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Deep Learning Architectures
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Deep Learning Architectures : A Mathematical Approach

1st ed. 2020

by Ovidiu Calin

Book Details

Format Paperback / Softback
Book Series Springer Series in the Data Sciences
ISBN-10 3030367231
ISBN-13 9783030367237
Edition 1st ed. 2020
Publisher Springer Nature Switzerland AG
Imprint Springer Nature Switzerland AG
Country of Manufacture GB
Country of Publication GB
Publication Date Feb 14th, 2021
Print length 760 Pages
Weight 1,446 grams
Dimensions 25.40 x 17.60 x 4.80 cms
Product Classification: Mathematical theory of computation Machine learning
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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.    

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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