Transfer Learning
An approach in which knowledge learned from one task is transferred to a related task to reduce training cost and data requirements.
Transfer learning refers to the reuse of knowledge learned by a model on one task or dataset for another related problem. This approach becomes especially valuable when data is limited, training time is constrained, or building a model from scratch is too expensive. It is used across a wide range of applications, from image classification to NLP. One of the reasons pre-trained models are so powerful in modern AI is exactly this: the model acquires general knowledge during pretraining and is then adapted to a target task. This creates major gains in speed, performance, and practical usability.
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