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🧠 Neural Network Hyperparameter Optimization Implementation

A 3D loss-landscape simulation showing how learning rate, momentum and optimizer choice change how a neural network's training run descends toward (or misses) a minimum.

AI & Machine Learning3DAdvanced60 FPS

A glowing marker trains in real time across a 3D loss landscape carved from overlapping basins — watch how learning rate, momentum and optimizer choice decide whether it settles into the deep global minimum or gets stuck in a shallow local one.

🔬 What It Demonstrates

The marker's height is the current loss; its purple trail is the optimizer's trajectory. Vanilla SGD, momentum SGD and a simplified Adam step are all computed live from the same underlying gradient each epoch.

🎮 How to Use

Pick an optimizer, then tune learning rate, momentum and batch-size noise. Watch epoch, loss, gradient norm and best loss update live, and restart training from a fresh random point to compare settings.

💡 Did You Know?

Adam adapts its effective step size per-parameter using running estimates of the gradient's mean and variance — which is why it often needs a much larger nominal learning rate than plain SGD to move at a comparable pace.

⚙ Under the hood

A 3D loss-landscape simulation showing how learning rate, momentum and optimizer choice change how a neural network's training run descends toward (or misses) a minimum.

нейронні мережігіперпараметриоптимізаціямашинне навчанняшвидкість навчаннямоментумThree.js

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