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A collection of Python code and machine learning exercises
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This repository contains code samples and supporting documentation for the series on machine learning and AI on my blog Specifically, you will find the following ressources here.

  • An implementation of a restricted Boltzmann machine (RBM) in the RBM subdirectory, supporting different algorithms (ordinary contrastive divergence and persistent contrastive divergence (PCD)) using pure Python executing on a CPU and TensorFlow executing on a GPU (PCD only at the moment)
  • A Python implementation of an Ising model simulation
  • A Hopfield network in Python
  • the directory FeedForward that contains sample code related to classical feed forward networks like logistic regression, multinomial regression and layered networks
  • various notebooks that I use on my blog for the sake of illustration
  • and some additional short samples that I refer to in my blob
  • finally a directory with additional documentation in LaTex, including material on Boltzmann machines, Backpropagation, more on Ising models, a short introduction to Markov chains and Markov chain Monte Carlo methods, the EM algorithm for Gaussian mixed models and a short introduction into statistical physics and thermodynamics

Of course all these implementations and documents have been created for educational purposes and are not intended for production use - feel free to play with them and modify them if needed. When you use or cite this code or the documentation, please add a reference to this site or my blog

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