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Thursday, April 11 • 11:15am - 11:45am
Pymc-Learn: Practical Probabilistic Machine Learning in Python

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Pymc-learn is a Python package providing a variety of state-of-the-art probabilistic models for supervised and unsupervised machine learning. It is inspired by scikit-learn and focuses on bringing probabilistic machine learning to non-specialists. It uses a general-purpose high-level language that mimics scikit-learn. Emphasis is put on ease of use, productivity, flexibility, performance, documentation, and an API consistent with scikit-learn. It depends on scikit-learn and pymc3 and is distributed under the new BSD-3 license, encouraging its use in both academia and industry. Source code, binaries, and documentation are available on http://github.com/pymc-learn/pymc-learn.

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avatar for Daniel Emaasit

Daniel Emaasit

Data Scientist, Haystax
I am a Data Scientist at Haystax in Washington, D.C. My interests involve developing principled probabilistic models for problems where training data are scarce by leveraging knowledge from subject-matter experts and context information. In particular, I am interested in flexible... Read More →


Thursday April 11, 2019 11:15am - 11:45am EDT
Violet Crown: Theater 3 200 W Main St, Charlottesville, VA 22902, USA