Deep Learning Architectures: A Mathematical Approach - Springer Series in the Data Sciences - Ovidiu Calin - Knjige - Springer Nature Switzerland AG - 9783030367237 - 14. februarja 2021
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Ovidiu Calin
Deep Learning Architectures: A Mathematical Approach - Springer Series in the Data Sciences 1st ed. 2020 edition

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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.

 

 


760 pages, 35 Illustrations, color; 172 Illustrations, black and white; XXX, 760 p. 207 illus., 35 i

Medij Knjige     Paperback Book   (Knjiga z mehkimi platnicami in lepljenim hrbtom)
Izdano 14. februarja 2021
ISBN13 9783030367237
Založniki Springer Nature Switzerland AG
Strani 760
Dimenzije 176 × 254 × 48 mm   ·   1,45 kg
Jezik Nemščina  

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