Ebook Deep Learning for NLP and Speech Recognition
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Deep Learning for NLP and Speech Recognition
Ebook Deep Learning for NLP and Speech Recognition
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From the Back Cover
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 IntroductionThe 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 BasicsThe 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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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 Recognition, heading the Applied Machine Learning research group. Most recently, he 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. He 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. His Ph.D. in Big Data Machine Learning 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, data science, and portfolio management teams. He is currently head of data science and data strategy at Digital Reasoning in Franklin, Tennessee. Most recently, John was managing partner and CIO at 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 pensions, endowments, and sovereign wealth funds 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 strategies and hedge fund portfolios; and at BNP Paribas, where he managed the US index options and MSCI delta-one trading desks. He is an expert 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.
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Product details
Hardcover: 676 pages
Publisher: Springer; 1st ed. 2019 edition (June 6, 2019)
Language: English
ISBN-10: 3030145956
ISBN-13: 978-3030145958
Product Dimensions:
7 x 10 inches
Shipping Weight: 1.7 pounds (View shipping rates and policies)
Average Customer Review:
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Amazon Best Sellers Rank:
#2,829,166 in Books (See Top 100 in Books)
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