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🎛️ Optimizer Hyperparameters Lab

Watch SGD, Momentum, RMSProp and Adam race down a live 3D loss surface as you tune learning rate and momentum, revealing how each hyperparameter shapes convergence.

AI & Machine Learning3DAdvanced60 FPS

Four gradient-descent optimizers — SGD, Momentum, RMSProp and Adam — race across the same live 3D loss surface, letting you watch learning rate and momentum reshape each one's path to the minimum.

🔬 What It Demonstrates

Each ball takes gradient steps scaled and shaped by its optimizer's own rule. On a steep ravine, plain SGD zig-zags or overshoots while adaptive methods like Adam and RMSProp settle in smoothly.

🎮 How to Use

Raise the learning rate to see instability appear, tune momentum to see smoother trajectories, switch landscapes to test escaping local dips, and isolate a single optimizer to inspect its trail.

💡 Did You Know?

Adam is the default optimizer in most deep learning frameworks precisely because it adapts its effective learning rate per-parameter, making it far more forgiving of a poorly tuned learning rate than plain SGD.

⚙ Under the hood

Watch SGD, Momentum, RMSProp and Adam race down a live 3D loss surface as you tune learning rate and momentum, revealing how each hyperparameter shapes convergence.

machine learningoptimizationhyperparameterssgdmomentumloss functionThree.js

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