Peekaboo

Yesterday a week-long scikit-learn coding sprint in Paris ended. And let me just say: a week is pretty long for a sprint. I think most of us were pretty exhausted in the end. But we put together a release candidate for 0.14 that Gael Varoquaux tagged last night. You can install it via:   pip install -U https://github.com/scikit-learn/scikit-learn/archive/0.14a1.zip There are also tarballs on github and binaries on sourceforge . If you want the most current version, you can check out the release branch on github: https://github.com/scikit-learn/scikit-learn/tree/0.14.X The full list of changes can be found in what's new . The purpose of  the release candidate is to give users a chance to give us feedback before the release. So please try it out and report back if you have any issues.

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++ - ...