N-shot learning

N-shot learning is a machine learning paradigm where models learn to perform new tasks using only n examples per class, where n represents a small, finite number. This approach encompasses zero-shot learning (no examples), one-shot learning (single example), few-shot learning (typically 2-10 examples), and many-shot learning (hundreds of examples). N-shot learning leverages meta-learning techniques, where models learn how to learn efficiently from limited data by training on diverse task distributions. Core methods include model-agnostic meta-learning (MAML), prototypical networks, and in-context learning with large language models. For AI agents, n-shot learning enables rapid adaptation to new domains, personalization without extensive retraining, and deployment in data-scarce environments. This capability is crucial for autonomous systems that must quickly acquire new skills, handle novel scenarios, and operate effectively when collecting large training datasets is impractical or expensive.