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 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.
