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An in-depth exploration of the Law of Unconscious Statistician and the proof that KL divergence is non-negative, with mathematical derivations and explanations.
An in-depth exploration of F-divergences, their properties, and how they are used in generative adversarial networks (GANs) for estimating and sampling from unknown probability distributions.
An in-depth exploration of the key concepts behind generative and discriminative machine learning models, including probability theory, divergence minimization, and information theory.
An in-depth exploration of random variables, probability distributions, and their role in machine learning, covering key concepts like sample spaces, probability measures, and distribution functions.