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.
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