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重磅干货，第一时间送达 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： https://zh./ 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 Ch03 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 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 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：
https://github.com/dsgiitr/d2l-pytorch
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