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Mathematical aspects of deep learning

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Mathematical aspects of deep learningedited by Philipp Grohs, Gitta Kutyniok.
其他作者:	Grohs, Philipp.
出版者:	Cambridge :Cambridge University Press,2023.
面頁冊數:	xviii, 473 p. :ill., digital ;26 cm.
附註:	Title from publisher's bibliographic system (viewed on 30 Nov 2022).
標題:	Deep learning (Machine learning)
電子資源:	https://doi.org/10.1017/9781009025096
ISBN:	9781009025096

Mathematical aspects of deep learning
Mathematical aspects of deep learning[electronic resource] /edited by Philipp Grohs, Gitta Kutyniok. - Cambridge :Cambridge University Press,2023. - xviii, 473 p. :ill., digital ;26 cm.

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

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.

ISBN: 9781009025096Subjects--Topical Terms:

913129
Deep learning (Machine learning)

LC Class. No.: Q325.73 / .M38 2023

Dewey Class. No.: 006.31

Mathematical aspects of deep learning
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245 0 0 $a Mathematical aspects of deep learning $h [electronic resource] / $c edited by Philipp Grohs, Gitta Kutyniok.
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500 $a Title from publisher's bibliographic system (viewed on 30 Nov 2022).
520 $a 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.
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