Home▸AI & Machine Learning▸Gradient-Based Methods for Hyperparameter Optimization

🧭 Gradient-Based Methods for Hyperparameter Optimization Lab

A 3D validation-loss surface over two hyperparameters where a marker rolls downhill by following its own gradient, with learning rate, momentum and gradient-noise controls you tune live.

AI & Machine Learning3DAdvanced60 FPS

A marker rolls downhill across a 3D validation-loss surface by following its own estimated gradient, letting you watch learning rate, momentum and gradient noise decide whether it settles into the true optimum or gets trapped in a shallow local minimum.

🔬 What It Demonstrates

The colored surface is validation loss over two hyperparameters; the arrow field shows the local downhill direction everywhere. The marker takes real gradient steps — exactly the mechanism behind hypergradient descent — rather than sampling isolated points like grid or random search.

🎮 How to Use

Raise the learning rate for bigger steps, add momentum to coast through flat or bumpy regions, and dial in gradient noise to see it jostle the marker out of local minima. Restart from the corner or generate a new landscape to compare runs.

💡 Did You Know?

Because gradient-based hyperparameter tuning reuses gradient information already computed during training, it can scale to dozens of hyperparameters at roughly the cost of one extra training run — far cheaper than grid search's exponential blow-up.

⚙ Under the hood

A 3D validation-loss surface over two hyperparameters where a marker rolls downhill by following its own gradient, with learning rate, momentum and gradient-noise controls you tune live.

machine learningoptimizationhyperparametersgradient descentneural networksalgorithmsThree.js

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