I have tagged and released the scikit-learn 0.14 release yesterday evening, after more than 6 months of heavy development from the team. I would like to give a quick overview of the highlights of this release in terms of features but also in term of performance. Indeed, the scikit-learn developers believe that performance matters [...]
Archive for the 'machine learning' Category
I am super excited to announce a job offer that is dear to my heart: doing quality open-source software, with Python scientific tools and machine learning, for clinical application of brain imaging. This is the most exciting job that I have had the chance to be recruiting for!
We are looking for a programmer to join [...]
Yesterday, we released version 0.11 of the scikit-learn toolkit for machine learning in Python, and there was much rejoincing.
Major features gained in the last releases
In the last 6 months, there have been many things happening with the scikit-learn. While I do not whish to give an exhaustive summary of features added (it can be found [...]
The scikit-learn got 3 students accepted for the Google summer of code.
Imanuel Bayer will work on making our sparse linear models, for regression and classification, faster. His proposal Optimizing sparse linear models using coordinate descent and strong rules.
David Marek will implement multi-layer perceptrons for the scikit. His proposal: Multilayer Perceptron
Vlad Niculae will work on speeding [...]
At the request of a friend, I am putting up some of the posters that I recently presented at conferences.
Large-scale functional-connectivity graphical models for individual subjects using population prior.
This is a poster for our NIPS work
Multi-subject dictionary learning to segment an atlas of brain spontaneous activity.
This is a poster for our IPMI work
Mayavi for 3D [...]
Once again, we are looking for a junior developer to work on the scikit-learn. Below is the official job posting. As a personal remark, I would like to stress that this is a unique opportunity to be payed for two years to work on learning and improving the scientific Python toolstack.
INRIA is looking to [...]
The Scipy 2011 conference in Austin
Last week, I was at the Scipy conference in Austin. It was really great to see old friends, and Austin is such a nice place.
The Scipy conference was held in UT Austin’s conference center, which is a fantastic venue. This is the first geek’s conference I have been at where [...]
The scikits.learn is a Python module for machine learning. The project builds on the scientific and numerical tools of the scipy community to provide state-of-the-art data analysis tools. It is developed by a community of open source developers to which my research team (Parietal, INRIA) contributes a lot and is a striving project. Its mailing [...]
The scikit-learn team is organizing a sprint on April 1st (that next Friday). Join us in Paris, Boston, or on IRC!
With the rise of the data sciences, the scikit-learn, a BSD-licensed Python package for machine learning, is becoming an asset for more and more endeavors. Machine learning has traditionally been considered as very technical [...]