CS professor: 3D loss landscape visualizations mislead, fueling old local-minima fears
CSProfKGD · x · 2026-10-26
In a discussion on teaching gradient descent, an ML professor argues that starting with the simple convex case makes the most pedagogical sense, and that visualizing increasingly messy loss landscapes in 3D can actually be misleading. He contends this low-dimensional intuition historically fueled skepticism toward gradient-based optimization — the fear of getting trapped in poor local minima — but fails when reasoning about very high-dimensional problems.
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