Statistical Mechanics of Neural Networks

Lieferzeit: Lieferbar innerhalb 14 Tagen

160,49 

ISBN: 9811675694
ISBN 13: 9789811675690
Autor: Huang, Haiping
Verlag: Springer Verlag GmbH
Umfang: xviii, 296 S., 22 s/w Illustr., 40 farbige Illustr., 296 p. 62 illus., 40 illus. in color.
Erscheinungsdatum: 05.01.2022
Auflage: 1/2022
Produktform: Gebunden/Hardback
Einband: Gebunden

This book highlights a comprehensive introduction to the fundamental statistical mechanics underneath the inner workings of neural networks. The book discusses in details important concepts and techniques including the cavity method, the mean-field theory, replica techniques, the Nishimori condition, variational methods, the dynamical mean-field theory, unsupervised learning, associative memory models, perceptron models, the chaos theory of recurrent neural networks, and eigen-spectrums of neural networks, walking new learners through the theories and must-have skillsets to understand and use neural networks. The book focuses on quantitative frameworks of neural network models where the underlying mechanisms can be precisely isolated by physics of mathematical beauty and theoretical predictions. It is a good reference for students, researchers, and practitioners in the area of neural networks.

Artikelnummer: 2912733 Kategorie:

Beschreibung

Autorenporträt

Haiping HuangDr. Haiping Huang received his Ph.D. degree in theoretical physics from the Institute of Theoretical Physics, the Chinese Academy of Sciences. He works as an associate professor at the School of Physics, Sun Yat-sen University, China. His research interests include the origin of the computational hardness of the binary perceptron model, the theory of dimension reduction in deep neural networks, and inherent symmetry breaking in unsupervised learning. In 2021, he was awarded Excellent Young Scientists Fund by National Natural Science Foundation of China.

Herstellerkennzeichnung:


Springer Verlag GmbH
Tiergartenstr. 17
69121 Heidelberg
DE

E-Mail: juergen.hartmann@springer.com

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