《动手学深度学习》例子的PyTorch实现

d2l-pytorch

Github项目链接：

This project reproduces the bookDive Into Deep Learning, adapting the code from MXNet into PyTorch.

This project is adapted from the original Dive Into Deep Learning book by Aston Zhang, Zachary C. Lipton, Mu Li, Alex J. Smola and all the community contributors. We have made an effort to modify the book and convert the MXnet code snippets into PyTorch.

Note: Some ipynb notebooks may not be rendered perfectly in Github. We suggest the repo or using nbviewer to view the notebooks.

Contributing

Please feel free to open a Pull Request to contribute a notebook in PyTorch for the rest of the chapters. Before starting out with the notebook, open an issue with the name of the notebook in order to contribute for the same. We will assign that issue to you (if no one has been assigned earlier).

Strictly follow the naming conventions for the IPython Notebooks and the subsections.

Also, if you think there's any section that requires more/better explanation, please use the issue tracker to open an issue and let us know about the same. We'll get back as soon as possible.

Find some code that needs improvement and submit a pull request.

Find a reference that we missed and submit a pull request.

Try not to submit huge pull requests since this makes them hard to understand and incorporate. Better send several smaller ones.

Support

If you like this repo and find it useful, please consider (★) starring it, so that it can reach a broader audience.

References

[1] Original Book Dive Into Deep Learning -> Github Repo

[2] Deep Learning - The Straight Dope

[3] PyTorch - MXNet Cheatsheet

Cite

If you use this work or code for your research please cite the original book with the following bibtex entry.

Chapters

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