Deeep learning and Boltzmann machines
Contents
Plans for the week of March 31-April 4, 2025
Alternatives for project 2
Boltzmann Machines
The network
Joint distribution
Network Elements, the energy function
Defining different types of RBMs
Cost function
Optimization / Training
Kullback-Leibler relative entropy
Setting up for gradient descent calculations
Mathematical details
Marginal Probability Density Functions
Conditional Probability Density Functions
Gaussian-Binary Restricted Boltzmann Machines
Joint Probability Density Function
Marginal Probability Density Functions
Conditional Probability Density Functions
Neural Quantum States
Cost function
Python version for the two non-interacting particles
Plans for the week of March 31-April 4, 2025
Discussions of various variants of project 2
Neural Networks and Boltzmann Machines, introduction to Boltzmann machines
Video of lecture TBA
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