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portada Deep Learning Architectures: A Mathematical Approach (Springer Series in the Data Sciences)
Type
Physical Book
Publisher
Author
Year
2020
Language
English
Pages
760
Format
Hardcover
ISBN13
9783030367206
Edition No.
1
Categories

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

Ovidiu Calin (Author) · Springer · Hardcover

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

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Synopsis "Deep Learning Architectures: A Mathematical Approach (Springer Series in the Data Sciences) "

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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