Deep Learning Architectures : A Mathematical Approach

Ovidiu Calin

ISBN 10: 3030367207 / ISBN 13: 9783030367206

Editore: Springer Nature Switzerland AG, 2020

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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.

Codice articolo 9783030367206

Dati bibliografici

• Titolo: Deep Learning Architectures : A Mathematical Approach
• Editore: Springer Nature Switzerland AG
• Data di pubblicazione: 2020
• Lingua: Inglese
• ISBN 10: 3030367207
• ISBN 13: 9783030367206
• Rilegatura: Buch
• Condizione: Neu
• Dimensioni: 241 L x 164 A x 45 P (millimetri) 241 millimetri di larghezza per 164 millimetri di altezza per 45 millimetri di profondità
• Peso: 1784 grammi

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Riassunto

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.

 

 

Informazioni sull?autore

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.