Posts in 'programming'

Technical discussions are hard; a few tips

Note

This post discuss the difficulties of communicating while developing open-source projects and tries to gives some simple advice.

A large software project is above all a social exercise in which technical experts try to reach good decisions together, for instance on github pull requests. But communication is difficult, in …

Getting a big scientific prize for open-source software

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An important acknowledgement for a different view of doing science: open, collaborative, and more than a proof of concept.

A few days ago, Loïc Estève, Alexandre Gramfort, Olivier Grisel, Bertrand Thirion, and myself received the “Académie des Sciences Inria prize for transfer”, for our contributions to the scikit-learn project …

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

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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 …