06
5 minute interactive lab
Loss functions
How does a model know how wrong it is?
YOU’LL LEAVE KNOWINGLoss converts a goal into a number learning can optimize.
THE EXPERIMENT
Turn a wrong prediction into useful feedback.
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.
OBJECTIVEMeasure how wrong
The prediction and target are identical across all three rows. Only the definition of “wrong” changes.
Squared error0.078
Squares the miss, emphasizing large errors.
Absolute error0.280
Treats every unit of error equally.
Cross-entropy0.329
Penalizes confident wrong classifications.
Loss converts a goal into a number learning can optimize.
A loss function measures a prediction against the target. Different losses encode different priorities: squared error punishes large misses, while cross-entropy strongly penalizes confident wrong classifications.
IN DEVELOPER TERMS
loss = measure(prediction, target)GO DEEPER
Check the model against the source.
Connectionism optimizes for intuition, then points you to the rigorous treatment.
Which way should every weight move?