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Hyperparameter Optimization Algorithms (2D)

2D top-down heatmap of a validation-loss surface: watch grid search, random search and Bayesian optimization sample it live, with a readout tracking trials, best loss and the winning hyperparameters.

AI & Machine Learning2DModerate60 FPS📱 Mobile-adapted⇄ 3D version

This 2D companion renders the same validation-loss landscape as the 3D version — a bowl distorted by a seeded ripple over learning rate and regularization strength — as a flat top-down heatmap instead of a rotating terrain, so the sampling pattern of each search algorithm reads at a glance: grid search's lattice, random search's scatter, and Bayesian optimization's clustering around promising basins with occasional exploratory jumps. A live readout tracks trial count, best loss, the winning hyperparameter pair, and how many trials the current run needed to get within 5% of its eventual best.

⚙ Under the hood

2D top-down heatmap of a validation-loss surface where grid search, random search and Bayesian optimization sample live, with a readout tracking trials, best loss, the winning hyperparameters and convergence speed.

machine learninghyperparameter tuningoptimization algorithmsgrid searchrandom searchbayesian optimization

2D · HTML5 Canvas 2D · 60 FPS target · runs fully client-side, no install