Bayesian Optimization (Bayes Opt): Easy explanation of popular hyperparameter tuning method

Bayesian Optimization is one of the most popular approaches to tune hyperparameters in machine learning. Still, it can be applied in several areas for single objective black-box optimization. In this video we explain you the basic methodology and show based on a specific example how it works. We focus especially on the acquisition function and also the difference of optimization performance using hyperparameters within the Bayesian optimization. This video aims not only to give you a better understanding of Bayesian Optimization but also to give a better feeling when it should be applied in which way. LinkedIn: https://www.linkedin.com/in/fabianrang/ GitLab: https://gitlab.com/youtube-optimization-geeks ------------------------------------------------------------------------------- Data Science to go: https://paretos.com