Deep Learning for NLP 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.  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 inthe areas of deep learning that intersect with NLP and speech. Topics includingattention mechanisms, memory augmented networks, transfer learning, multi-tasklearning, domain adaptation, reinforcement learning, and end-to-end deep learning forspeech recognition are covered using case studies.