How models actually learn, from vanilla gradient descent to Adam
The loss landscape is the shape of L(θ) over parameter space. For neural networks it is high-dimensional and non-convex: it has broad flat stretches, sharply curved ones, saddle points that climb in some directions while dropping in others, and many separate low-loss regions that often turn out to be connected.
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▶ The Loss Landscape