by Dima Kochkov

Boltzmann Machines

Boltzmann Machines represent a class of Neural Networks that can be used for unsupervised learning. Inspired by ideas from physics and neuroscience these nets allow a simple, genuine learning rule. The learning is based on minimization of Kullback–Leibler divergence between learned probability distribution and the dataset.

Presentation Summary

link to pdf


  • A fairly long python tutorial on RBM example
  • Link to a simplistic c++ code


All Machine learning

machine learning energy based models unsupervised learning