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

Libro 4 de 11: Springer Series in the Data Sciences

Ovidiu Calin

 
9783030367237: Deep Learning Architectures: A Mathematical Approach (Springer Series in the Data Sciences)
Tapa blanda
ISBN 10: 3030367231 ISBN 13: 9783030367237
Editorial: Springer Nature B.V., 2021

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 B.V.
Año de publicación
2021
Idioma
Inglés
ISBN 10 
3030367231
ISBN 13 
9783030367237
Encuadernación
Tapa blanda
Número de edición
1
Número de páginas
792
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