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These tutorials have been merged intothe 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.

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

• WIP Predicting discrete events with an RNN

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

curl -LO https://repo.continuum.io/archive/Anaconda3-4.3.0-Linux-x86_64.shbash 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-pytorchcd practical-pytorchjupyter notebook

• 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)

• 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

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

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

• A Neural Conversational Model

• Deep Learning For NLP In PyTorch

If you have ideas or find mistakesplease leave a note.