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📉 Mathematical Foundations of Hyperparameter Optimization

Explore a 3D loss landscape and watch grid search, random search and gradient descent hunt for its global minimum in real time.

AI & Machine Learning3DAdvanced60 FPS

A synthetic validation-loss surface rendered in 3D, searched live by grid search, random search and gradient descent so you can see how each optimization strategy explores hyperparameter space.

🔬 What It Demonstrates

Hyperparameter tuning is minimizing an objective function L(θ) over a search space. The purple-to-pink surface height encodes loss; gradient descent follows −∇L(θ) downhill, while random and grid search sample the space directly.

🎮 How to Use

Pick an optimizer, then tune the step size, landscape ruggedness and batch size. Watch the current-best marker chase the true minimum and compare how quickly — and how reliably — each strategy finds it.

💡 Did You Know?

Bergstra & Bengio's 2012 study found random search often beats grid search at equal budget, because most objective functions are dominated by only a few truly important hyperparameters.

⚙ Under the hood

Explore a 3D loss landscape and watch grid search, random search and gradient descent hunt for its global minimum in real time.

machine learningoptimizationhyperparameterssearch algorithmsgradient descentloss functionThree.js

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