{"product_id":"deep-learning-for-nlp-and-speech-recognition-paperback","title":"Deep Learning for Nlp and Speech Recognition - Paperback","description":"\u003cdiv\u003e\u003cp style=\"text-align: right;\"\u003e\u003ca href=\"https:\/\/reportcopyrightinfringement.com\/\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cb\u003eReport copyright infringement\u003c\/b\u003e\u003c\/a\u003e\u003c\/p\u003e\u003c\/div\u003e\u003cp\u003eby \u003cb\u003eUday Kamath\u003c\/b\u003e (Author), \u003cb\u003eJohn Liu\u003c\/b\u003e (Author), \u003cb\u003eJames Whitaker\u003c\/b\u003e (Author)\u003c\/p\u003e\u003cp\u003eThis 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. \u003ci\u003eDeep Learning for NLP and Speech Recognition\u003c\/i\u003e 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. \u003cbr\u003eMany 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. \u003cbr\u003eThe book is organized into three parts, aligning to different groups of readers and their expertise. The three parts are: \u003cbr\u003e \u003c\/p\u003e\u003cp\u003e \u003cb\u003eMachine Learning, NLP, and Speech Introduction\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThe first part has \u003cb\u003ethree chapters \u003c\/b\u003ethat 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.\u003c\/p\u003e \u003cp\u003e \u003cb\u003eDeep Learning Basics\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThe \u003cb\u003efive chapters\u003c\/b\u003e 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. \u003c\/p\u003e \u003cp\u003e \u003cb\u003eAdvanced Deep Learning Techniques for Text and Speech\u003c\/b\u003e\u003c\/p\u003e The third part has \u003cb\u003efive chapters\u003c\/b\u003e 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.\u003ch3\u003eBack Jacket\u003c\/h3\u003e\u003cp\u003eWith 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. \u003ci\u003eDeep Learning for NLP and Speech Recognition\u003c\/i\u003e 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. \u003cbr\u003eThe book is organized into three parts, aligning to different groups of readers and their expertise. The three parts are: \u003cbr\u003e\u003c\/p\u003e\u003cp\u003e \u003cb\u003eMachine Learning, NLP, and Speech Introduction\u003c\/b\u003e\u003c\/p\u003eThe first part has \u003cb\u003ethree chapters \u003c\/b\u003ethat 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.\u003cp\u003e\u003c\/p\u003e\u003cp\u003e \u003cb\u003eDeep Learning Basics\u003c\/b\u003e\u003c\/p\u003e\u003cp\u003eThe \u003cb\u003efive chapters\u003c\/b\u003e 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.\u003c\/p\u003e\u003cp\u003e \u003cb\u003eAdvanced Deep Learning Techniques for Text and Speech\u003c\/b\u003e\u003c\/p\u003e The third part has \u003cb\u003efive chapters\u003c\/b\u003e 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.\u003ch3\u003eAuthor Biography\u003c\/h3\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cb\u003eUday Kamath\u003c\/b\u003e 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.\u003c\/p\u003e\u003cp\u003e \u003c\/p\u003e\u003cp\u003e\u003cb\u003eJohn Liu\u003c\/b\u003e 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.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eJames (Jimmy) Whitaker\u003c\/b\u003e 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.\u003c\/p\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eNumber of Pages:\u003c\/strong\u003e 621\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eDimensions:\u003c\/strong\u003e 1.31 x 10 x 7 IN\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eIllustrated:\u003c\/strong\u003e Yes\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003ePublication Date:\u003c\/strong\u003e August 14, 2020\u003c\/div\u003e\n            ","brand":"BooksCloud","offers":[{"title":"Default Title","offer_id":47212835963129,"sku":"9783030145989","price":145.78,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0789\/2782\/3097\/files\/K1J4RnhsL3VEbTN0ZldubDFDdUxSUT09.webp?v=1768098616","url":"https:\/\/bookscloud.io\/products\/deep-learning-for-nlp-and-speech-recognition-paperback","provider":"BooksCloud Book Dropshipping","version":"1.0","type":"link"}