Deep Learning for NLP and Speech Recognition

✍ Scribed by Uday Kamath, John Liu, James Whitaker

• Publisher: Springer International Publishing
• Year: 2019
• Tongue: English
• Leaves: 640
• Edition: 1st ed.
• Category: Library

✦ Synopsis

This textbook explains Deep Learning Architecture, with applications to various NLP Tasks, including Document Classification, Machine Translation, Language Modeling, and Speech Recognition. With the widespread adoption of deep learning, natural language processing (NLP),and speech applications in many areas (including Finance, Healthcare, and Government) there is a growing need for one comprehensive resource that maps deep learning techniques to NLP and speech and provides insights into using the tools and libraries for real-world applications. Deep Learning for NLP and Speech Recognition explains recent deep learning methods applicable to NLP and speech, provides state-of-the-art approaches, and offers real-world case studies with code to provide hands-on experience.
Many books focus on deep learning theory or deep learning for NLP-specific tasks while others are cookbooks for tools and libraries, but the constant flux of new algorithms, tools, frameworks, and libraries in a rapidly evolving landscape means that there are few available texts that offer the material in this book.
The book is organized into three parts, aligning to different groups of readers and their expertise. The three parts are:

Machine Learning, NLP, and Speech Introduction

The first part has three chapters that introduce readers to the fields of NLP, speech recognition, deep learning and machine learning with basic theory and hands-on case studies using Python-based tools and libraries.

Deep Learning Basics

The five chapters in the second part introduce deep learning and various topics that are crucial for speech and text processing, including word embeddings, convolutional neural networks, recurrent neural networks and speech recognition basics. Theory, practical tips, state-of-the-art methods, experimentations and analysis in using the methods discussed in theory on real-world tasks.

Advanced Deep Learning Techniques for Text and Speech

The third part has five chapters that discuss the latest and cutting-edge research in the areas of deep learning that intersect with NLP and speech. Topics including attention mechanisms, memory augmented networks, transfer learning, multi-task learning, domain adaptation, reinforcement learning, and end-to-end deep learning for speech recognition are covered using case studies.

✦ Table of Contents

Front Matter ....Pages i-xxviii
Front Matter ....Pages 1-1
Introduction (Uday Kamath, John Liu, James Whitaker)....Pages 3-38
Basics of Machine Learning (Uday Kamath, John Liu, James Whitaker)....Pages 39-86
Text and Speech Basics (Uday Kamath, John Liu, James Whitaker)....Pages 87-138
Front Matter ....Pages 139-139
Basics of Deep Learning (Uday Kamath, John Liu, James Whitaker)....Pages 141-201
Distributed Representations (Uday Kamath, John Liu, James Whitaker)....Pages 203-261
Convolutional Neural Networks (Uday Kamath, John Liu, James Whitaker)....Pages 263-314
Recurrent Neural Networks (Uday Kamath, John Liu, James Whitaker)....Pages 315-368
Automatic Speech Recognition (Uday Kamath, John Liu, James Whitaker)....Pages 369-404
Front Matter ....Pages 407-407
Attention and Memory Augmented Networks (Uday Kamath, John Liu, James Whitaker)....Pages 407-462
Transfer Learning: Scenarios, Self-Taught Learning, and Multitask Learning (Uday Kamath, John Liu, James Whitaker)....Pages 463-493
Transfer Learning: Domain Adaptation (Uday Kamath, John Liu, James Whitaker)....Pages 495-535
End-to-End Speech Recognition (Uday Kamath, John Liu, James Whitaker)....Pages 537-574
Deep Reinforcement Learning for Text and Speech (Uday Kamath, John Liu, James Whitaker)....Pages 575-613
Back Matter ....Pages 615-621

✦ Subjects

Computer Science; Computational Intelligence; Python