Deep Learning Architectures - A Mathematical Approach (Paperback, 1st ed. 2020)

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.

General

Imprint:	Springer Nature Switzerland AG
Country of origin:	Switzerland
Series:	Springer Series in the Data Sciences
Release date:	February 2021
First published:	2020
Authors:	Ovidiu Calin
Dimensions:	254 x 178mm (L x W)
Format:	Paperback
Pages:	760
Edition:	1st ed. 2020
ISBN-13:	978-3-03-036723-7
Categories:	Books > Science & Mathematics > Mathematics > Applied mathematics > Mathematical modelling Books > Computing & IT > Applications of computing > Artificial intelligence > Machine learning
LSN:	3-03-036723-1
Barcode:	9783030367237

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