Statistical mechanics of neural networks / Haiping Huang.
Huang, Haiping, author.
2021
QA76.87 .H83 2021
Available Online

Linked e-resources

Linked Resource Online Access
Concurrent users Unlimited
Authorized users Authorized users
Document Delivery Supplied Can lend chapters, not whole ebooks

Details

Title Statistical mechanics of neural networks / Haiping Huang.
Author Huang, Haiping, author.
ISBN 9789811675706 electronic book
9811675708 electronic book
9789811675690
9811675694
DOI https://doi.org/10.1007/978-981-16-7570-6
Published Singapore : Springer, [2021]
Copyright ©2021
Language English
Description 1 online resource (302 pages) : illustrations (some color)
Item Number 10.1007/978-981-16-7570-6 doi
Call Number QA76.87 .H83 2021
Dewey Decimal Classification 006.32
Summary 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.
Bibliography, etc. Note Includes bibliographical references.
Access Note Access limited to authorized users.
Digital File Characteristics text file PDF
Source of Description Description based on online resource; title from digital title page (viewed on January 25, 2022).
Available in Other Form Statistical Mechanics of Neural Networks
Linked Resources Online Access
Record Appears in Online Resources > Ebooks
All Resources
Introduction
Spin glass models and cavity method
Variational mean-eld theory and belief propagation
Monte Carlo simulation methods
High-temperature expansion
Nishimori line
Random energy model
Statistical mechanical theory of Hopeld 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-eld theory of Ising Perceptron
Mean-eld model of multi-layered Perceptron
Mean-eld theory of dimension reduction
Chaos theory of random recurrent neural networks
Statistical mechanics of random matrices
Perspectives.

Browse Subjects

Show more subjects...

Statistics

from
to
Export