Transfer learning is a technique in deep learning where a pre-trained neural network, which has been trained on a large and general dataset, is used as a starting point for solving a new task or dataset. By leveraging the learned representations from the pre-trained model, transfer learning enables training on smaller and more specific datasets, saving time and computational resources.