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 Recognition, 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 managedthe 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 Recognition, 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.

A comprehensive resource that builds up from elementary deep learning, text, and speech principles to advanced state-of-the-art neural architectures

A ready reference for deep learning techniques applicable to common NLP and speech recognition applications

A useful resource on successful architectures and algorithms with essential mathematical insights explained in detail

An in-depth reference and comparison of the latest end-to-end neural speech processing approach

A panoramic resource on leading edge transfer learning, domain adaptation and deep reinforcement learning architectures for text and speech

Practical aspects of using these techniques with tips and tricks essential for real-world applications

A hands-on approach to using Python-based deep learning libraries such as Keras, TensorFlow, and PyTorch to apply these techniques in the context of real-world case studies

Thirteen case studies with code, data, and configurations across different approaches for NLP and Speech recognition tasks such as Embeddings, Classification, Distributed Representation, Summarization, Machine Translation, Sentiment Analysis, Cross Domain Transfer Learning, Multi-Task NLP, End to End Speech, and Question Answering