Cross-entropy and Kullback-Leibler (KL) divergence are key mathematical concepts in machine learning, used to measure the loss in classification tasks and compare probability distributions. Entropy measures the uncertainty in a random variable, while cross-entropy extends this concept to two distributions, and KL divergence refines it to better compare these distributions. These metrics play a critical role in training and evaluating machine learning models.
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Information content of a single random eventEntropyCross-entropyKL divergenceUses in machine learningRelation to Maximum Likelihood EstimationSort: