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Using Bayesian Optimization to Tune Machine Learning Models
Recorded at:

| by Scott Clark Follow 0 Followers on Feb 07, 2017 | NOTICE: QCon.ai - Applied AI for Developers Apr 15 - 17, 2019, San Francisco. Join us!
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.

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.

Managing Big Data has become a major competitive advantage for many organizations and hence maintaining a proper analytics platform is vital for an organization's survival. This conference provides insights and potential solutions to address Big Data issues from well known experts and thought leaders through panel sessions and open Q&A sessions.

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