Uday Kamath; John Liu; James Whitaker Deep Learning for NLP and Speech Recognition 9783030145958

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Автор: Uday Kamath; John Liu; James Whitaker
Название: Deep Learning for NLP and Speech Recognition
Перевод названия: Удаи Камат, Джон Лью, Джеймс Уайтакер: Технология глубокого обучения для обрабоки естественных языко
ISBN: 9783030145958
Издательство: Springer
Классификация:

Торгово-промышленная деятельность и управление

Языки программирования и описания сценариев: общие сведения

Искусственный интеллект

ISBN-10: 3030145956
Обложка/Формат: Hardcover
Страницы: 621
Вес: 1.42 кг.
Дата издания: 2019
Язык: English
Издание: 1st ed. 2019
Иллюстрации: 300 illustrations, color; 13 illustrations, black and white; xxviii, 621 p. 313 illus., 300 illus. in color.; 300 illustrations, color; 13 illustratio
Размер: 254 x 178 x 35
Читательская аудитория: Professional & vocational
Основная тема: Computer Science
Ссылка на Издательство: Link
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Поставляется из: Германии
Описание: 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 IntroductionThe 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 BasicsThe 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.

Дополнительное описание: 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

Deep Learning for NLP and Speech Recognition, Uday Kamath; John Liu; James Whitaker