Transfer Learning

Using AI

Reusing what a model learned on one task as the starting point for another - dramatically cutting the data and cost needed.

Transfer learning starts from a model that already knows a lot - one trained on millions of general images or vast text - and adapts it to a new task with a comparatively tiny dataset. The general knowledge transfers, only the specifics need teaching.This is why a clinic can build a usable scan classifier from hundreds of examples instead of millions, and it is the principle behind fine-tuning language models. For smaller markets and languages, including the Balkans, it is often the only affordable route to custom AI.