Transfer Learning
Transfer learning is a machine learning technique where a model trained on one task or dataset is reused as the starting point for a different but related task. Instead of training from scratch, you take a model that already understands a broad domain and adapt it to your specific use case with much less data and compute. Transfer learning is the foundation of modern LLMs — they are pre-trained on vast text corpora, then adapted for specific applications through fine-tuning.
Example
A model pre-trained on millions of English web pages already understands grammar, context, and general knowledge. A hospital fine-tunes this model on a few thousand clinical notes for medical summarization. The model transfers its language understanding to the medical domain, achieving strong performance that would be impossible if trained from scratch on such a small dataset.
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