What is transfer learning?

Transfer learning is a machine learning technique where a model is first trained on a large dataset or task, and then the learned knowledge is transferred to a new, related task or dataset.

Transfer learning can be used to speed up training time, improve generalization performance, or adapt a model to a new domain with limited data. Transfer learning has been applied to many domains, such as computer vision, natural language processing, and speech recognition.