THE PRINCIPLES OF DEEP LEARNING THEORY

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 and informal probability theory and 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