The Science of Deep Learning

‘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‘Drori’s book covers deep learning, from fundamentals to applications. The fundamentals are covered with clear figures and examples, making the underlying algorithms easy to understand for non-specialists. The multidisciplinary applications are thoughtfully selected to illustrate the broad applications of deep neural networks to specialized domains while highlighting the common themes and architectures between them.’ Tonio Buonassisi, Professor of Mechanical Engineering, Massachusetts Institute of Technology‘Drori’s textbook makes the learning curve for deep learning a whole lot easier to climb. It follows a rigid scientific narrative, accompanied by a trove of code examples and visualizations. These enable a truly multi-modal approach to learning that will allow many students to understand the material better and sets them on a path of exploration.’ Joaquin Vanschoren, Assistant Professor of Machine Learning, Eindhoven University of Technology‘This is an instrumental book which I highly recommend to all students studying modern AI techniques.’ Alfred Zimmermann, Reutlingen University‘This book presents the fundamental concepts and algorithms of deep learning such as NN, Optimisation, CNN, RNN, Transformer, GNN, Generative models, and Reinforcement learning. Teaching these algorithms to the bachelor students are essential. Further, the book finishes with applications and use cases that would further help the reader to apply those fundamental algorithms and build projects. I really like the way this book written. It introduces less jargon but more essential and sufficient content.’ Md Zia Ullah, Edinburgh Napier 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.