Mathematical Aspects of Deep Learning (Hardcover)

Philipp Grohs

ISBN 10: 1316516784 / ISBN 13: 9781316516782

Published by Cambridge University Press, Cambridge, 2022

About this Item

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. The development of a theoretical foundation for deep learning methods constitutes one of the most active and exciting research topics in applied mathematics. Written by leading experts in the field, this book acts as a mathematical introduction to deep learning for researchers and graduate students trying to get into the field. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Seller Inventory # 9781316516782

Bibliographic Details

• Title: Mathematical Aspects of Deep Learning (Hardcover)
• Publisher: Cambridge University Press, Cambridge
• Publication Date: 2022
• Language: English
• ISBN 10: 1316516784
• ISBN 13: 9781316516782
• Binding: Hardcover
• Condition: new

About this title

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.