Bayesian Optimization for Machine Learning : A Practical Guidebook

December 14, 2016 ยท Declared Dead ยท ๐Ÿ› arXiv.org

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Authors Ian Dewancker, Michael McCourt, Scott Clark arXiv ID 1612.04858 Category cs.LG: Machine Learning Citations 74 Venue arXiv.org Last Checked 5 months ago
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.
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