The Science of Deep Learning

The Science of Deep Learning
Iddo Drori
RRP:
NZ$ 91.95
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NZ$ 82.76
Hardback
Not defined - 450pg
18 Aug 2022 UK
International import eta 7-19 days
9781108835084
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The Science of Deep Learning emerged from courses taught by the author that have provided thousands of students with training and experience for their academic studies, and prepared them for careers in deep learning, machine learning, and artificial intelligence in top companies in industry and academia. The book begins by covering the foundations of deep learning, followed by key deep learning architectures. Subsequent parts on generative models and reinforcement learning may be used as part of a deep learning course or as part of a course on each topic. The book includes state-of-the-art topics such as Transformers, graph neural networks, variational autoencoders, and deep reinforcement learning, with a broad range of applications. The appendices provide equations for computing gradients in backpropagation and optimization, and best practices in scientific writing and reviewing. The text presents an up-to-date guide to the field built upon clear visualizations using a unified notation and equations, lowering the barrier to entry for the reader. The accompanying website provides complementary code and hundreds of exercises with solutions.
' In the avalanche of books on Deep Learning, this one stands out. Iddo Drori has mastered reinforcement learning - in its technical meaning and in his successful, commonsense approach to teaching and understanding. ' Gilbert Strang, Massachusetts Institute of Technology ' This book covers an impressive breadth of foundational concepts and algorithms behind modern deep learning. By reading this book, readers will quickly but thoroughly learn and appreciate foundations and advances of modern deep learning. ' Kyunghyun Cho, New York University ' This book offers a fascinating tour of the field of deep learning, which in only ten years has come to revolutionize almost every area of computing. Drori provides concise descriptions of many of the most important developments, combining unified mathematical notation and ample figures to form an essential resource for students and practitioners alike. ' Jonathan Ventura, Cal Poly ' Drori' s textbook goes under the hood of deep learning, covering a broad swath of modern techniques in optimization that are useful for efficiently training neural networks. The book also covers regularization methods to avoid overfitting, a common issue when working with deep learning models. Overall, this is an excellent textbook for students and practitioners who want to gain a deeper understanding of deep learning. ' Madeleine Udell, Stanford University ' This textbook provides an excellent introduction to contemporary methods and models in deep learning. I expect this book to become a key resource in data science education for students and researchers. ' Nakul Verma, Columbia University ' This new book by Professor Drori brings fresh insights from his experience teaching thousands of students at Columbia, MIT, and NYU during the past several years. The book is a unique resource and opportunity for educators and researchers worldwide to build on his highly successful deep learning course. ' Claudio Silva, New York University
Iddo Drori is a faculty member and associate professor at Boston University, a lecturer at MIT, and adjunct associate professor at Columbia University. He was a visiting associate professor at Cornell University in operations research and information engineering, and research scientist and adjunct professor at NYU Center for Data Science, Courant Institute, and NYU Tandon. He holds a PhD in computer science and was a postdoctoral research fellow at Stanford University in statistics. He also holds an MBA in organizational behavior and entrepreneurship and has a decade of industry research and leadership experience. His main research is in machine learning, AI, and computer vision, with 70 publications and over 5,100 citations, and he has taught over 35 courses in computer science. He has won multiple competitions in computer vision conferences and received multiple best paper awards in machine learning conferences.
Status: Active
ISBN-13: 9781108835084
Published: 18 Aug 2022
Published In: United Kingdom
Imprint: Cambridge University Press
Publisher: Cambridge University Press
Format: Hardback
Pages: 450

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