[Baypiggies] Tomorrow night's meeting: Reduce sklearn training time

[Jeff Fischer]

*Thursday, March 25th 7:00 pm to 8:30 pm*

This month, we'll have a talk from Michael Galarnyk of Anyscale about three
ways to reduce the time it takes to do training for the popular
Scikit-Learn machine learning library.
*Talk Description*
Scikit-Learn is an easy to use Python library for machine learning.
However, sometimes scikit-learn models can take a long time to train. There
are quite a few approaches to solving this problem like:


   - Changing your optimization function (solver)
   - Using different hyperparameter optimization techniques (grid search,
   random search, early stopping, etc)
   - Parallelize or distribute your training with joblib and Ray

Attendees will learn about each approach, the advantages and disadvantages
of each, and how to speed up their scikit-learn workflow.

*Speaker Bio*
Michael Galarnyk works in Developer Relations at Anyscale
<https://www.anyscale.com/>, the company behind the Ray Project. In his
spare time, he teaches Python based Machine Learning classes through
Stanford Continuing Studies and LinkedIn Learning. You can find him on
Twitter (https://twitter.com/GalarnykMichael), Medium (
https://medium.com/@GalarnykMichael), and GitHub (
https://github.com/mGalarnyk).

*Code of Conduct*
https://baypiggies.net/pages/code_of_conduct.html

Interactions online have less nuance than in-person interactions. Please be
Open, Considerate and Respectful. Also, please refrain from discussing
topics unrelated to the Python community or the technical content of the
meeting.

*RSVP*
We will conduct the meeting via Zoom webinar. Please register in advance at
the following link:
https://stanford.zoom.us/webinar/register/WN_aPzQ-g5fRPeR10GYmPqWTg

It would also be helpful if you responded on our Meetup page for the event:
https://www.meetup.com/BAyPIGgies/events/276852797/

Hope to see you there!
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