oukaishen / practical-pytorch

Project based PyTorch tutorials, focused on using neural networks (especially RNNs) for natural language tasks

These tutorials have been merged into the official PyTorch tutorials. Please go there for better maintained versions of these tutorials compatible with newer versions of PyTorch.

---

Learn PyTorch with project-based tutorials. These tutorials demonstrate modern techniques with readable code and use regular data from the internet.

Tutorials

Series 1: RNNs for NLP

Applying recurrent neural networks to natural language tasks, from classification to generation.

• Classifying Names with a Character-Level RNN
• Generating Shakespeare with a Character-Level RNN
• Generating Names with a Conditional Character-Level RNN
• Translation with a Sequence to Sequence Network and Attention
• Exploring Word Vectors with GloVe
• WIP Sentiment Analysis with a Word-Level RNN and GloVe Embeddings

Series 2: RNNs for timeseries data

• WIP Predicting discrete events with an RNN

Get Started

The quickest way to run these on a fresh Linux or Mac machine is to install Anaconda:

curl -LO https://repo.continuum.io/archive/Anaconda3-4.3.0-Linux-x86_64.sh
bash Anaconda3-4.3.0-Linux-x86_64.sh

Then install PyTorch:

conda install pytorch -c soumith

Then clone this repo and start Jupyter Notebook:

git clone http://github.com/spro/practical-pytorch
cd practical-pytorch
jupyter notebook

Recommended Reading

PyTorch basics

• http://pytorch.org/ For installation instructions
• Offical PyTorch tutorials for more tutorials (some of these tutorials are included there)
• Deep Learning with PyTorch: A 60-minute Blitz to get started with PyTorch in general
• Introduction to PyTorch for former Torchies if you are a former Lua Torch user
• jcjohnson's PyTorch examples for a more in depth overview (including custom modules and autograd functions)

Recurrent Neural Networks

• The Unreasonable Effectiveness of Recurrent Neural Networks shows a bunch of real life examples
• Deep Learning, NLP, and Representations for an overview on word embeddings and RNNs for NLP
• Understanding LSTM Networks is about LSTMs work specifically, but also informative about RNNs in general

Machine translation

• Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
• Sequence to Sequence Learning with Neural Networks

Attention models

• Neural Machine Translation by Jointly Learning to Align and Translate
• Effective Approaches to Attention-based Neural Machine Translation

Other RNN uses

• A Neural Conversational Model

Other PyTorch tutorials

• Deep Learning For NLP In PyTorch

Feedback

If you have ideas or find mistakes please leave a note.