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This open access book presents the first comprehensive overview of general methods in Automated Machine Learning (AutoML), collects descriptions of existing systems based on these methods, and discusses the first series of international challenges of AutoML systems.
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.
“This interesting collection should be useful for AutoML researchers seeking an overview and comprehensive bibliography.” (Anoop Malaviya, Computing Reviews, June 14, 2021)