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🧠 Inside a Neural Network: Forward Pass

A step-by-step walkthrough of the forward pass, the sequence of weighted sums and activation functions that turns raw input data into a neural network's prediction.

AI & Machine Learning3DAdvanced60 FPS

A layered 3D network of neurons where every weighted connection and every activation is computed live, letting you watch a signal travel from raw input to prediction one layer at a time.

🔬 What It Demonstrates

Each neuron's glow encodes its activation value; each connection's color and thickness encode its weight's sign and magnitude. Running the pass animates the real weighted-sum-then-activation computation layer by layer.

🎮 How to Use

Adjust the three input values, pick an activation function, and scale the weights, then press Run forward pass to watch the signal propagate and see the predicted output class light up.

💡 Did You Know?

Without a nonlinear activation function like ReLU, sigmoid, or tanh, any stack of layers collapses mathematically into one linear function, no matter how many layers you add.

⚙ Under the hood

A step-by-step walkthrough of the forward pass, the sequence of weighted sums and activation functions that turns raw input data into a neural network's prediction.

artificial intelligenceneural networksmachine learningdeep learningforward propagationalgorithmsdata scienceThree.js

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