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