Bayesian optimization with Scikit-Optimize
summary
Covers Bayesian optimization as a strategy for efficiently minimizing very expensive objective functions, using the scikit-optimize Python library. Explains when Bayesian optimization is appropriate, what makes it “Bayesian,” how statistical surrogate models and acquisition functions drive exploration vs. exploitation, and demonstrates tuning neural-network hyperparameters with scikit-optimize.
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