Coding Bayesian Optimization (Bayes Opt) with BOTORCH - Python example for hyperparameter tuning

Bayesian Optimization is one of the most common optimization algorithms. While there are some black box packages for using it they don't allow a lot of custom changes and are not well suited for all problems. Facebook AI released a library called Botorch which enables the customization of all different layers of Bayes Opt (from GP-model up to the acquisition function). In this video, you get a top-level overview of how to code a Bayesian optimization from scratch and what to have in mind. Based on this knowledge you can then dive deeper into the single subparts to improve your own algorithm. It is a python based library! Theory for BayesOpt: https://www.youtube.com/watch?v=M-NTkxfd7-8 BOTORCH: https://botorch.org Links for Chapters: 0:00 Intro 0:35 Show test function 2:26 Generate initial samples 7:05 One Bayes Opt iteration 17:56 Optimization Loop 28:55 Outro ------------------------------------------------------------------------------- Data Science to go: https://paretos.com