What is meta learning?

Meta learning, often referred to as 'learning to learn,' is a subfield of machine learning where algorithms are designed to improve their learning process based on previous experiences. Instead of simply learning a specific task, meta learning models aim to generalize from past tasks to solve new, unseen tasks more efficiently. This approach is particularly useful in situations with limited data, as the model leverages knowledge gained from previous tasks to quickly adapt. Meta learning is widely applied in areas like few-shot learning, optimization, and reinforcement learning.