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Full Description

Neural networks are a family of powerful machine learning models and this book focuses on their application to natural language data.

The first half of the book (Parts I and II) covers the basics of supervised machine learning and feed-forward neural networks, the basics of working with machine learning over language data, and the use of vector-based rather than symbolic representations for words. It also covers the computation-graph abstraction, which allows to easily define and train arbitrary neural networks, and is the basis behind the design of contemporary neural network software libraries.

The second part of the book (Parts III and IV) introduces more specialized neural network architectures, including 1D convolutional neural networks, recurrent neural networks, conditioned-generation models, and attention-based models. These architectures and techniques are the driving force behind state-of-the-art algorithms for machine translation, syntactic parsing, and many other applications. Finally, we also discuss tree-shaped networks, structured prediction, and the prospects of multi-task learning.

Contents

Preface
Acknowledgments
Introduction
Learning Basics and Linear Models
Learning Basics and Linear Models
From Linear Models to Multi-layer Perceptrons
Feed-forward Neural Networks
Neural Network Training
Features for Textual Data
Case Studies of NLP Features
From Textual Features to Inputs
Language Modeling
Pre-trained Word Representations
Pre-trained Word Representations
Using Word Embeddings
Case Study: A Feed-forward Architecture for Sentence
Case Study: A Feed-forward Architecture for Sentence Meaning Inference
Ngram Detectors: Convolutional Neural Networks
Recurrent Neural Networks: Modeling Sequences and Stacks
Concrete Recurrent Neural Network Architectures
Modeling with Recurrent Networks
Modeling with Recurrent Networks
Conditioned Generation
Modeling Trees with Recursive Neural Networks
Modeling Trees with Recursive Neural Networks
Structured Output Prediction
Cascaded, Multi-task and Semi-supervised Learning
Conclusion
Bibliography
Author's Biography