Open Access Week is on and this year’s motto is „Open in Action“. Thus we’ll take the chance to feature a really interesting open science project we’ve recently stumbled upon: OpenML. OpenML sets out (and actually already achieved) to make machine learning available to a broader audience (especially scientists) and build a platform to create, share, evaluate and use machine learning algorithms. We took the chance to talk to Heidi Seibold and Joaquin Vanschoren about the project’s history, current state and future plans! Feel invited to give OpenML a chance, test it, or contribute to it. For now, enjoy this episode!
P.S.: we had to partly switch to a backup recording option, hopefully you don’t mind too much.
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Teilnehmer
Matthias Fromm
Twitter
GitHub
Website
ORCiD
Linkedin
Website
Thomann Wishlist
Amazon Wishlist
Konrad Förstner
Twitter
Website
GitHub
ORCiD
ResearchGate
Heidi Seibold
Twitter
GitHub
Website
ORCiD
Joaquin Vanschoren
Twitter
GitHub
Website
Linkedin
ResearchGate
Shownotes: OSR059 OpenML [EN]
Open Access Week; — OpenML;.
Introduction Heidi 00:01:16
Heidi Seibold; — Heidi on Twitter;.
Introduction Joaquin 00:02:18
Joaquin Vanshoren; — Joaquin on Twitter;.
Machine Learning 00:03:14
Machine Learning; — Neural Networks;.
The Basics of OpenML 00:06:36
Polymath; — Tim Gowers; — Bernd Bischl; — R Project for Statistical Computing; — OpenML Github Repository; — OpenML Community on Google groups;.
Working with and on OpenML 00:18:34
Contributions; — Github DOI; — Altmetric; — Users statistics; — Comparing and evaluating machine learning algorithms; — International Conference on Machine Learning ICML; — European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases ECMLPKDD;.
Linked Open Data; — Integrate OpenML into machine learning tools; — Funding Opportunities;.
OpenML Blog; — Jupyter Notebooks; — OpenML on Youtube;.