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Deep Learning for NLP and Speech Recognition

  • Uday Kamath
  • John Liu
  • James Whitaker
Textbook

Table of contents

  1. Front Matter
    Pages i-xxviii
  2. Machine Learning, NLP, and Speech Introduction

    1. Front Matter
      Pages 1-1
    2. Uday Kamath, John Liu, James Whitaker
      Pages 3-38
    3. Uday Kamath, John Liu, James Whitaker
      Pages 39-86
    4. Uday Kamath, John Liu, James Whitaker
      Pages 87-138
  3. Deep Learning Basics

    1. Front Matter
      Pages 139-139
    2. Uday Kamath, John Liu, James Whitaker
      Pages 141-201
    3. Uday Kamath, John Liu, James Whitaker
      Pages 203-261
    4. Uday Kamath, John Liu, James Whitaker
      Pages 263-314
    5. Uday Kamath, John Liu, James Whitaker
      Pages 315-368
    6. Uday Kamath, John Liu, James Whitaker
      Pages 369-404
  4. Advanced Deep Learning Techniques for Text and Speech

    1. Front Matter
      Pages 407-407
    2. Uday Kamath, John Liu, James Whitaker
      Pages 407-462
    3. Uday Kamath, John Liu, James Whitaker
      Pages 463-493
    4. Uday Kamath, John Liu, James Whitaker
      Pages 495-535
    5. Uday Kamath, John Liu, James Whitaker
      Pages 537-574
    6. Uday Kamath, John Liu, James Whitaker
      Pages 575-613
  5. Back Matter
    Pages 615-621

About this book

Introduction

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. 

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. 

Keywords

Deep Learning Architecture Document Classification Machine Translation Language Modeling Speech Recognition Natural Language Processing

Authors and affiliations

  • Uday Kamath
    • 1
  • John Liu
    • 2
  • James Whitaker
    • 3
  1. 1.Digital Reasoning Systems Inc.McLeanUSA
  2. 2.Intelluron CorporationNashvilleUSA
  3. 3.Digital Reasoning Systems Inc.McLeanUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-030-14596-5
  • Copyright Information Springer Nature Switzerland AG 2019
  • Publisher Name Springer, Cham
  • eBook Packages Computer Science
  • Print ISBN 978-3-030-14595-8
  • Online ISBN 978-3-030-14596-5
  • Buy this book on publisher's site
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