Statistical Mechanics of Neural Networks

Gebonden Engels 2022 9789811675690
Verwachte levertijd ongeveer 9 werkdagen

Samenvatting

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.

Specificaties

ISBN13:9789811675690
Taal:Engels
Bindwijze:gebonden
Uitgever:Springer Nature Singapore

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Inhoudsopgave

Introduction.- Spin glass models and cavity method.- Variational mean-field theory and belief propagation.- Monte Carlo simulation methods.- High-temperature expansion.- Nishimori line.- Random energy model.- Statistical mechanical theory of Hopfield model.-  Replica symmetry and replica symmetry breaking.- Statistical mechanics of restricted Boltzmann machine.- Simplest model of unsupervised learning with binary synapses.-  Inherent-symmetry breaking in unsupervised learning.- Mean-field theory of Ising Perceptron.- Mean-field model of multi-layered Perceptron.- Mean-field theory of dimension reduction.- Chaos theory of random recurrent neural networks.- Statistical mechanics of random matrices.- Perspectives.

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        Statistical Mechanics of Neural Networks