A fast-paced dive into how differential geometry models probability and statistics. We treat distributions as points on a curved statistical manifold, with the Fisher information metric guiding distance and learning via natural gradient. Along the way we explore why normal distributions sit in hyperbolic geometry, and how this perspective reshapes inference, optimization, and generalization in ML and beyond.

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