自然言語処理と音声認識のための深層学習入門 Deep Learning for NLP and Speech Recognition

著者名：Kamath, Uday/Liu, John/Whitaker, James

Springer（2019/06/10発売）

• 言語：ENG
• ISBN：9783030145958
• eISBN：9783030145965

Description

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

Notation xv.- Part 1: Machine Learning, NLP, and Speech Introduction.- Chapter 1 Introduction 1.- Chapter 2 Basics of Machine Learning 2.- Chapter 3 Text and Speech Basics 49.- Part 2: Deep Learning Basics.- Chapter 4 Basics of Deep Learning 105.- Chapter 5 Distributed Representations 213.- Chapter 6 Convolutional Neural Networks 275.- Chapter 7 Recurrent Neural Networks 329.- Chapter 8 Automatic Speech Recognition 387.- Part 3: Advance Deep Learning Techniques for Text and Speech.- Chapter 9 Attention and Memory Augmented Networks 429.- Chapter 10 Transfer learning: Scenarios, Self-Taught Learning, and Multitask Learning 485.- Chapter 11 Transfer Learning: Domain Adaptation 515.- Chapter 12 End-to-end Speech Recognition 559.- Chapter 13 Deep Reinforcement Learning for Text and Speech 601.- Future Outlook 647.