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🎛️ Hyperparameter Constraints and Relationships

Understand hyperparameter constraints and relationships. Learn how to handle dependencies and constraints in hyperparameter optimization.

AI & Machine Learning3DAdvanced60 FPS

A 3D search landscape over learning rate and batch size, cut by a curved coupling constraint and an optional regularization-budget band, showing exactly how joint hyperparameter relationships shrink the feasible region an optimizer can search.

🔬 What It Demonstrates

The violet corridor traces the ideal learning-rate/batch-size coupling (a generalized linear scaling rule). Configurations far from it — or that miss a second regularization-budget constraint — are shaded red as infeasible, mirroring how conditional search spaces work in tools like Optuna.

🎮 How to Use

Adjust the coupling exponent and tolerance to bend and widen the feasible corridor, toggle the regularization constraint to see the feasible region shrink further, and resample random configurations to watch which ones pass both constraints.

💡 Did You Know?

Ignoring hyperparameter dependencies wastes enormous compute: a random search that doesn't know learning rate should scale with batch size will spend most of its trials on combinations that were never going to converge.

⚙ Under the hood

Understand hyperparameter constraints and relationships. Learn how to handle dependencies and constraints in hyperparameter optimization.

machine learninghyperparameter optimizationartificial intelligencealgorithmsdata scienceneural networksThree.js

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