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🧮 Regularization Hyperparameters

An interactive 3D loss-landscape and dropout-network simulation showing how L1, L2 and elastic-net regularization strength shrinks and sparsifies model weights, and how dropout thins a neural network.

AI & Machine Learning3DAdvanced60 FPS

A two-part 3D lab: a loss "bowl" shows how L1, L2 and elastic-net penalties shrink and sparsify a model's weights as lambda grows, while a linked neural network shows dropout randomly thinning hidden neurons.

🔬 What It Demonstrates

The purple marker is the closed-form regularized solution for two weights; watch it slide from the grey unregularized optimum toward the origin as lambda increases, and land exactly on an axis under L1 — that's sparsity.

🎮 How to Use

Pick a penalty type, drag Lambda to change regularization strength, and set the Dropout rate to see more or fewer hidden neurons switched off each training step. Resample the dropout mask on demand.

💡 Did You Know?

Because the L1 constraint region has sharp corners on the weight axes, gradient contours are far more likely to first touch it exactly at a corner — which is why Lasso regression tends to zero out entire features.

⚙ Under the hood

machine learningneural networksregularizationoptimizationloss functionsdropoutThree.js

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