Gradient descent explained at 3 levels: from blindfolded hill-walking to non-convex dynamics
cneuralnetwork · x · 2026-11-06
A tiered explainer of gradient descent:
- For a 10-year-old: blindfolded on a hill — feel which way goes downhill, take a small step, repeat.
- CS student: update parameters opposite to the loss gradient to minimize the objective.
- Researcher: optimization dynamics depend on the loss landscape, curvature, stochastic gradient noise, and learning-rate schedule; in non-convex settings global convergence isn't guaranteed.
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