Deep Learning for Nlp and Speech Recognition (Hardcover)
暫譯: 深度學習於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、自然語言處理（NLP）和語音應用在許多領域（包括金融、醫療保健和政府）的廣泛採用，對於一個綜合資源的需求日益增加，這個資源能夠將深度學習技術映射到NLP和語音上，並提供使用工具和庫進行實際應用的見解。《Deep Learning for NLP and Speech Recognition》解釋了適用於NLP和語音的最新深度學習方法，提供了最先進的方法，並通過實際案例研究和代碼提供實踐經驗。

許多書籍專注於深度學習理論或針對NLP特定任務的深度學習，而其他書籍則是工具和庫的食譜，但在快速演變的環境中，不斷變化的新算法、工具、框架和庫意味著可用的文本很少能提供本書中的材料。

本書分為三個部分，針對不同的讀者群體及其專業知識。這三個部分為：

**機器學習、NLP和語音介紹**

第一部分有**三章**，向讀者介紹NLP、語音識別、深度學習和機器學習的領域，並使用基於Python的工具和庫進行基本理論和實踐案例研究。

**深度學習基礎**

第二部分的**五章**介紹了深度學習及其對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、遞迴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，探討在實際任務中使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**，討論了深度學習領域中與NLP和語音交叉的最新和前沿研究。涵蓋的主題包括注意力機制、記憶增強網絡、遷移學習、多任務學習、領域適應、強化學習和端到端深度學習在語音識別中的應用，並使用案例研究進行說明。

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，專注於架構和建造基於分析的商業解決方案。他目前擔任 Digital Reasoning 的首席分析官，該公司是人工智慧在自然語言處理 (NLP) 和語音識別領域的領先企業之一，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，為金融行業構建機器學習產品和解決方案，專注於詐騙、合規性和網絡安全。Uday 之前撰寫了多本有關機器學習的書籍，例如《Machine Learning: End-to-End guide for Java developers: Data Analysis, Machine Learning, and Neural Networks simplified》和《Mastering Java Machine Learning: A Java developer's guide to implementing machine learning and big data architectures》。Uday 在不同的機器學習期刊和會議上發表了多篇學術論文。他擁有大數據機器學習的博士學位，並且是最早使用進化計算進行機器學習算法通用擴展的先驅之一。

**John Liu** 在過去的 22 年中管理定量研究、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，這是一家新興的人工智慧即服務解決方案公司。最近，John 擔任 Digital Reasoning 的數據科學和數據策略副總裁。之前，他是位於紐約的定量投資公司 Spartus Capital 的首席資訊官。在此之前，John 在花旗集團擔任高級執行職位，負責投資組合解決方案小組，為機構客戶提供定量投資和風險策略的建議；在印第安納州公共雇員退休金管理局，管理 70 億美元的公共股票投資組合；在范德堡大學，負責 20 億美元的股票和替代投資組合；以及在法國巴黎銀行，管理美國指數期權和 MSCI delta-one 交易台。他以在投資管理中應用強化學習的專業知識而聞名，並撰寫了多篇論文和書籍章節，主題包括自然語言處理、表示學習、系統性風險、資產配置和 EM 理論。2016 年，John 被評選為納什維爾年度數據科學家。他在賓夕法尼亞大學獲得電機工程的學士、碩士和博士學位，並且是 CFA 特許持有人。

**James (Jimmy) Whitaker** 在 Digital Reasoning 管理應用研究。他目前領導金融科技領域的語音分析深度學習開發，並在過去 4 年中專注於構建 NLP、語音識別和計算機視覺的機器學習應用。他在牛津大學獲得計算機科學碩士學位，因其在隱寫分析領域應用機器學習而獲得優異成績，之前在基督兄弟大學獲得電機工程和計算機科學的學士學位。在從事深度學習之前，Jimmy 曾擔任複雜交通計劃的概念工程師和風險管理員。

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