Deep Learning Architectures: A Mathematical Approach

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.

Author: Ovidiu Calin
Binding Type: Paperback
Publisher: Springer
Published: 02/14/2021
Series: Springer the Data Sciences
Pages: 760
Weight: 2.4lbs
Size: 9.21h x 6.14w x 1.57d
ISBN: 9783030367237
2020 Edition