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Deep learning for NLP and speech recognition

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Deep learning for NLP and speech recognition/ by Uday Kamath, John Liu, James Whitaker.
作者:	Kamath, Uday.
其他作者:	Liu, John.
出版者:	Cham :Springer International Publishing : : 2019.,
面頁冊數:	xxviii, 621 p. :ill., digital ; : 24 cm.;
Contained By:	Springer eBooks
標題:	Natural language processing (Computer science) -
電子資源:	https://doi.org/10.1007/978-3-030-14596-5
ISBN:	9783030145965

Deep learning for NLP and speech recognition
Kamath, Uday.

Deep learning for NLP and speech recognition[electronic resource] /by Uday Kamath, John Liu, James Whitaker. - Cham :Springer International Publishing :2019. - xxviii, 621 p. :ill., digital ;24 cm.

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.

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.

ISBN: 9783030145965

Standard No.: 10.1007/978-3-030-14596-5doiSubjects--Topical Terms:

641811
Natural language processing (Computer science)

LC Class. No.: QA76.9.N38 / K36 2019

Dewey Class. No.: 006.35

Deep learning for NLP and speech recognition
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505 0 $a 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.
520 $a 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.
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