Code written by Roland
Hidden Markov Models
in Python. Allows discrete and Gaussian observations and training on a single or on multiple observation sequences.
Ultra-fast hebbian K-means algorithm
in about 10 lines of code.
Example application script
Gaussian mixture models
A GPU implementation of a
gated Boltzmann machine
. Code written jointly with Josh Susskind. The module uses Volodymyr Mnih's cudamat package.
A slightly older CPU implementation of a gated Boltzmann machine along with examples on
is a Python port of Carl Rasmussen's
Code for computing
kernel information embeddings
. GPU-enabled through Tijmen Tieleman's gnumpy package.
model is a very large mixture of logistic regressors with weight sharing. This implementation uses Volodymyr Mnih's cudamat package for training on GPUs.
I have been using
for almost all of my research since about 2005.
Here are some slides and code-snippets from a ten-minute mini-tutorial I gave to the machine learning group in Toronto in 2007 on
switching from Matlab to Python