Posts in 'programming'

A foundation for scikit-learn at Inria

We have just announced that a foundation will be supporting scikit-learn at Inria [1]: scikit-learn.fondation-inria.fr

Growth and sustainability

This is an exciting turn for us, because it enables us to receive private funding. As a result, we will be able to have secure employment for some existing core …

Sprint on scikit-learn, in Paris and Austin

Two weeks ago, we held a scikit-learn sprint in Austin and Paris. Here is a brief report, on progresses and challenges.

Several sprints

We actually held two sprint in Austin: one open sprint, at the scipy conference sprints, which was open to new contributors, and one core sprint, for more …

Beyond computational reproducibility, let us aim for reusability

Note

Scientific progress calls for reproducing results. Due to limited resources, this is difficult even in computational sciences. Yet, reproducibility is only a means to an end. It is not enough by itself to enable new scientific results. Rather, new discoveries must build on reuse and modification of the state …

Scikit-learn Paris sprint 2017

Two week ago, we held in Paris a large international sprint on scikit-learn. It was incredibly productive and fun, as always. We are still busy merging in the work, but I think that know is a good time to try to summarize the sprint.

A massive workforce

We had a …

Data science instrumenting social media for advertising is responsible for todays politics

To my friends developing data science for the social media, marketing, and advertising industries,

It is time to accept that we have our share of responsibility in the outcome of the US elections and the vote on Brexit. We are not creating the society that we would like. Facebook, Twitter …

Better Python compressed persistence in joblib

New persistence in joblib enables low-overhead storage of big data contained in arbitrary objects

Of software and Science. Reproducible science: what, why, and how

At MLOSS 15 we brainstormed on reproducible science, discussing why we care about software in computer science. Here is a summary blending notes from the discussions with my opinion.

“Without engineering, science is not more than philosophy”     —     the community

How do we enable better Science? Why do we do software …

Nilearn 0.2: more powerful machine learning for neuroimaging

After 6 months of efforts, We just released version 0.2 of nilearn, dedicated to making machine learning in neuroimaging easier and more powerful.

This release integrates the features of the july sprint, and more.

Highlights

Better documentation …

MLOSS 2015: wising up to building open-source machine learning

Note

The 2015 edition of the machine learning open source software (MLOSS) workshop was full of very mature discussions that I strive to report here.

I give links to the videos. Some machine-learning researchers have great thoughts about growing communities of coders, about code as a process and a deliverable …

Nilearn sprint: hacking neuroimaging machine learning

A couple of weeks ago, we had in Paris the second international nilearn sprint, dedicated to making machine learning in neuroimaging easier and more powerful.

It was such a fantastic experience, as nilearn is really shaping up as a simple yet powerful tool, and there is a lot of enthusiasm …