Lilian Weng • 2/20/2022

Learning with not Enough Data Part 2: Active Learning

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This technical article delves into active learning, a method for efficiently improving machine learning models with limited labeled data. It defines key concepts, notations, and acquisition functions like uncertainty sampling, margin score, and entropy to intelligently select which unlabeled samples to annotate within a fixed budget, with a focus on deep neural models.

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#Machine Learning #Deep Learning #Supervised Learning
Learning with not Enough Data Part 2: Active Learning

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