04
6 minute interactive lab
The forward pass
How does an input become an output?
YOU’LL LEAVE KNOWINGInference is a chain of deterministic transformations.
THE EXPERIMENT
Trace every number from input to prediction.
Move the controls. Watch the internal state respond. The explanation follows the behavior—not the other way around.
Runs the real small-scale equationThe displayed values are calculated from the formula shown in this lesson.
INFERENCE TRACEFollow the arithmetic
The parameters stay fixed. Changing an input recomputes every downstream activation in order.
inputs
→a = 0.70b = 0.30weighted sums
→z₁ = 0.540z₂ = 0.080ReLU
→h₁ = 0.540h₂ = 0.080sigmoid output66.6%
z = 0.688Inference is a chain of deterministic transformations.
A forward pass applies the network’s current weights, biases, and activations in order. Nothing learns during this step: the same input and parameters always produce the same output.
IN DEVELOPER TERMS
prediction = layer₂(activation(layer₁(input)))GO DEEPER
Check the model against the source.
Connectionism optimizes for intuition, then points you to the rigorous treatment.
Did the model learn—or memorize?