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https://github.com/delip/PyTorchNLPBook

Code and data accompanying Natural Language Processing with PyTorch published by O'Reilly Media https://amzn.to/3JUgR2L
https://github.com/delip/PyTorchNLPBook

deep-learning deep-neural-networks natural-language-processing neural-machine-translation neural-networks nlp pytorch pytorch-nlp pytorch-tutorial

Last synced: 19 days ago
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Code and data accompanying Natural Language Processing with PyTorch published by O'Reilly Media https://amzn.to/3JUgR2L

Host: GitHub
URL: https://github.com/delip/PyTorchNLPBook
Owner: delip
License: apache-2.0
Created: 2019-01-24T01:18:13.000Z (almost 6 years ago)
Default Branch: master
Last Pushed: 2023-03-12T18:45:11.000Z (almost 2 years ago)
Last Synced: 2024-12-21T00:05:50.881Z (25 days ago)
Topics: deep-learning, deep-neural-networks, natural-language-processing, neural-machine-translation, neural-networks, nlp, pytorch, pytorch-nlp, pytorch-tutorial
Language: Jupyter Notebook
Homepage:
Size: 7.73 MB
Stars: 1,998
Watchers: 55
Forks: 809
Open Issues: 27
Metadata Files:
- Readme: README.md
- License: LICENSE

Awesome Lists containing this project

README

        # Natural Language Processing with PyTorch

_Build Intelligent Language Applications Using Deep Learning_


By Delip Rao and Brian McMahan

Welcome. This is a companion repository for the book [Natural Language Processing with PyTorch: Build Intelligent Language Applications Using Deep Learning](https://www.amazon.com/Natural-Language-Processing-PyTorch-Applications/dp/1491978236/).

Table of Contents

=================

* Get Started!

* [Chapter 1: Introduction](https://github.com/joosthub/PyTorchNLPBook/tree/master/chapters/chapter_1)

  * PyTorch Basics

* Chapter 2: A Quick Tour of NLP

* [Chapter 3: Foundational Components of Neural Networks](https://github.com/joosthub/PyTorchNLPBook/tree/master/chapters/chapter_3)

  * In-text examples

  * Diving deep into supervised training

  * Classifying sentiment of restaurant reviews using a Perceptron

* [Chapter 4: Feed-forward Networks for NLP](https://github.com/joosthub/PyTorchNLPBook/tree/master/chapters/chapter_4)

  * Limitations of the Perceptron

  * Introducing Multi-layer Perceptrons (MLPs)

  * Introducing Convolutional Neural Networks (CNNs)

  * Surname Classification with an MLP

  * Surname Classification with a CNN

* [Chapter 5: Embedding Words and Types](https://github.com/joosthub/PyTorchNLPBook/tree/master/chapters/chapter_5)

  * Using Pretrained Embeddings

  * Learning Continous Bag-of-words Embeddings (CBOW)

  * Transfer Learning using Pre-trained Embeddings

* [Chapter 6: Sequence Modeling for NLP](https://github.com/joosthub/PyTorchNLPBook/tree/master/chapters/chapter_6)

  * A sequence representation for Surnames

* [Chapter 7: Intermediate Sequence Modeling for NLP](https://github.com/joosthub/PyTorchNLPBook/tree/master/chapters/chapter_7)

  * Generating novel surnames from sequence representations

  * Uncondition generation

  * Conditioned generation

* [Chapter 8: Advanced Sequence Modeling for NLP](https://github.com/joosthub/PyTorchNLPBook/tree/master/chapters/chapter_8)

  * Understanding PackedSequences 

  * Sequence to Sequence Learning

  * Attention

  * Neural Machine Translation

* Chapter 9: Classics, Frontiers, Next Steps