Natural Language Processing with PyTorch

Build Intelligent Language Applications Using Deep Learning
By Delip Rao and Brian McMahan

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