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Deep Learning Architectures
A Mathematical Approach
Buch von Ovidiu Calin
Sprache: Englisch

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Beschreibung

This book describes how neural networks operate from the mathematical point of view. As a result, neural networks can be interpreted both as function universal approximators and information processors. The book bridges the gap between ideas and concepts of neural networks, which are used nowadays at an intuitive level, and the precise modern mathematical language, presenting the best practices of the former and enjoying the robustness and elegance of the latter.

This book can be used in a graduate course in deep learning, with the first few parts being accessible to senior undergraduates. In addition, the book will be of wide interest to machine learning researchers who are interested in a theoretical understanding of the subject.

This book describes how neural networks operate from the mathematical point of view. As a result, neural networks can be interpreted both as function universal approximators and information processors. The book bridges the gap between ideas and concepts of neural networks, which are used nowadays at an intuitive level, and the precise modern mathematical language, presenting the best practices of the former and enjoying the robustness and elegance of the latter.

This book can be used in a graduate course in deep learning, with the first few parts being accessible to senior undergraduates. In addition, the book will be of wide interest to machine learning researchers who are interested in a theoretical understanding of the subject.

Inhaltsverzeichnis
Introductory Problems.- Activation Functions.- Cost Functions.- Finding Minima Algorithms.- Abstract Neurons.- Neural Networks.- Approximation Theorems.- Learning with One-dimensional Inputs.- Universal Approximators.- Exact Learning.- Information Representation.- Information Capacity Assessment.- Output Manifolds.- Neuromanifolds.- Pooling.- Convolutional Networks.- Recurrent Neural Networks.- Classification.- Generative Models.- Stochastic Networks.- Hints and Solutions.
Details
Erscheinungsjahr: 2020
Fachbereich: Allgemeines
Genre: Mathematik
Rubrik: Naturwissenschaften & Technik
Medium: Buch
Inhalt: xxx
760 S.
172 s/w Illustr.
35 farbige Illustr.
760 p. 207 illus.
35 illus. in color.
ISBN-13: 9783030367206
ISBN-10: 3030367207
Sprache: Englisch
Herstellernummer: 978-3-030-36720-6
Autor: Calin, Ovidiu
Auflage: 1st ed. 2020
Hersteller: Springer
Springer, Berlin
Springer International Publishing
Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, D-69121 Heidelberg, juergen.hartmann@springer.com
Abbildungen: XXX, 760 p. 207 illus., 35 illus. in color.
Maße: 44 x 159 x 241 mm
Von/Mit: Ovidiu Calin
Erscheinungsdatum: 14.02.2020
Gewicht: 1,816 kg
Artikel-ID: 117630246
Inhaltsverzeichnis
Introductory Problems.- Activation Functions.- Cost Functions.- Finding Minima Algorithms.- Abstract Neurons.- Neural Networks.- Approximation Theorems.- Learning with One-dimensional Inputs.- Universal Approximators.- Exact Learning.- Information Representation.- Information Capacity Assessment.- Output Manifolds.- Neuromanifolds.- Pooling.- Convolutional Networks.- Recurrent Neural Networks.- Classification.- Generative Models.- Stochastic Networks.- Hints and Solutions.
Details
Erscheinungsjahr: 2020
Fachbereich: Allgemeines
Genre: Mathematik
Rubrik: Naturwissenschaften & Technik
Medium: Buch
Inhalt: xxx
760 S.
172 s/w Illustr.
35 farbige Illustr.
760 p. 207 illus.
35 illus. in color.
ISBN-13: 9783030367206
ISBN-10: 3030367207
Sprache: Englisch
Herstellernummer: 978-3-030-36720-6
Autor: Calin, Ovidiu
Auflage: 1st ed. 2020
Hersteller: Springer
Springer, Berlin
Springer International Publishing
Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, D-69121 Heidelberg, juergen.hartmann@springer.com
Abbildungen: XXX, 760 p. 207 illus., 35 illus. in color.
Maße: 44 x 159 x 241 mm
Von/Mit: Ovidiu Calin
Erscheinungsdatum: 14.02.2020
Gewicht: 1,816 kg
Artikel-ID: 117630246
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