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Statistical mechanics of neural networks

Huang, Haiping.

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Statistical mechanics of neural networks

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Statistical mechanics of neural networks/ by Haiping Huang.
作者:	Huang, Haiping.
出版者:	Singapore :Springer Singapore : : 2021.,
面頁冊數:	xviii, 296 p. :ill., digital ;24 cm.
內容註:	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.
Contained By:	Springer Nature eBook
標題:	Neural networks (Computer science) - Statistical methods. -
電子資源:	https://doi.org/10.1007/978-981-16-7570-6
ISBN:	9789811675706

Statistical mechanics of neural networks
Huang, Haiping.

Statistical mechanics of neural networks[electronic resource] /by Haiping Huang. - Singapore :Springer Singapore :2021. - xviii, 296 p. :ill., digital ;24 cm.

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.

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.

ISBN: 9789811675706

Standard No.: 10.1007/978-981-16-7570-6doiSubjects--Topical Terms:

3538276
Neural networks (Computer science)
--Statistical methods.

LC Class. No.: QA76.87 / .H83 2021

Dewey Class. No.: 006.32

Statistical mechanics of neural networks
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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.
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.
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