Meta-Learning is a subfield of machine learning that involves training models to learn how to learn from data. This approach is useful for tasks where the model needs to adapt quickly to new situations or environments. For example, a meta-learner trained on one task can be fine-tuned to perform well on another related task without requiring extensive retraining. The goal of meta-learning is to enable models to generalize across different tasks and domains. --- Why it matters: Meta-Learning matters because it allows models to adapt quickly to new situations, which is crucial in applications such as few-shot learning and transfer learning. Source: https://lilianweng.github.io/posts/2018-11-30-meta-learning/