Home▸AI & Machine Learning▸How Hyperparameters Affect Model Performance: Impact Analysis

🧠 How Hyperparameters Affect Model Performance

An interactive 3D loss-landscape where you tune learning rate, momentum, batch size and regularization and watch a gradient-descent optimizer converge, oscillate or diverge in real time.

AI & Machine Learning3DAdvanced60 FPS

A 3D loss landscape where a gradient-descent ball hunts for the minimum while you tune the exact hyperparameters that decide whether real model training converges, oscillates or diverges.

🔬 What It Demonstrates

Learning rate sets step size, momentum carries velocity through shallow bumps, batch size injects gradient noise, and L2 regularization smooths the landscape itself — the same four levers that shape real training curves.

🎮 How to Use

Drag each slider and watch the ball's trajectory respond immediately: overshoot from a high learning rate, jitter from a small batch size, or a smoother bowl from stronger regularization.

💡 Did You Know?

Learning rate is often called the single most important hyperparameter — set it a little too high on a real network and loss can explode to NaN within a handful of steps, exactly as the ball flies off the landscape here.

⚙ Under the hood

An interactive 3D loss-landscape where you tune learning rate, momentum, batch size and regularization and watch a gradient-descent optimizer converge, oscillate or diverge in real time.

machine learningoptimizationneural networksgradient descenthyperparametersThree.js

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