Overview
- Contains a fair number of end-of chapter exercises
- Full solutions provided to all exercises
- Appendices including topics needed in the book exposition
Part of the book series: Springer Series in the Data Sciences (SSDS)
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About this book
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.
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Keywords
Table of contents (20 chapters)
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Front Matter
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Introduction to Neural Networks
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Front Matter
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Analytic Theory
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Front Matter
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Information Processing
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Front Matter
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Geometric Theory
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Front Matter
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Other Architectures
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Front Matter
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Reviews
“This book is useful to students who have already had an introductory course in machine learning and are further interested to deepen their understanding of the machine learning material from the mathematical point of view.” (T. C. Mohan, zbMATH 1441.68001, 2020)
Authors and Affiliations
About the author
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.
Bibliographic Information
Book Title: Deep Learning Architectures
Book Subtitle: A Mathematical Approach
Authors: Ovidiu Calin
Series Title: Springer Series in the Data Sciences
DOI: https://doi.org/10.1007/978-3-030-36721-3
Publisher: Springer Cham
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: Springer Nature Switzerland AG 2020
Hardcover ISBN: 978-3-030-36720-6Published: 14 February 2020
Softcover ISBN: 978-3-030-36723-7Published: 14 February 2021
eBook ISBN: 978-3-030-36721-3Published: 13 February 2020
Series ISSN: 2365-5674
Series E-ISSN: 2365-5682
Edition Number: 1
Number of Pages: XXX, 760
Number of Illustrations: 172 b/w illustrations, 35 illustrations in colour
Topics: Mathematical Applications in Computer Science, Machine Learning