KL Divergence
Kullback Leibler divergence, or KL divergence, is…
August 7, 201725m 38s
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Show Notes
Kullback Leibler divergence, or KL divergence, is a measure of information loss when you try to approximate one distribution with another distribution. It comes to us originally from information theory, but today underpins other, more machine-learning-focused algorithms like t-SNE. And boy oh boy can it be tough to explain. But we're trying our hardest in this episode!
Topics
datasciencemachinelearninglineardigressions