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Semi-supervised and active learning: Machine Learning course | Zoonk

28. Semi-supervised and active learning

Train models when labeled examples are limited by combining labeled and unlabeled data. Use pseudo-labeling, consistency objectives, active learning, and human review to spend labeling effort where it matters most.

Machine Learning

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