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This book describes how neural networks operate from the mathematical point of view. As a result, neural networks can be interpreted both as function universal approximators and information processors. The book bridges the gap between ideas and concepts of neural networks, which are used nowadays at an intuitive level, and the precise modern mathematical language, presenting the best practices of the former and enjoying the robustness and elegance of the latter. This book can be used in a graduate course in deep learning, with the first few parts being accessible to senior undergraduates. In addition, the book will be of wide interest to machine learning researchers who are interested in a theoretical understanding of the subject.
This book describes how neural networks operate from the mathematical point of view. As a result, neural networks can be interpreted both as function universal
Math and Architectures of Deep Learning bridges the gap between theory and practice, laying out the math of deep learning side by side with practical implementa
This book offers a timely reflection on the remarkable range of algorithms and applications that have made the area of deep learning so attractive and heavily r
Theoretical results suggest that in order to learn the kind of complicated functions that can represent high-level abstractions (e.g. in vision, language, and o
This book introduces readers to the fundamental concepts of deep learning and offers practical insights into how this learning paradigm supports automatic mecha
