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🎯 Hyperparameter Optimization Algorithms

Learn about hyperparameter optimization algorithms. Explore grid search, random search, Bayesian optimization and other algorithms for tuning machine learning models.

AI & Machine Learning3DAdvanced60 FPS

A 3D validation-loss landscape over two hyperparameters — learning rate and regularization strength — that grid search, random search and Bayesian optimization each explore in real time so you can watch how their sampling patterns differ.

🔬 What It Demonstrates

Grid search sweeps a fixed lattice, random search scatters trials uniformly, and Bayesian optimization uses past trials to pick each next point, trading exploration of uncertain regions against exploiting the current best basin.

🎮 How to Use

Pick a search algorithm and evaluation budget, then watch trial markers land on the loss surface. Adjust ruggedness to add local minima, and tune exploration vs exploitation to see Bayesian search behave more greedily or more broadly.

💡 Did You Know?

Bergstra & Bengio (2012) showed random search often beats grid search at equal budget, because most hyperparameter surfaces are dominated by only a few truly important dimensions.

⚙ Under the hood

Learn about hyperparameter optimization algorithms. Explore grid search, random search, Bayesian optimization and other algorithms for tuning machine learning models.

machine learninghyperparameter tuningoptimization algorithmsgrid searchrandom searchbayesian optimizationThree.js

3D · Three.js / WebGL renderer · 60 FPS target · runs fully client-side, no install