Deep Learning Architectures

A Mathematical Approach

By:

Ovidiu Calin

Format:

Paperback / softback

Series:

Springer Series in the Data Sciences

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Description

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 Biography:

Ovidiu Calin, a graduate from University of Toronto, is a professor at Eastern Michigan University and a former visiting professor at Princeton University and University of Notre Dame. He has delivered numerous lectures at several universities in Japan, Hong Kong, Taiwan, and Kuwait over the last 15 years. His publications include over 60 articles and 8 books in the fields of machine learning, computational finance, stochastic processes, variational calculus and geometric analysis.

Release date Australia

February 14th, 2021

Author

Ovidiu Calin

Series

Springer Series in the Data Sciences

Pages

760

Edition

1st ed. 2020

Audience

Professional & Vocational

Illustrations

35 Illustrations, color; 172 Illustrations, black and white; XXX, 760 p. 207 illus., 35 illus. in color.

ISBN-13

9783030367237

Product ID

34554052

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