Deep Learning Architectures: A Mathematical Approach (Springer Series in the Data Sciences) - Tapa dura

Libro 4 de 11: Springer Series in the Data Sciences

Calin, Ovidiu

 
9783030367206: Deep Learning Architectures: A Mathematical Approach (Springer Series in the Data Sciences)
Tapa dura
ISBN 10: 3030367207 ISBN 13: 9783030367206
Editorial: Springer Nature Switzerland AG, 2020

Sinopsis

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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Acerca del autor

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.

De la contraportada

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.

 

 


"Sobre este título" puede pertenecer a otra edición de este libro.

Editorial
Springer Nature Switzerland AG
Año de publicación
2020
Idioma
Inglés
ISBN 10 
3030367207
ISBN 13 
9783030367206
Encuadernación
Tapa dura
Número de edición
1
Número de páginas
790
Contacto del fabricante
Springer Nature Customer Service Center GmbH
ProductSafety@springernature.com

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Heidelberg
69115
Alemania

Otras ediciones populares con el mismo título

9783030367237: Deep Learning Architectures: A Mathematical Approach (Springer Series in the Data Sciences)

Edición Destacada

ISBN 10:  3030367231 ISBN 13:  9783030367237
Editorial: Springer, 2021
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