Mathematical Aspects of Deep Learning (Hardcover)

In recent years the development of new classification and regression algorithms based on deep learning has led to a revolution in the fields of artificial intelligence, machine learning, and data analysis. The development of a theoretical foundation to guarantee the success of these algorithms constitutes one of the most active and exciting research topics in applied mathematics. This book presents the current mathematical understanding of deep learning methods from the point of view of the leading experts in the field. It serves both as a starting point for researchers and graduate students in computer science, mathematics, and statistics trying to get into the field and as an invaluable reference for future research.

General

Imprint:	Cambridge UniversityPress
Country of origin:	United Kingdom
Release date:	December 2022
Editors:	Philipp Grohs • Gitta Kutyniok
Dimensions:	251 x 174 x 26mm (L x W x T)
Format:	Hardcover
Pages:	492
ISBN-13:	978-1-316-51678-2
Categories:	Books > Computing & IT > General theory of computing > Mathematical theory of computation Books > Science & Mathematics > Mathematics > Calculus & mathematical analysis > Vector & tensor analysis Books > Computing & IT > Applications of computing > Artificial intelligence > Machine learning
LSN:	1-316-51678-4
Barcode:	9781316516782

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