A week ago, the 2014 edition of the scikit-learn sprint was held in Paris. This was the third time that we held an internation sprint and it was hugely productive, and great fun, as always.
Great people and great venues
We had a mix of core contributors and newcomers, which is a great combination, as it enables [...]
We have just released the 0.15 version of scikit-learn. Hurray!! Thanks to all involved.
A long development stretch
It’s been a while since the last release of scikit-learn. So a lot has happened. Exactly 2611 commits according my count.
Quite clearly, we have more and more existing code, more and more features to support. This means that when [...]
I’d like to welcome the four students that were accepted for the GSoC this year:
Issam: Extending Neural networks
Hamzeh: Sparse Support for Ensemble Methods
Manoj: Making Linear models faster
Maheshakya: Locality Sensitive Hashing
Welcome to all of you. Your submissions were excellent, and you demonstrated a good will to integrate in the project, with its social and coding dynamics. It is [...]
Work with us on putting machine learning in the hand of cognitive scientists
Parietal is a research team that creates advanced data analysis to mine functional brain images and solve medical and cognitive science problems. Our day to day work is to write machine-learning and statistics code to understand and use better images of brain function [...]
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 [...]
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 [...]