Deep Learning for NLP and Speech Recognition [electronic resource] / by Uday Kamath, John Liu, James Whitaker.
- 1st ed. 2019.
- Cham : Springer International Publishing : Imprint: Springer, 2019.
- Computer Science (Springer-11645)
1 online resource (XXVIII, 621 pages) : 313 illustrations, 300 illustrations in color.
- Artificial intelligence.
Python (Computer program language).
- Local subjects:
- Artificial Intelligence. (search)
Computational Intelligence. (search)
- System Details:
- text file PDF
- 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. .
- 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.
- Liu, John, author., Author,
Whitaker, James, author., Author,
SpringerLink (Online service)
- Contained In:
- Springer eBooks
- Other format:
- Printed edition:
9783030145958 (Printed edition)
9783030145972 (Printed edition)
9783030145989 (Printed edition)
- Publisher Number:
- 10.1007/978-3-030-14596-5 doi
- Access Restriction:
- Restricted for use by site license.
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