Statistical Mechanics of Neural Networks

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...

Full description

Bibliographic Details
Main Author: Huang, Haiping
Format: eBook
Language:English
Published: Singapore Springer Nature Singapore 2021, 2021
Edition:1st ed. 2021
Subjects:
Online Access:
Collection: Springer eBooks 2005- - Collection details see MPG.ReNa
LEADER 02775nmm a2200325 u 4500
001 EB002010470
003 EBX01000000000000001173369
005 00000000000000.0
007 cr|||||||||||||||||||||
008 220201 ||| eng
020 |a 9789811675706 
100 1 |a Huang, Haiping 
245 0 0 |a Statistical Mechanics of Neural Networks  |h Elektronische Ressource  |c by Haiping Huang 
250 |a 1st ed. 2021 
260 |a Singapore  |b Springer Nature Singapore  |c 2021, 2021 
300 |a XVIII, 296 p. 62 illus., 40 illus. in color  |b online resource 
505 0 |a 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 
653 |a Computational intelligence 
653 |a Artificial Intelligence 
653 |a Mathematical Models of Cognitive Processes and Neural Networks 
653 |a Computational Intelligence 
653 |a Statistical Mechanics 
653 |a Neural networks (Computer science)  
653 |a Artificial intelligence 
653 |a Statistical Physics 
041 0 7 |a eng  |2 ISO 639-2 
989 |b Springer  |a Springer eBooks 2005- 
856 4 0 |u https://doi.org/10.1007/978-981-16-7570-6?nosfx=y  |x Verlag  |3 Volltext 
082 0 |a 530.13 
520 |a 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