Mathematical Aspects of Deep Learning

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Philipp Grohs (Universität Wien, Austria) Gitta Kutyniok (Ludwig-Maximilians-Universität Munchen)

Mathematical Aspects of Deep Learning

Philipp Grohs (Universität Wien, Austria), Gitta Kutyniok (Ludwig-Maximilians-Universität Munchen)

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English

Cambridge University Press
22 December 2022

Calculus & mathematical analysis; Mathematical theory of computation; Machine learning

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.

Edited by: Philipp Grohs (Universität Wien Austria), Gitta Kutyniok (Ludwig-Maximilians-Universität Munchen)
Imprint: Cambridge University Press
Country of Publication: United Kingdom
Edition: New edition
Dimensions: Height: 251mm, Width: 174mm, Spine: 26mm
Weight: 1.070kg
ISBN: 9781316516782
ISBN 10: 1316516784
Pages: 492
Publication Date: 22 December 2022
Audience: College/higher education , Further / Higher Education
Format: Hardback
Publisher's Status: Active

Philipp Grohs is Professor of Applied Mathematics at the University of Vienna and Group Leader of Mathematical Data Science at the Austrian Academy of Sciences. Gitta Kutyniok is Bavarian AI Chair for Mathematical Foundations of Artificial Intelligence at Ludwig-Maximilians Universität München and Adjunct Professor for Machine Learning at the University of Tromsø.