Natural Language Processing with PyTorch

Build Intelligent Language Applications Using Deep Learning
By Delip Rao and Brian McMahan
Examples with refactored code by Risto Hinno

Welcome. This is a companion repository for the book Natural Language Processing with PyTorch: Build Intelligent Language Applications Using Deep Learning.

I have refactored original code a bit and fixed some bugs. Most notable changes are adding src folder to each chapter and learner class for training/using the model. There are also notebooks for showing how to use refactored code.

Table of Contents

• Get Started!
• Chapter 1: Introduction
  • PyTorch Basics
• Chapter 2: A Quick Tour of NLP
• Chapter 3: Foundational Components of Neural Networks
  • In-text examples
  • Diving deep into supervised training
  • Classifying sentiment of restaurant reviews using a Perceptron
• Chapter 4: Feed-forward Networks for NLP
  • 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
  • Using Pretrained Embeddings
  • Learning Continous Bag-of-words Embeddings (CBOW)
  • Transfer Learning using Pre-trained Embeddings
• Chapter 6: Sequence Modeling for NLP
  • A sequence representation for Surnames
• Chapter 7: Intermediate Sequence Modeling for NLP
  • Generating novel surnames from sequence representations
  • Uncondition generation
  • Conditioned generation
• Chapter 8: Advanced Sequence Modeling for NLP
  • Understanding PackedSequences
  • Sequence to Sequence Learning
  • Attention
  • Neural Machine Translation
• Chapter 9: Classics, Frontiers, Next Steps