Transfer learning is a technique in deep learning that allows us to leverage the knowledge gained from pre-trained models to enhance model performance for new tasks. The idea behind transfer learning is to utilize the learned representations and features from one task or dataset and apply it to another related task or dataset.

For instance, if we have a pre-trained model that has been trained on a large dataset for image recognition, we can use the knowledge gained from this model to solve another image classification problem, even if the new dataset is small.

By using transfer learning, we can benefit from the capacity, generalization, and features learned by the pre-trained models, saving significant time and computation resources.

Imagine the possibilities of building powerful models without starting from scratch every time!