Statistical Mechanics of Neural Networks (Hardcover, 1st ed. 2021)

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.

General

Imprint:	Springer Verlag, Singapore
Country of origin:	Singapore
Release date:	2022
First published:	2021
Authors:	Hai-Ping Huang
Dimensions:	235 x 155mm (L x W)
Format:	Hardcover
Pages:	296
Edition:	1st ed. 2021
ISBN-13:	978-981-16-7569-0
Categories:	Books > Science & Mathematics > Physics > General Books > Science & Mathematics > Mathematics > Applied mathematics > Mathematical modelling Books > Science & Mathematics > Chemistry > Physical chemistry > Quantum & theoretical chemistry Books > Computing & IT > Applications of computing > Artificial intelligence > General
LSN:	981-16-7569-4
Barcode:	9789811675690

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