Mathematical Aspects of Deep Learning

ISBN 10: 1316516784 / ISBN 13: 9781316516782

Published by Cambridge University Press, 2023

The book has been read, but is in excellent condition. Pages are intact and not marred by notes or highlighting. The spine remains undamaged. Seller Inventory # GOR014619351

Synopsis:

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.

About the Authors: 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.

Bibliographic Details

Title: Mathematical Aspects of Deep Learning
Publisher: Cambridge University Press
Publication Date: 2023
Binding: Paperback
Condition: Very Good