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A subfield of Machine Learning that leverages similarities and differences across multiple tasks to simultaneously solve them. By sharing representations and knowledge between related tasks, multi-task learning improves the efficiency and performance of models, as it enables them to generalize better across tasks by learning from diverse but related data.