Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
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: 3 months ago
JSON representation
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: 2024-06-22T23:51:41.722Z (6 months 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: 1,960
- Watchers: 54
- Forks: 800
- Open Issues: 25
-
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 McMahanWelcome. 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