Transfer learning is a technique in machine learning where knowledge gained from training a model on one task is applied to a different but related task. It involves taking a pre-trained model, usually trained on a large dataset, and reusing its learned features or weights to solve a different problem. By leveraging the pre-trained model's knowledge, transfer learning can significantly reduce the amount of labeled data required for the new task. For example, a model trained on a large dataset of images can be used as a starting point for a different image classification task, where it has to classify new types of images.

Transfer learning is like learning to play the guitar and then using that knowledge to learn to play the piano. With transfer learning, you take what you already know and apply it to a new, related task. For instance, if you have a model that can detect different breeds of dogs, you can use that model as a starting point to build another model that detects different types of animals. By reusing the knowledge gained from the previous task, the model can learn the new task more efficiently and with limited labeled data.