Each neuron receives weighted signals from the layer before, sums them, and decides through a non-linear squashing function whether its signal is worth passing forward. Toggle the input features below and watch activations cascade through the network, layer by layer, until a prediction crystallizes at the output.
The loss landscape stretches in every direction. To descend, we compute the partial derivative of loss with respect to each parameter, then step in the opposite direction. Learning rate controls step size; momentum accumulates velocity to power through shallow valleys. Drag the sliders and watch the optimizer's path snap to convergence.
A model too simple cannot capture the data; a model too complex memorizes noise. Between these failures lies the bias–variance frontier. Drag the complexity slider and watch the decision boundary flex from a straight line to a writhing membrane, while training and validation errors diverge.
Paint data points onto the canvas, choose a preset dataset, then train a small neural network in real time. Watch the loss curve descend and the decision boundary adapt to your data. Everything runs in your browser — no servers, no APIs, just gradients.