Theoretical Foundations of Transfer Learning

Transfer learning is a method where a model trained on one task gains knowledge that helps solve different but related tasks more efficiently. Think of it like learning to ride a bike; the skills transfer when you try rollerblading since both involve balance and coordination. In AI, instead of starting from scratch, models leverage pre-existing information to adapt quickly to new problems with less data and training. This approach saves time and resources, making AI systems more flexible and effective across various applications.