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🧠 Neural Network Architecture Hyperparameters

Learn about neural network architecture hyperparameters. Understanding layer sizes, activation functions, and network design parameters.

AI & Machine Learning3DAdvanced60 FPS

A live 3D feed-forward network where changing depth, width, activation function and dropout rate visibly reshapes the model — every neuron and connection is drawn to scale with the current settings.

🔬 What It Demonstrates

Architecture hyperparameters — layer count, layer width, activation choice and dropout — define a network's capacity and parameter count before any training happens. The simulation recomputes and redraws the full graph every time a control changes.

🎮 How to Use

Adjust hidden layers, neurons per layer, dropout rate and activation function, then watch the parameter count and active-neuron count update. Toggle the animated forward pass to see a signal propagate layer by layer, skipping neurons dropped out.

💡 Did You Know?

Doubling a hidden layer's width roughly doubles the weights connected to it on both sides — parameter count grows much faster with width than with depth in a fixed-width network.

⚙ Under the hood

Learn about neural network architecture hyperparameters. Understanding layer sizes, activation functions, and network design parameters.

neural networksdeep learninghyperparametersmachine learningartificial intelligencelayersThree.js

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