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Transfer learning is the idea that, after a machine learning system (or a non-machine learning system, for that matter, like a human) has learned to solve some problem, it should be able to transfer this knowledge to solving similar problems. Humans are pretty good at this, at least compared to current ML systems, which tend to suck. Why do we expect transfer learning to work? It seems that, in…
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