Neural networks are a family of powerful machine learning models. This book focuses on the application of neural network models 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.

Table of Contents

1 Introduction

PART I Supervised Classification and Feed-forward

Neural Networks

2 Learning Basics and Linear Models

3 From Linear Models to Multi-layer Perceptrons

4 Feed-forward Neural Networks

5 Neural Network Training

PART II Working with Natural Language Data

7 Case Studies of NLP Features

8 From Textual Features to Inputs

9 Language Modeling

10 Pre-trained Word Representations

11 Using Word Embeddings

12 Case Study: A Feed-forward Architecture for Sentence Meaning Inference

PART III Specialized Architectures

13 Ngram Detectors: Convolutional Neural Networks .

14 Recurrent Neural Networks: Modeling Sequences and Stacks

15 Concrete Recurrent Neural Network Architectures

16 Modeling with Recurrent Networks

17 Conditioned Generation

PART IV Additional Topics

18 Modeling Trees with Recursive Neural Networks

19 Structured Output Prediction

20 Cascaded, Multi-task and Semi-supervised Learning

21 Conclusion