Deep Learning Architectures: A Mathematical Approach

Deep Learning Architectures: A Mathematical Approach

PAPERBACK

14 Feb, 2021

This book describes how neural networks operate from the mathematical point of view. As a result, neural networks can be interpreted both as function unive ...

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ISBN-10:

3030367231

ISBN-13:

9783030367237

Dimensions

9.21 X 6.14 X 1.57 inches

Language

English

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.


Product Details

ISBN-10

:3030367231

ISBN-13

:9783030367237

Publication date

: 14 Feb, 2021

Edition

:2020 Edition

Category

: Mathematics

Sub-Category

: Applied

Format

:PAPERBACK

Language

:English

Reading Level

: All

No. of Units

:1

Dimension

: 9.21 X 6.14 X 1.57 inches

Weight

:1.089 Kg

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.

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