Training & Learning
Transfer Learning
The practice of reusing knowledge from a model trained on one task to accelerate learning on a different but related task.
Transfer learning is the idea that a model trained on one task can be adapted to a different task far more efficiently than training from scratch. The first task provides general features; fine-tuning specializes them for the new task.
In practice, you take a pre-trained model like GPT or BERT, then fine-tune it on your specific task with much less data. This is how most production AI is built today.
Why it works: lower layers learn general features (edges, words, grammar) while upper layers specialize. Transfer preserves the general knowledge.
Transfer learning drastically reduces training cost, data requirements, and time to production. It's the reason a small team can build capable AI products without training from scratch.