Meta Learning

Meta-learning is a machine learning methodology that focuses on "learning to learn," aiming to optimize the learning algorithm itself so that the model can quickly adapt to new tasks or environments. Its core objective is to enhance the model's generalization ability and learning efficiency, reducing the reliance on large amounts of labeled data. In practical applications, meta-learning can significantly improve performance in areas such as few-shot learning, personalized recommendations, and reinforcement learning.