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.
