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.
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