This SpringerBrief describes how to build a rigorous end-to-end mathematical framework for deep neural networks. The authors provide tools to represent and desc

Neural networks have been widely used in many applications, such as image classification and understanding, language processing, and control of autonomous syste

For convenience, many of the proofs of the key theorems have been rewritten so that the entire book uses a relatively uniform notion.

A new edition of a graduate-level machine learning textbook that focuses on the analysis and theory of algorithms. This book is a general introduction to machin

This volume develops an effective theory approach to understanding deep neural networks of practical relevance.