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🎯 Hyperparameter Tuning Workflows

Watch grid search, random search and Bayesian optimization hunt for the minimum across a 3D validation-loss landscape, and see how sampling strategy shapes tuning efficiency.

AI & Machine Learning3DAdvanced60 FPS

A 3D validation-loss landscape over two hyperparameters, searched live by grid search, random search or a simplified Bayesian optimizer — watch each strategy hunt for the minimum under the same trial budget.

🔬 What It Demonstrates

The colored terrain encodes validation loss across a learning-rate/regularization grid: blue basins are good configurations, red ridges are bad ones. Each marker is one training run's result; the gold marker tracks the best trial found so far.

🎮 How to Use

Pick a search strategy, set the trial budget and landscape ruggedness, then watch trials populate the surface. Toggle the search path to see the order trials were evaluated in, and compare how fast each strategy converges.

💡 Did You Know?

Grid search scales exponentially with the number of tuned hyperparameters, which is why most production ML pipelines default to random search or Bayesian/sequential model-based optimization once past two or three dimensions.

⚙ Under the hood

Watch grid search, random search and Bayesian optimization hunt for the minimum across a 3D validation-loss landscape, and see how sampling strategy shapes tuning efficiency.

machine learninghyperparameter optimizationgrid searchrandom searchbayesian optimizationtuningThree.js

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