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KL Divergence
Kullback Leibler divergence, or KL divergence, is a measure of information loss when you try to approximate one distribution with another distribution. \xa0It comes to us originally from information theory, but today underpins other, more machine-learning-focused algorithms like t-SNE. \xa0And boy oh boy can it be tough to explain. \xa0But we're trying our hardest in this episode!