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Название: Deep Learning for NLP and Speech Recognition
Автор: Uday Kamath, John Liu, James Whitaker
Издательство: Springer
Год: 2019
Страниц: 640
Язык: английский
Формат: pdf (true)
Размер: 19.1 MB

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.

Python is becoming the lingua franca of data scientists and researchers for performing experiments in deep learning. There are many libraries with Python-enabled bindings for deep learning, NLP, and speech that have sprung up in the last few years. Therefore, we use both the Python language and its accompanying libraries for all case studies in this book. As it is unfeasible to fully cover every topic in a single book, we present what we believe are the key concepts with regard to NLP and speech that will translate into application. In particular, we focus on the intersection of those areas, wherein we can leverage different frameworks and libraries to explore modern research and related applications.

The book is organized into three parts, aligning to different groups of readers and their expertise. The three parts are:

1) 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.

2) 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.

3) 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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