Format:
1 Online-Ressource (VI, 126 Seiten)
,
Illustrationen
ISBN:
9783111025803
,
9783111025551
Series Statement:
De Gruyter Textbook
Content:
The goal of this book is to provide a mathematical perspective on some key elements of the so-called deep neural networks (DNNs). Much of the interest in deep learning has focused on the implementation of DNN-based algorithms. Our hope is that this compact textbook will offer a complementary point of view that emphasizes the underlying mathematical ideas. We believe that a more foundational perspective will help to answer important questions that have only received empirical answers so far. The material is based on a one-semester course Introduction to Mathematics of Deep Learning" for senior undergraduate mathematics majors and first year graduate students in mathematics. Our goal is to introduce basic concepts from deep learning in a rigorous mathematical fashion, e.g introduce mathematical definitions of deep neural networks (DNNs), loss functions, the backpropagation algorithm, etc. We attempt to identify for each concept the simplest setting that minimizes technicalities but still contains the key mathematics.
Note:
Literaturverzeichnis: Seite [121]-124
,
In English
Additional Edition:
ISBN 9783111024318
Additional Edition:
Erscheint auch als Druck-Ausgabe Berlyand, Leonid Mathematics of deep learning Berlin : De Gruyter, 2023 ISBN 9783111024318
Language:
English
Subjects:
Computer Science
,
Mathematics
Keywords:
Deep Learning
;
Maschinelles Lernen
;
Künstliche Intelligenz
;
Neuronales Netz
;
Angewandte Mathematik
DOI:
10.1515/9783111025551
