Bayesian Optimization for Machine Learning : A Practical Guidebook
Machine Learning
2016-12-16 v1
Abstract
The engineering of machine learning systems is still a nascent field; relying on a seemingly daunting collection of quickly evolving tools and best practices. It is our hope that this guidebook will serve as a useful resource for machine learning practitioners looking to take advantage of Bayesian optimization techniques. We outline four example machine learning problems that can be solved using open source machine learning libraries, and highlight the benefits of using Bayesian optimization in the context of these common machine learning applications.
Cite
@article{arxiv.1612.04858,
title = {Bayesian Optimization for Machine Learning : A Practical Guidebook},
author = {Ian Dewancker and Michael McCourt and Scott Clark},
journal= {arXiv preprint arXiv:1612.04858},
year = {2016}
}