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A subfield of Machine Learning that explores how algorithms can analyze and adapt their own learning process to improve their generalization ability. Often referred to as “learning to learn,” meta-learning focuses on designing models that can learn from previous experiences and apply that knowledge to improve their performance on new tasks.