Peekaboo

As promised, my group recently published our Restricted Boltzmann Machine implementation . It is based upon the CUV Library that is being developed here. The idea is to combine the ease of programming of Python with the computing power of the GPU. We used this implementation for several papers and it grew a lot over time. Here is a list of most of the features: Restricted Boltzmann Machine Training With n-step Contrastive Divergence With persistent Contrastive Divergence Weight decay, momentum, batch-learning Binary or gaussian visible nodes Restricted Boltzmann Machine Evaluation Sampling from the model Visualizing Filters Annealed Importance Sampling for approximating the partition function Calculating the partition function exactly Visualization and saving of hidden representations Stacking RBMs to Deep Belief Networks Sampling from DBNs Deep Boltzmann Machine Training With n-step Contrastive Divergence With persistent Contrastive Divergence Deep Boltzmann Ma...

Wow. So much to read today . While following link upon link, I found so many great toolkits that I think it is worth listing them here. One of the greatest sources was the GNU/Linux AI & Alife HOWTO . [edit] It's been a while since I wrote this blog post but still many people seem to find it, so here a quick update. After looking into many libraries, I started using scikit-learn and then using it exclusively. Now I am a regular contributor. It is a fast growing project with great documentation resources, many algorithms and it is just so easy to use. Also, working with Python and the Python crowd is fun. I heartly recommend it. [/edit] Here goes: Vowpal Wabbit - project on very fast online gradient descent by Yahoo research (C++) VFML (Very Fast Machine Learning) - library for very fast decision trees and Bayes networks (C++) Stochastic Gradient Descent - library for SVMs with stochastic gradient descent (C++) Maximum Entropy Modeling Toolkit for Python and C++ - ...