Hugendubel.info - Die B2B Online-Buchhandlung 

Merkliste
Die Merkliste ist leer.
Bitte warten - die Druckansicht der Seite wird vorbereitet.
Der Druckdialog öffnet sich, sobald die Seite vollständig geladen wurde.
Sollte die Druckvorschau unvollständig sein, bitte schliessen und "Erneut drucken" wählen.

Deep Learning Architectures

A Mathematical Approach
BuchGebunden
760 Seiten
Englisch
Springererschienen am14.02.20201st ed. 2020
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.mehr
Verfügbare Formate
BuchGebunden
EUR48,14
BuchKartoniert, Paperback
EUR32,09
E-BookPDF1 - PDF WatermarkE-Book
EUR69,54

Produkt

KlappentextThis 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.
Zusammenfassung
Contains a fair number of end-of chapter exercises

Full solutions provided to all exercises

Appendices including topics needed in the book exposition
Details
ISBN/GTIN978-3-030-36720-6
ProduktartBuch
EinbandartGebunden
Verlag
Erscheinungsjahr2020
Erscheinungsdatum14.02.2020
Auflage1st ed. 2020
ReiheSpringer Series in the Data Sciences
Seiten760 Seiten
SpracheEnglisch
Gewicht1816 g
IllustrationenXXX, 760 p. 207 illus., 35 illus. in color.
Artikel-Nr.47680766

Inhalt/Kritik

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.mehr
Kritik
"This book is useful to students who have already had an introductory course in machine learning and are further interested to deepen their understanding of the machine learning material from the mathematical point of view." (T. C. Mohan, zbMATH 1441.68001, 2020)mehr

Autor

Ovidiu Calin, a graduate from University of Toronto, is a professor at Eastern Michigan University and a former visiting professor at Princeton University and University of Notre Dame. He has delivered numerous lectures at several universities in Japan, Hong Kong, Taiwan, and Kuwait over the last 15 years. His publications include over 60 articles and 8 books in the fields of machine learning, computational finance, stochastic processes, variational calculus and geometric analysis.