Mathematical aspects of deep learning / edited by Philipp Grohs, Gitta Kutyniok.

Format:

Book

Contributor:

Grohs, Philipp, editor.

Kutyniok, Gitta, editor.

Language:

English

Subjects (All):

Deep learning (Machine learning).

Mathematics.

Physical Description:

1 online resource (xviii, 473 pages) : digital, PDF file(s).

Edition:

First edition.

Place of Publication:

Cambridge : Cambridge University Press, 2023.

Summary:

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.

Notes:

Title from publisher's bibliographic system (viewed on 30 Nov 2022).

Includes bibliographical references.

ISBN:

9781009035682

1009035681

9781009025096

1009025090