EPISODE · Aug 7, 2017 · 25 MIN
KL Divergence
from Linear Digressions · host Katie Malone
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!
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