Translations:Transfer Learning/1/en: Difference between revisions

Revision as of 22:05, 27 April 2026

Information about message (contribute)

This message has no documentation. If you know where or how this message is used, you can help other translators by adding documentation to this message.

Message definition (Transfer Learning)

'''Transfer learning''' is a machine learning technique in which a model trained on one task is reused as the starting point for a model on a different but related task. By leveraging knowledge acquired from large-scale {{Term|pre-training|pretraining}}, transfer learning dramatically reduces the amount of labelled data, compute, and training time required for downstream applications.

Transfer learning is a machine learning technique in which a model trained on one task is reused as the starting point for a model on a different but related task. By leveraging knowledge acquired from large-scale pretraining, transfer learning dramatically reduces the amount of labelled data, compute, and training time required for downstream applications.

Revision as of 19:42, 27 April 2026 (view source) FuzzyBot (talk \| contribs) (Importing a new version from external source) ← Older edit		Revision as of 22:05, 27 April 2026 (view source) FuzzyBot (talk \| contribs) (Importing a new version from external source) Tag: Manual revert Newer edit →
Line 1:		Line 1:
	'''Transfer learning''' is a machine learning technique in which a model trained on one task is reused as the starting point for a model on a different but related task. By leveraging knowledge acquired from large-scale ~~{{Term\|pre-training\|~~pretraining}}, transfer learning dramatically reduces the amount of labelled data, compute, and training time required for downstream applications.		'''Transfer learning''' is a machine learning technique in which a model trained on one task is reused as the starting point for a model on a different but related task. By leveraging knowledge acquired from large-scale pretraining, transfer learning dramatically reduces the amount of labelled data, compute, and training time required for downstream applications.