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During this quarantine time, I binge-watched @Stanford #CS330 lectures taught by the brilliant @chelseabfinn. This blog post is a summary of the key takeaways on #Bayesian Meta-Learning that I’ve learned. #AtHomeWithAI

medium.com/cracking-the-d…

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Bayesian meta-learning generates hypotheses about the underlying function, samples from the data distribution, and reasons about model uncertainty. It is suitable for problems in safety-critical domains, exploration strategies for meta-RL, and active learning.

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