Transfer learning is a machine learning technique where knowledge gained from a model trained on one task is reused to improve performance on a new, but related, task. Instead of training a model from scratch with a large dataset, you can take a pre-trained model and use it as a starting point. This approach is similar to how humans learn, where we apply knowledge from one skill to help us learn a new, related one (e.g., knowing how to play the guitar makes it easier to learn the ukulele).

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