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Automated Machine Learning: Methods, Systems, Challenges

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1 Hyperparameter Optimization.- 2 Meta-Learning.- 3 Neural Architecture Search.- 4 Auto-WEKA.- 5 Hyperopt-Sklearn.- 6 Auto-sklearn.- 7 Towards Automatically-Tuned Deep Neural Networks.- 8 TPOT.- 9 The Automatic Statistician.- 10 AutoML Challenges.

236 pages, Paperback

Published May 23, 2019

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Frank Hutter

9 books

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Profile Image for Walter Ullon.
326 reviews162 followers
December 14, 2022
A great resource for a very academic but thorough look at the math, systems, and algorithms tackling the problem of AutoML.

It is essentially a collection of papers, which is both good and bad depending on the audience. For instance, if you are looking for a resource to show you how to implement AutoML in your business or to solve a particular problem, then look elsewhere. You will find no tutorials here.

However, if you are looking for an analysis of the various methods and ensembles out there with the full zeal of academic rigor, then start here.

I have implemented AutoML before reading this book to toy problems, but I never felt like I grasped what the different algorithms were doing (or how) until I read some of these chapters.

It will definitely be a resource I come back to again and again, if not for the content itself, then for the vast trove of references.

Recommended (to the right audience).
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