Transfer learning has emerged as a powerful technique for building effective models with limited training data. This article examines this increasingly popular approach.

We'll look at how knowledge gained from training on one task can be applied to another, similar task, significantly reducing the amount of data and computation required.

This capability is making machine learning more accessible and applicable to domains where labeled data is scarce.