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Using Bayesian Optimization to Tune Machine Learning Models
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| by Scott Clark Follow 0 Followers on Feb 07, 2017 |
38:49

Summary
Scott Clark introduces Bayesian Global Optimization as an efficient way to optimize ML model parameters, explaining the underlying techniques and comparing it to other standard methods.

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Bio

Scott Clark has been applying optimal learning techniques in industry and academia for years, from bioinformatics to production advertising systems. Before SigOpt, Scott worked on the Ad Targeting team at Yelp leading the charge on academic research and outreach with projects like the Yelp Dataset Challenge and open sourcing MOE. Scott was chosen as one of Forbes' 30 under 30 in 2016.

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