L'Agenda du Libre

PyData Paris / Scikit Learn Fondamentaux scientifiques et réussites industrielles

Programme dirigé par Stéfane Fermigier (Abilian et président du GT Logiciel libre de Systematic), avec Nicolas Chauvat (Logilab), Jean-Paul Smets (Nexedi), Gaël Varoquaux et Alexandre Gramfort (Scikit-Learn), Florian Douetteau (Dataiku)

Keynote speakers

Two keynote speeches by three (1+2) leaders of the PyData community, to get us inspired by the bright future of Python in Big Data and Machine Learning:

• Francesc Alted (UberResearch GmbH): Handling Big Data, more from the point of view of technological handicaps (different hardware, software, architectural paradigms for databases...)
• Gaël Varoquaux (Inria) & Alexandre Gramfort (Telecom ParisTech) : Scikit-learn for easy machine learning: the vision, the tools its development

Speakers

16 talks and roundtables that will cover a broad range of topics: high performance computing (HPC) with Python, machine learning, statistics, dealing with messy data, industry use cases...

Technical track(s)

• Alexandre Gramfort (Telecom ParisTech): "Linear predictions with scikit-learn: simple and efficient"
• Gilles Louppes (CERN): Tree models with scikit-learn: great learners with little assumptions
• Kirill Smelkov (Nexedi): "out-of-core NumPy arrays without changing your code with wendelin-core"
• Antoine Pitrou (Continuum Analytics): "Numba, a JIT compiler for fast numerical code"
• Ian Oswvald : "Cleaning Confused Collections of Characters"
• Serge Guelton and Pierrick Brunet: "Pythran: Static Compilation of Parallel Scientific Kernels"

Business track : Industrial uses of Scikit-Learn

• Julien Sananikone (PriceMinister),
• Benjamin Guinebertière (Microsoft),
• Samuel Charron (Data Publica),
• Thomas Cabrol (Dataiku),
• Jean-Paul Smets & Sébastien Robin (Nexedi): Industrial Monitoring with the Wendelin Big Data platform

INSCRIVEZ-VOUS !

Lieu : Telecom Paristech, 46 rue Barrault, 75013 Paris