Deep Learning Architectures : A Mathematical Approach

Ovidiu Calin

ISBN 10: 3030367207 / ISBN 13: 9783030367206

Published by Springer Nature Switzerland AG, 2020

About this Item

Druck auf Anfrage Neuware - Printed after ordering - 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.

Seller Inventory # 9783030367206

Bibliographic Details

• Title: Deep Learning Architectures : A Mathematical Approach
• Publisher: Springer Nature Switzerland AG
• Publication Date: 2020
• Language: English
• ISBN 10: 3030367207
• ISBN 13: 9783030367206
• Binding: Buch
• Condition: Neu
• Dimensions: 263W x 182H x 42D millimeters 263 millimeters width by 182 millimeters height by 42 millimeters depth
• Weight: 1744 grams

About this title

Synopsis

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.

 

 

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.