Thanks for the summary. I was there as well, and it seemed like scikit-learn had a strong showing. It seemed as though many talks that weren't directly on scikit-learn still mentioned it or used the models during the presentation.
On Fri, Jun 30, 2017 at 9:47 AM, Francois Dion <francois.d...@gmail.com> wrote: > This past weekend was the Numfocus sponsored Data Intelligence conference > at Capital One, in Mclean, Virginia (close to Washington DC for those not > familiar with the US geography). > > A few presentations mentioned/used scikit-learn, including Ben Bengfort's > Visual Pipelines ( > http://data-intelligence.ai/presentations/13 ), Zachary Beaver's Airflow > + Scikit-Learn ( http://data-intelligence.ai/presentations/19 ) and > Pramit Choudary's Learning to Learn Model Behavior ( > http://data-intelligence.ai/presentations/22 ), to name a few. > > I presented "Seeking Exotics" on Sunday (http://data-intelligence.ai/ > presentations/21), on anomalous and erroneous data, and how statistics, > visualizations and scikit-learn can help (covered PCA, truncatedSVD, t-sne, > ellipticenvelope, one class classifiers and scikit-learn related > imbalanced-learn and sk-sos). > > One of the slide I had up resonated quite a bit with the audience, both in > person and on social media: > > https://twitter.com/tnfilipiak/status/878999245076008960 > > The notebooks are on github: https://github.com/fdion/seeking_exotics > > > Francois > -- > @f_dion - https://about.me/francois.dion - https://www.linkedin.com/in/ > francois-dion-8b639b79/ > > > _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn > >
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