Learning systems / fit versus represent
Curve Fitter
A model does not jump to its answer. Scrub through the real updates to see how a curve settles, and why an activation lets the same tiny network bend a boundary.
Watch two hypotheses learn
Both models see the white training dots. Outlined dots stay hidden from fitting and test whether the curve learned something useful.
Fit progress
epoch 0 of 1,000
Training loss
Each line is a real loss history. The marker follows the scrubber.
Change the hypothesis
Degree adds coefficients. The epoch stays put when you change it, so you can compare how much each hypothesis learned with the same budget.
linear now
y = 0.00 + 0.00x
polynomial now
3 changing coefficients
gradient descent, not a jump to the answer
Linear / held out
0.2043
Degree 2 / held out
0.2043
Every frame is computed in this browser from deterministic data and real batch-gradient updates. The displayed accuracy is a geometry illustration, not a production generalization claim.