Statistical Mechanics of Neural Networks
portes grátis
Statistical Mechanics of Neural Networks
Huang, Haiping
Springer Verlag, Singapore
01/2022
296
Dura
Inglês
9789811675690
15 a 20 dias
641
Descrição não disponível.
Chapter 1: IntroductionChapter 2: Spin Glass Models and Cavity Method
Chapter 3: Variational Mean-Field Theory and Belief Propagation
Chapter 4: Monte-Carlo Simulation Methods
Chapter 5: High-Temperature Expansion Techniques
Chapter 6: Nishimori Model
Chapter 7: Random Energy Model
Chapter 8: Statistical Mechanics of Hopfield Model
Chapter 9: Replica Symmetry and Symmetry Breaking
Chapter 10: Statistical Mechanics of Restricted Boltzmann Machine
Chapter 11: Simplest Model of Unsupervised Learning with Binary Synapses
Chapter 12: Inherent-Symmetry Breaking in Unsupervised Learning
Chapter 13: Mean-Field Theory of Ising Perceptron
Chapter 14: Mean-Field Model of Multi-Layered Perceptron
Chapter 15: Mean-Field Theory of Dimension Reduction in Neural Networks
Chapter 16: Chaos Theory of Random Recurrent Networks
Chapter 17: Statistical Mechanics of Random Matrices
Chapter 18: Perspectives
Chapter 3: Variational Mean-Field Theory and Belief Propagation
Chapter 4: Monte-Carlo Simulation Methods
Chapter 5: High-Temperature Expansion Techniques
Chapter 6: Nishimori Model
Chapter 7: Random Energy Model
Chapter 8: Statistical Mechanics of Hopfield Model
Chapter 9: Replica Symmetry and Symmetry Breaking
Chapter 10: Statistical Mechanics of Restricted Boltzmann Machine
Chapter 11: Simplest Model of Unsupervised Learning with Binary Synapses
Chapter 12: Inherent-Symmetry Breaking in Unsupervised Learning
Chapter 13: Mean-Field Theory of Ising Perceptron
Chapter 14: Mean-Field Model of Multi-Layered Perceptron
Chapter 15: Mean-Field Theory of Dimension Reduction in Neural Networks
Chapter 16: Chaos Theory of Random Recurrent Networks
Chapter 17: Statistical Mechanics of Random Matrices
Chapter 18: Perspectives
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Unsupervised Learning;Mean-field Theory;Cavity Method;Replica Method;Hopfield Model;Restricted Boltzmann Machine;Random Matrices
Chapter 1: IntroductionChapter 2: Spin Glass Models and Cavity Method
Chapter 3: Variational Mean-Field Theory and Belief Propagation
Chapter 4: Monte-Carlo Simulation Methods
Chapter 5: High-Temperature Expansion Techniques
Chapter 6: Nishimori Model
Chapter 7: Random Energy Model
Chapter 8: Statistical Mechanics of Hopfield Model
Chapter 9: Replica Symmetry and Symmetry Breaking
Chapter 10: Statistical Mechanics of Restricted Boltzmann Machine
Chapter 11: Simplest Model of Unsupervised Learning with Binary Synapses
Chapter 12: Inherent-Symmetry Breaking in Unsupervised Learning
Chapter 13: Mean-Field Theory of Ising Perceptron
Chapter 14: Mean-Field Model of Multi-Layered Perceptron
Chapter 15: Mean-Field Theory of Dimension Reduction in Neural Networks
Chapter 16: Chaos Theory of Random Recurrent Networks
Chapter 17: Statistical Mechanics of Random Matrices
Chapter 18: Perspectives
Chapter 3: Variational Mean-Field Theory and Belief Propagation
Chapter 4: Monte-Carlo Simulation Methods
Chapter 5: High-Temperature Expansion Techniques
Chapter 6: Nishimori Model
Chapter 7: Random Energy Model
Chapter 8: Statistical Mechanics of Hopfield Model
Chapter 9: Replica Symmetry and Symmetry Breaking
Chapter 10: Statistical Mechanics of Restricted Boltzmann Machine
Chapter 11: Simplest Model of Unsupervised Learning with Binary Synapses
Chapter 12: Inherent-Symmetry Breaking in Unsupervised Learning
Chapter 13: Mean-Field Theory of Ising Perceptron
Chapter 14: Mean-Field Model of Multi-Layered Perceptron
Chapter 15: Mean-Field Theory of Dimension Reduction in Neural Networks
Chapter 16: Chaos Theory of Random Recurrent Networks
Chapter 17: Statistical Mechanics of Random Matrices
Chapter 18: Perspectives
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
