月的努力，这个项目已经基本完成，并登上了 GitHub 热榜。
首先放上这份资源的 GitHub 地址：
https://github.com/dsgiitr/d2l-pytorch
详细目录如下：
- Ch02 Installation
- Installation
- Ch03 Introduction
- Introduction
- Ch04 The Preliminaries: A Crashcourse
- 4.1 Data Manipulation
- 4.2 Linear Algebra
- 4.3 Automatic Differentiation
- 4.4 Probability and Statistics
- 4.5 Naive Bayes Classification
- 4.6 Documentation
- Ch05 Linear Neural Networks
- 5.1 Linear Regression
- 5.2 Linear Regression Implementation from Scratch
- 5.3 Concise Implementation of Linear Regression
- 5.4 Softmax Regression
- 5.5 Image Classification Data (Fashion-MNIST)
- 5.6 Implementation of Softmax Regression from Scratch
- 5.7 Concise Implementation of Softmax Regression
- Ch06 Multilayer Perceptrons
- 6.1 Multilayer Perceptron
- 6.2 Implementation of Multilayer Perceptron from Scratch
- 6.3 Concise Implementation of Multilayer Perceptron
- 6.4 Model Selection Underfitting and Overfitting
- 6.5 Weight Decay
- 6.6 Dropout
- 6.7 Forward Propagation Backward Propagation and Computational Graphs
- 6.8 Numerical Stability and Initialization
- 6.9 Considering the Environment
- 6.10 Predicting House Prices on Kaggle
- Ch07 Deep Learning Computation
- 7.1 Layers and Blocks
- 7.2 Parameter Management
- 7.3 Deferred Initialization
- 7.4 Custom Layers
- 7.5 File I/O
- 7.6 GPUs
- Ch08 Convolutional Neural Networks
- 8.1 From Dense Layers to Convolutions
- 8.2 Convolutions for Images
- 8.3 Padding and Stride
- 8.4 Multiple Input and Output Channels
- 8.5 Pooling
- 8.6 Convolutional Neural Networks (LeNet)
- Ch09 Modern Convolutional Networks
- 9.1 Deep Convolutional Neural Networks (AlexNet)
- 9.2 Networks Using Blocks (VGG)
- 9.3 Network in Network (NiN)
- 9.4 Networks with Parallel Concatenations (GoogLeNet)
- 9.5 Batch Normalization
- 9.6 Residual Networks (ResNet)
- 9.7 Densely Connected Networks (DenseNet)
- Ch10 Recurrent Neural Networks
- 10.1 Sequence Models
- 10.2 Language Models
- 10.3 Recurrent Neural Networks
- 10.4 Text Preprocessing
- 10.5 Implementation of Recurrent Neural Networks from Scratch
- 10.6 Concise Implementation of Recurrent Neural Networks
- 10.7 Backpropagation Through Time
- 10.8 Gated Recurrent Units (GRU)
- 10.9 Long Short Term Memory (LSTM)
- 10.10 Deep Recurrent Neural Networks
- 10.11 Bidirectional Recurrent Neural Networks
- 10.12 Machine Translation and DataSets
- 10.13 Encoder-Decoder Architecture
- 10.14 Sequence to Sequence
- 10.15 Beam Search
- Ch11 Attention Mechanism
- 11.1 Attention Mechanism
- 11.2 Sequence to Sequence with Attention Mechanism
- 11.3 Transformer
- Ch12 Optimization Algorithms
- 12.1 Optimization and Deep Learning
- 12.2 Convexity
- 12.3 Gradient Descent
- 12.4 Stochastic Gradient Descent
- 12.5 Mini-batch Stochastic Gradient Descent
- 12.6 Momentum
- 12.7 Adagrad
- 12.8 RMSProp
- 12.9 Adadelta
- 12.10 Adam
其中，每一小节都是可以运行的 Jupyter 记事本，你可以自由修改代码和超参数来获取及时反馈，从而积累深度学习的实战经验。
目前，PyTorch 代码还有 6 个小节没有完成，但整体的完成度已经很高了！开发团队希望更多的爱好者加入进来，贡献一份力量！
最后，再次附上 GitHub 地址：
