Transformers for Machine Learning (Chapman & Hall/CRC Machine Learning & Pattern Recognition)

<p>"Transformers are becoming a core part of many neural network architectures, employed in a wide range of applications such as NLP, Speech Recognition, Time Series, and Computer Vision. Transformers have gone through many adaptations and alterations, resulting in newer techniques and methods. Transformers for Machine Learning: A Deep Dive is the first comprehensive book on transformers. The theoretical explanations of the state-of-the-art transformer architectures will appeal to postgraduate students and researchers (academic and industry) as it will provide a single entry point with deep discussions of a quickly moving field. The practical hands-on case studies and code will appeal to undergraduate students, practitioners, and professionals as it allows for quick experimentation and lowers the barrier to entry into the field"--</p>

Transformers for Machine Learning: A Deep Dive (Chapman & Hall/CRC Machine Learning & Pattern Recognition)

Computers & Technology

Graham, Kenneth

Kamath, Uday

Emara, Wael

Computer Science

Chapman and Hall/CRC

<p>Transformers are becoming a core part of many neural network architectures, employed in a wide range of applications such as NLP, Speech Recognition, Time Series, and Computer Vision. Transformers have gone through many adaptations and alterations, resulting in newer techniques and methods. Transformers for Machine Learning: A Deep Dive is the first comprehensive book on transformers.<br />
Key Features: A comprehensive reference book for detailed explanations for every algorithm and techniques related to the transformers. 60+ transformer architectures covered in a comprehensive manner. A book for understanding how to apply the transformer techniques in speech, text, time series, and computer vision. Practical tips and tricks for each architecture and how to use it in the real world. Hands-on case studies and code snippets for theory and practical real-world analysis using the tools and libraries, all ready to run in Google Colab.<br />
The theoretical explanations of the state-of-the-art transformer architectures will appeal to postgraduate students and researchers (academic and industry) as it will provide a single entry point with deep discussions of a quickly moving field. The practical hands-on case studies and code will appeal to undergraduate students, practitioners, and professionals as it allows for quick experimentation and lowers the barrier to entry into the field.</p>

Deep Learning for NLP and Speech Recognition

<p>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. <i>Deep Learning for NLP and Speech Recognition</i> 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.  <br>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. <br>The book is organized into three parts, aligning to different groups of readers and their expertise. The three parts are:<br></p>
<p>      <b>Machine Learning, NLP, and Speech Introduction</b></p> <p>The first part has <b>three chapters </b>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.</p> <p>      <b>Deep Learning Basics</b></p> <p>The <b>five chapters</b> 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. </p> <p>      <b>Advanced Deep Learning Techniques for Text and Speech</b></p>  The third part has <b>five chapters</b> 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.

Liu, John

Whitaker, James

Springer

<p>This textbook explains Deep Learning Architecture, with applications to various NLP Tasks, including Document Classification, Machine Translation, Language Modeling, and Speech Recognition.&nbsp;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&nbsp; into&nbsp; using&nbsp; the&nbsp; tools&nbsp; and&nbsp; libraries&nbsp; for&nbsp; real-world&nbsp; applications.&nbsp;<i>Deep Learning for NLP and Speech Recognition</i>&nbsp;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.&nbsp;&nbsp; <div><br /></div> <div>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.&nbsp;</div> <div><br /></div> <div>The book is organized into three parts, aligning to different groups of readers and their expertise. The three parts are:<br /></div> <p>&nbsp; &nbsp; &nbsp;&nbsp;<b>Machine Learning, NLP, and Speech Introduction</b></p> <p>The first part has <b>three chapters</b> that introduce readers to the fields of&nbsp; NLP, speech recognition,&nbsp; deep learning and machine learning with basic theory and hands-on case studies using Python-based tools and libraries.</p> <p>&nbsp; &nbsp; &nbsp;&nbsp;<b>Deep Learning Basics</b></p> <p>The <b>five chapters</b> 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.</p> <p>&nbsp; &nbsp; &nbsp;&nbsp;<b>Advanced Deep Learning Techniques for Text and Speech</b></p> <div>The third part has <b>five chapters</b> 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.&nbsp;</div></p>

<p><div>This book is written both for readers entering the field, and for practitioners with a background in AI and an interest in developing real-world applications. The book is a great resource for practitioners and researchers in both industry and academia, and the discussed case studies and associated material can serve as inspiration for a variety of projects and hands-on assignments in a classroom setting. I will certainly keep this book as a personal resource for the courses I teach, and strongly recommend it to my students.&nbsp; &nbsp; &nbsp; &nbsp;</div> <div><br /></div> <div>--Dr. Carlotta Domeniconi, Associate Professor, Computer Science Department, GMU</div> <p><br /></p> <div>This book offers a curriculum for introducing interpretability to machine learning at every stage. The authors provide compelling examples that a core teaching practice like leading interpretive discussions can be taught and learned by teachers and sustained effort. And what better way to strengthen the quality of AI and Machine learning outcomes. I hope that this book will become a primer for teachers, data Science educators, and ML developers, and together we practice the art of interpretive machine learning.</div> <div><br /></div> <div>--Anusha Dandapani, Chief Data and Analytics Officer, UNICC and Adjunct Faculty, NYU</div> <p><br /></p> <div> <p>This is a wonderful book! I'm pleased that the next generation of scientists will finally be able to learn this important topic. This is the first book I've seen that has up-to-date and well-rounded coverage. Thank you to the authors!</p> <p>--Dr. Cynthia Rudin, Professor of Computer Science, Electrical and Computer Engineering, Statistical Science, and Biostatistics & Bioinformatics<br /></p> </div> <div>&nbsp;<br /></div> <div>Literature on Explainable AI has up until now been relatively scarce and featured mainly mainstream algorithms like SHAP and LIME. This book has closed this gap by providing an extremely broad review of various algorithms proposed in the scientific circles over the previous 5-10 years. This book is a great guide to anyone who is new to the field of XAI or is already familiar with the field and is willing to expand their knowledge.&nbsp; A comprehensive review of the state-of-the-art Explainable AI methods starting from visualization, interpretable methods, local and global explanations, time series methods, and finishing with deep learning provides an unparalleled source of information currently unavailable anywhere else. Additionally, notebooks with vivid examples are a great supplement that makes the book even more attractive for practitioners of any level.</div> <div><br /></div> <div>Overall, the authors provide readers with an enormous breadth of coverage without losing sight of practical aspects, which makes this book truly unique and a great addition to the library of any data scientist.</div> <div><br /></div> <div>Dr. Andrey Sharapov, Product Data Scientist, Explainable AI Expert and Speaker, Founder of Explainable AI-XAI Group</div> <div><br /></div></p>

Explainable Artificial Intelligence: An Introduction to Interpretable Machine Learning

Large Language Models: A Deep Dive: Bridging Theory and Practice

Keenan, Kevin

Somers, Garrett

Sorenson, Sarah

Applied Causal Inference

Naylor, Mitchell

Independently published

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