hyperparameter-tuning
2 free lessons tagged hyperparameter-tuning across AI. Each one is a short sequence of focused steps with narration and a five-question quiz at the end — take them in any order, no signup required.
Bayesian Optimization in Practice
The theory becomes useful through libraries, and its flagship job is tuning machine-learning models. This lesson covers that application, the Tree-structured Parzen Estimator behind Hyperopt and Optuna, the tools you would actually reach for, batching, and the honest limits of the method.
Optimizing an Expensive Black Box
Some functions cost hours or dollars to evaluate once, give no gradient, and must be optimized in as few tries as possible. This lesson sets up that problem, shows why grid and random search waste the budget, and introduces the surrogate-model loop that Bayesian optimization is built on.