N-shot learning is a type of machine learning problem where a model is required to learn from a very limited number of examples (usually N examples) for each class during training. It is a subfield of few-shot learning, which focuses on training models to recognize new objects or categories based on a small number of samples.
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Tech Insights, Information, and InspirationGPT Few-Shot Learning
GPT few-shot learning refers to the ability of Generative Pre-trained Transformer (GPT) models to learn and generalize from a small number of examples or training instances, also known as “few-shot learning.” In the context of GPT models like GPT-3, few-shot learning demonstrates the model’s capacity to understand and perform tasks with very limited guidance or additional training.
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