What is transfer learning?

Transfer learning is the technique of taking a model pre-trained on a large dataset (like ImageNet for images or Wikipedia for text) and fine-tuning it on a smaller, task-specific dataset. The pre-trained model has already learned general features (edges, textures, grammar) that are reusable. This dramatically reduces training time and data requirements. In computer vision, models like ResNet and VGG are commonly fine-tuned. In NLP, BERT and GPT are pre-trained and fine-tuned on downstream tasks.