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!

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!

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

KL Divergence

Linear Digressions is a podcast about machine learning and data science.  Machine learning is being used to solve a ton of interesting problems, and to accomplish goals that were out of reach even a few short years ago.

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Linear Digressions is a podcast about machine learning and data science. Machine learning is being used to solve a ton of interesting problems, and to accomplish goals that were out of reach even a few short years ago.

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KL Divergence

KL Divergence

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