Deep Learning for Nlp and Speech Recognition (Hardcover) (深度學習：自然語言處理與語音辨識)

Kamath, Uday, Liu, John, Whitaker, James

Deep Learning for Nlp and Speech Recognition (Hardcover)

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商品描述

This 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. 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.
Many 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.
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 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.

商品描述(中文翻譯)

這本教科書解釋了深度學習架構，並應用於各種自然語言處理（NLP）任務，包括文件分類、機器翻譯、語言建模和語音識別。隨著深度學習、NLP和語音應用在金融、醫療保健和政府等領域的廣泛應用，對於將深度學習技術應用於NLP和語音的全面資源的需求越來越大，並提供使用工具和庫進行實際應用的見解。《深度學習用於NLP和語音識別》解釋了最近適用於NLP和語音的深度學習方法，提供了最先進的方法，並提供了帶有代碼的實際案例研究，以提供實踐經驗。

許多書籍專注於深度學習理論或深度學習專用於NLP任務，而其他書籍則是針對工具和庫的食譜，但是在快速發展的領域中，新算法、工具、框架和庫不斷變化，意味著很少有可用的文本提供本書中的材料。

本書分為三個部分，以適應不同讀者群體和他們的專業知識。這三個部分分別是：

機器學習、NLP和語音介紹

第一部分有三章，介紹讀者NLP、語音識別、深度學習和機器學習的基本理論，並使用基於Python的工具和庫進行實際案例研究。

深度學習基礎

第二部分有五章，介紹深度學習和對於語音和文本處理至關重要的各種主題，包括詞嵌入、卷積神經網絡、循環神經網絡和語音識別基礎。理論、實用技巧、最先進的方法、實驗和分析，以及在實際任務中應用理論中討論的方法。

文本和語音的高級深度學習技術

第三部分有五章，討論與NLP和語音相交的深度學習領域的最新和尖端研究。包括注意機制、記憶增強網絡、轉移學習、多任務學習、領域適應、強化學習和端到端深度學習等主題，並使用案例研究進行探討。

作者簡介

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 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 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.

作者簡介(中文翻譯)

Uday Kamath擁有超過20年的經驗，設計和建立基於分析的商業解決方案。他目前擔任Digital Reasoning的首席分析官，該公司是自然語言處理和語音識別人工智能領域的領先公司之一，負責應用機器學習研究小組。最近，Uday擔任BAE Systems Applied Intelligence的首席數據科學家，為金融行業建立機器學習產品和解決方案，專注於欺詐、合規和網絡安全。Uday以前曾撰寫過許多機器學習方面的書籍，例如《機器學習：Java開發人員的端到端指南：數據分析、機器學習和神經網絡簡化》和《精通Java機器學習：Java開發人員實施機器學習和大數據架構的指南》。Uday在不同的機器學習期刊和會議上發表了許多學術論文。Uday擁有大數據機器學習博士學位，是首批使用進化計算進行機器學習算法的泛化縮放的人之一。

John Liu在過去的22年中一直管理量化研究、投資組合管理和數據科學團隊。他目前是Intelluron Corporation的首席執行官，該公司是一家新興的AI即服務解決方案公司。最近，John擔任Digital Reasoning的數據科學和數據戰略負責人。以前，他是紐約量化投資公司Spartus Capital的首席投資官。在此之前，John在花旗集團擔任高級執行職位，負責指導機構客戶進行量化投資和風險策略的投資組合解決方案團隊；在印第安納州公務員退休金處，管理70億美元的公共股票投資組合；在范德堡大學，監督20億美元的股票和替代投資組合；在法國巴黎銀行，管理美國指數期權和MSCI Delta One交易部門。他以在投資管理中應用強化學習的專業知識而聞名，並撰寫了許多關於自然語言處理、表示學習、系統風險、資產配置和EM理論等主題的論文和專書章節。2016年，John被評為納什維爾的年度數據科學家。他在賓夕法尼亞大學獲得了學士、碩士和博士學位，專業是電氣工程，並擁有CFA資格。

James（Jimmy）Whitaker負責Digital Reasoning的應用研究工作。他目前在金融科技領域領導深度學習的語音分析項目，過去4年一直在NLP、語音識別和計算機視覺領域建立機器學習應用。他在牛津大學獲得計算機科學碩士學位，並因在Steganalysis領域應用機器學習而獲得優異成績，此前他在基督教兄弟大學獲得電氣工程和計算機科學的學士學位。在深度學習之前，Jimmy曾擔任複雜交通項目的概念工程師和風險經理。

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