From the Back Cover
-------------------
With the widespread adoption of deep learning, natural language
processing (NLP),and speech applications in many areas (including
Finance, care, and Government) there is a growing need for
one comprehensive resource that s deep learning techniques to
NLP and speech and provides ins into using the tools
and libraries for real-world applications. Deep Learning for
NLP and Speech Re 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 re, 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 re 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 re are covered
using case studies.
Read more ( javascript:void(0) )
About the Author
----------------
Uday Kamath has more than 20 years of experience architecting and
building analytics-based commercial solutions. He currently works
as the Chief Analytics Officer at Digital Reasoning, one of the
leading companies in AI for NLP and Speech Re, heading
the Applied Machine Learning research group. Most
recently, Uday served as the Chief Data Scientist at BAE Systems
Applied Intelligence, building machine learning products and
solutions for the financial industry, focused on fraud,
compliance, and cybersecurity. Uday has previously authored many
books on machine learning such as Machine Learning: End-to-End
guide for Java developers: Data Analysis, Machine Learning, and
Neural Networks simplified and Mastering Java Machine Learning: A
Java developer's guide to implementing machine learning and big
data architectures. Uday has published many academic papers in
different machine learning journals and conferences. Uday has
a Ph.D. in Big Data Machine Learning and was one of the first in
generalized scaling of machine learning algorithms using
evolutionary computing.
John Liu spent the past 22 years managing quantitative research,
portfolio management and data science teams. He is currently CEO
of Intelluron Corporation, an emerging AI-as-a-service solution
company. Most recently, John was head of data science and data
strategy as VP at Digital Reasoning. Previously, he was CIO of
Spartus Capital, a quantitative investment firm in New York.
Prior to that, John held senior executive roles at Citigroup,
where he oversaw the portfolio solutions group that advised
institutional clients on quantitative investment and risk
strategies; at the Indiana Public Employees pension, where he
managed the $7B public equities portfolio; at Vanderbilt
University, where he oversaw the $2B equity and alternative
investment portfolios; and at BNP Paribas, where he managed the
US index options and MSCI delta-one trading desks. He is known
for his expertise in reinforcement learning applied to investment
management and has authored numerous papers and book chapters on
topics including natural language processing, representation
learning, systemic risk, asset allocation, and EM theory. In
2016, John was named Nashville's Data Scientist of the Year. He
earned his B.S., M.S., and Ph.D. in electrical engineering from
the University of Pennsylvania and is a CFA Charterholder.
James (Jimmy) Whitaker manages Applied Research at Digital
Reasoning. He currently leads deep learning developments in
speech analytics in the FinTech space, and has spent the last 4
years building machine learning applications for NLP, Speech
Re, and Computer Vision. He received his masters in
Computer Science from the University of Oxford, where he received
a distinction for his application of machine learning in the
field of Steganalysis after completing his undergraduate degrees
in Electrical Engineering and Computer Science from Christian
Brothers University. Prior to his work in deep learning, Jimmy
worked as a concept engineer and risk manager for complex
transportation initiatives.
Read more ( javascript:void(0) )