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https://github.com/spro/practical-pytorch
Go to https://github.com/pytorch/tutorials - this repo is deprecated and no longer maintained
https://github.com/spro/practical-pytorch
natural-language-generation natural-language-processing nlg nlp seq2seq
Last synced: 3 months ago
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Go to https://github.com/pytorch/tutorials - this repo is deprecated and no longer maintained
- Host: GitHub
- URL: https://github.com/spro/practical-pytorch
- Owner: spro
- License: mit
- Archived: true
- Created: 2017-01-22T01:28:10.000Z (almost 8 years ago)
- Default Branch: master
- Last Pushed: 2021-07-01T04:34:00.000Z (over 3 years ago)
- Last Synced: 2024-07-31T21:54:34.360Z (6 months ago)
- Topics: natural-language-generation, natural-language-processing, nlg, nlp, seq2seq
- Language: Jupyter Notebook
- Homepage:
- Size: 1.63 MB
- Stars: 4,513
- Watchers: 147
- Forks: 1,102
- Open Issues: 90
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
**These tutorials have been merged into [the official PyTorch tutorials](https://github.com/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](https://github.com/spro/practical-pytorch/blob/master/char-rnn-classification/char-rnn-classification.ipynb)
* [Generating Shakespeare with a Character-Level RNN](https://github.com/spro/practical-pytorch/blob/master/char-rnn-generation/char-rnn-generation.ipynb)
* [Generating Names with a Conditional Character-Level RNN](https://github.com/spro/practical-pytorch/blob/master/conditional-char-rnn/conditional-char-rnn.ipynb)
* [Translation with a Sequence to Sequence Network and Attention](https://github.com/spro/practical-pytorch/blob/master/seq2seq-translation/seq2seq-translation.ipynb)
* [Exploring Word Vectors with GloVe](https://github.com/spro/practical-pytorch/blob/master/glove-word-vectors/glove-word-vectors.ipynb)
* *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](https://www.continuum.io/anaconda-overview):
```
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](http://pytorch.org/tutorials/) for more tutorials (some of these tutorials are included there)
* [Deep Learning with PyTorch: A 60-minute Blitz](http://pytorch.org/tutorials/beginner/deep_learning_60min_blitz.html) to get started with PyTorch in general
* [Introduction to PyTorch for former Torchies](https://github.com/pytorch/tutorials/blob/master/Introduction%20to%20PyTorch%20for%20former%20Torchies.ipynb) if you are a former Lua Torch user
* [jcjohnson's PyTorch examples](https://github.com/jcjohnson/pytorch-examples) for a more in depth overview (including custom modules and autograd functions)
### Recurrent Neural Networks
* [The Unreasonable Effectiveness of Recurrent Neural Networks](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) shows a bunch of real life examples
* [Deep Learning, NLP, and Representations](http://colah.github.io/posts/2014-07-NLP-RNNs-Representations/) for an overview on word embeddings and RNNs for NLP
* [Understanding LSTM Networks](http://colah.github.io/posts/2015-08-Understanding-LSTMs/) 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](http://arxiv.org/abs/1406.1078)
* [Sequence to Sequence Learning with Neural Networks](http://arxiv.org/abs/1409.3215)
### Attention models
* [Neural Machine Translation by Jointly Learning to Align and Translate](https://arxiv.org/abs/1409.0473)
* [Effective Approaches to Attention-based Neural Machine Translation](https://arxiv.org/abs/1508.04025)
### Other RNN uses
* [A Neural Conversational Model](http://arxiv.org/abs/1506.05869)
### Other PyTorch tutorials
* [Deep Learning For NLP In PyTorch](https://github.com/rguthrie3/DeepLearningForNLPInPytorch)
## Feedback
If you have ideas or find mistakes [please leave a note](https://github.com/spro/practical-pytorch/issues/new).