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🔍 Optimization Tools and Frameworks for ML

Watch Grid Search, Random Search, Bayesian (TPE) and Ray Tune-style ASHA search a 3D hyperparameter loss surface for the lowest valley.

AI & Machine Learning3DAdvanced60 FPS

A 3D hyperparameter loss landscape where four real tuning strategies — Grid Search, Random Search, Optuna-style Bayesian TPE and Ray Tune's ASHA early-stopping — race to find the lowest valley.

🔬 What It Demonstrates

The terrain's height is validation loss over two hyperparameters. Each strategy samples that surface differently: exhaustive grids, uniform randomness, probability-guided Bayesian sampling, or parallel trials pruned early if they underperform.

🎮 How to Use

Pick an algorithm, set trial speed, ruggedness and trial budget, then watch markers land on the surface. The glowing beacon marks the best loss found so far; ASHA trials that get pruned fade out mid-search.

💡 Did You Know?

Optuna's default TPE sampler and Ray Tune's ASHA scheduler are both designed to spend compute where it matters most — which is why modern AutoML pipelines rarely use plain grid search once the search space grows past a few dimensions.

⚙ Under the hood

Watch Grid Search, Random Search, Bayesian (TPE) and Ray Tune-style ASHA search a 3D hyperparameter loss surface for the lowest valley.

machine learninghyperparameter tuningsearch algorithmsbayesian optimizationray tunegrid searchThree.js

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