ankitvad/practical-pytorch

PyTorch tutorials demonstrating modern techniques with readable code

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

Note: Check back soon for updates to support PyTorch 0.4 and 1.0.

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.