02
6 minute interactive lab
Why neural networks need hidden layers
Why can’t one neuron solve every pattern?
YOU’LL LEAVE KNOWINGDepth composes simple features into useful representations.
THE EXPERIMENT
Turn a straight line into a useful shape.
Move the controls. Watch the internal state respond. The explanation follows the behavior—not the other way around.
Conceptual simulationThe behavior is deliberately simplified and labeled; it teaches the relationship, not a trained production model.
ARCHITECTUREAdd hidden layers
One linear boundary cannot separate the curved groups.
test accuracy52%
learned decision boundary
Depth composes simple features into useful representations.
A layer does not magically add intelligence. It gives the network another chance to recombine simple signals. Successive layers can build edges into curves, and curves into concepts.
IN DEVELOPER TERMS
output = layer₃(layer₂(layer₁(input)))GO DEEPER
Check the model against the source.
Connectionism optimizes for intuition, then points you to the rigorous treatment.
Why introduce a bend into the network?