Transfer Learning

Transfer learning leverages knowledge gained from solving one problem and applies it to a different, related problem. Instead of training a model from scratch — which requires massive data and compute — transfer learning starts with a pre-trained model and adapts it to new tasks with much less data.

The approach is fundamental to modern AI. ImageNet-pretrained CNNs are fine-tuned for medical imaging. Language models pre-trained on web text are adapted for customer support, code generation, or legal analysis. This dramatically reduces the data, compute, and expertise needed to build effective AI systems.

Transfer learning makes AI accessible to organizations without the resources to train models from scratch. Understanding when and how to apply transfer learning — choosing the right pre-trained model, deciding how many layers to freeze, managing domain shift — is a core competency for ML practitioners.