What is Transfer Learning?

Transfer Learning

Definition A machine learning approach where a model trained on one task or dataset is adapted for a different but related task, leveraging knowledge already encoded in its weights. Transfer learning dramatically reduces the data and compute needed for specialized applications.

In Depth

The modern LLM paradigm — pre-train on broad internet text, then fine-tune for specific tasks — is the canonical example of transfer learning at scale. In computer vision, ImageNet-pre-trained models are routinely fine-tuned for medical imaging, satellite imagery analysis, and industrial inspection with far fewer labeled examples than training from scratch would require.

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