Gilbert Strang
American mathematician
Book Recommendations:
Recommended by Gilbert Strang
“'This book’s physics-trained authors have made a cool discovery, that feature learning depends critically on the ratio of depth to width in the neural net.'” (from Amazon)
by Daniel A. Roberts, Sho Yaida, Boris Hanin·You?
by Daniel A. Roberts, Sho Yaida, Boris Hanin·You?
This textbook establishes a theoretical framework for understanding deep learning models of practical relevance. With an approach that borrows from theoretical physics, Roberts and Yaida provide clear and pedagogical explanations of how realistic deep neural networks actually work. To make results from the theoretical forefront accessible, the authors eschew the subject's traditional emphasis on intimidating formality without sacrificing accuracy. Straightforward and approachable, this volume balances detailed first principle derivations of novel results with insight and intuition for theorists and practitioners alike. This self contained textbook is ideal for students and researchers interested in artificial intelligence with minimal prerequisites of linear algebra, calculus. informal probability theory. it can easily fill a semester long course on deep learning theory. For the first time, the exciting practical advances in modern artificial intelligence capabilities can be matched with a set of effective principles, providing a timeless blueprint for theoretical research in deep learning.
Recommended by Gilbert Strang
“'This book explains the least squares method and the linear algebra it depends on - and the authors do it right!'” (from Amazon)
by Stephen Boyd, Lieven Vandenberghe·You?
by Stephen Boyd, Lieven Vandenberghe·You?
This groundbreaking textbook combines straightforward explanations with a wealth of practical examples to offer an innovative approach to teaching linear algebra. Requiring no prior knowledge of the subject, it covers the aspects of linear algebra - vectors, matrices, and least squares - that are needed for engineering applications, discussing examples across data science, machine learning and artificial intelligence, signal and image processing, tomography, navigation, control, and finance. The numerous practical exercises throughout allow students to test their understanding and translate their knowledge into solving real-world problems, with lecture slides, additional computational exercises in Julia and MATLAB®, and data sets accompanying the book online. Suitable for both one-semester and one-quarter courses, as well as self-study, this self-contained text provides beginning students with the foundation they need to progress to more advanced study.