Bayesian Optimization with Python
Define objective function of Bayesian optimization
Implement Bayesian Optimization
Use Bayesian Optimization and GPyOpt in your projects
In this guided project you will get familiar with the basics of Bayesian optimization and Implement Bayesian optimization algorithm process and use it in a machine learning project, We will consider function optimization task and also Hyperparameters tuning using Bayesian optimization and GPyOpt library. Bayesian optimization is a nice topic, whether you want to do a high dimensional or a computationally expensive optimization it's efficient. By the end of this project you will be able to understand and start applying Bayesian optimization in your machine learning projects.
Skills you will develop
Learn step-by-step
In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:
Define Objective function - One Dimensional Case
Optimize 1-D Objective function using GPyopt
Define Objective function - Two Dimensional Case
Optimize 2-D Objective function using GPyopt
Using Bayesian Optimization in Machine Learning
How Guided Projects work
Your workspace is a cloud desktop right in your browser, no download required
In a split-screen video, your instructor guides you step-by-step
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