001441638 000__ 03582cam\a2200517Ii\4500
001441638 001__ 1441638
001441638 003__ OCoLC
001441638 005__ 20230309003337.0
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001441638 008__ 220108s2021\\\\si\a\\\\ob\\\\000\0\eng\d
001441638 019__ $$a1290840213$$a1291147765$$a1291170626$$a1294367521
001441638 020__ $$a9789811675706$$qelectronic book
001441638 020__ $$a9811675708$$qelectronic book
001441638 020__ $$z9789811675690
001441638 020__ $$z9811675694
001441638 0247_ $$a10.1007/978-981-16-7570-6$$2doi
001441638 035__ $$aSP(OCoLC)1291317829
001441638 040__ $$aEBLCP$$beng$$erda$$cEBLCP$$dYDX$$dYDXIT$$dGW5XE$$dOCLCO$$dDCT$$dOCLCF$$dOCLCO$$dOCLCQ
001441638 049__ $$aISEA
001441638 050_4 $$aQA76.87$$b.H83 2021
001441638 08204 $$a006.32$$223
001441638 1001_ $$aHuang, Haiping,$$eauthor.
001441638 24510 $$aStatistical mechanics of neural networks /$$cHaiping Huang.
001441638 264_1 $$aSingapore :$$bSpringer,$$c[2021]
001441638 264_4 $$c©2021
001441638 300__ $$a1 online resource (302 pages) :$$billustrations (some color)
001441638 336__ $$atext$$btxt$$2rdacontent
001441638 337__ $$acomputer$$bc$$2rdamedia
001441638 338__ $$aonline resource$$bcr$$2rdacarrier
001441638 347__ $$atext file$$bPDF$$2rda
001441638 504__ $$aIncludes bibliographical references.
001441638 5050_ $$aIntroduction -- 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.
001441638 506__ $$aAccess limited to authorized users.
001441638 520__ $$aThis 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.
001441638 588__ $$aDescription based on online resource; title from digital title page (viewed on January 25, 2022).
001441638 650_0 $$aNeural networks (Computer science)$$xStatistical methods.
001441638 650_6 $$aRéseaux neuronaux (Informatique)$$xMéthodes statistiques.
001441638 655_0 $$aElectronic books.
001441638 77608 $$iPrint version:$$aHuang, Haiping$$tStatistical Mechanics of Neural Networks$$dSingapore : Springer Singapore Pte. Limited,c2022$$z9789811675690
001441638 852__ $$bebk
001441638 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-981-16-7570-6$$zOnline Access$$91397441.1
001441638 909CO $$ooai:library.usi.edu:1441638$$pGLOBAL_SET
001441638 980__ $$aBIB
001441638 980__ $$aEBOOK
001441638 982__ $$aEbook
001441638 983__ $$aOnline
001441638 994__ $$a92$$bISE