Deep Learning Architectures: A Mathematical Approach - Springer Series in the Data Sciences - Ovidiu Calin - Grāmatas - Springer Nature Switzerland AG - 9783030367237 - 2021. gada 14. februāris
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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

Mediji Grāmatas     Paperback Book   (Grāmata ar mīksto vāku un līmēto muguru)
Izlaists 2021. gada 14. februāris
ISBN13 9783030367237
Izdevēji Springer Nature Switzerland AG
Lapas 760
Izmēri 176 × 254 × 48 mm   ·   1,45 kg
Valoda Vācu  

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