One picture each: gradient descent and backprop explained without arithmetic
alfcnz · x · 2026-09-24
AI educator alfcnz continues his minimal-visuals ML fundamentals series:
- Gradient descent: with one parameter, the loss surface is a valley and the gradient points downhill, illustrated with a three-step table of decreasing loss.
- Backprop: the error signal ỹ − y flows back from the output to the hidden knobs, explainable with a single picture and no arithmetic.
- Learning signal: the loss L(w, x, y) = C(y, ỹ) scores how wrong the network is (squared error on the board); the gradient with respect to s is ỹ − y regardless of output loss, with σ(s) for hidden units and softargmax(s) at the output.
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