0-1 loss just counts mistakes — exactly what accuracy measures:

L₀₋₁(y,ŷ) = 0 if right, 1 if wrong

Problem: it's flat almost everywhere, with no useful slope — gradient descent has nothing to follow. So every algorithm in this course secretly optimizes a smooth stand-in instead.