Deep Learning Architectures: A Mathematical Approach Paperback - 2021
by Ovidiu Calin
From the rear cover
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.
Details
- Title Deep Learning Architectures: A Mathematical Approach
- Author Ovidiu Calin
- Binding Paperback
- Pages 760
- Volumes 1
- Language ENG
- Publisher Springer
- Date 2021-02-14
- Illustrated Yes
- Features Illustrated
- ISBN 9783030367237 / 3030367231
- Weight 2.4 lbs (1.09 kg)
- Dimensions 9.21 x 6.14 x 1.57 in (23.39 x 15.60 x 3.99 cm)
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