Day 18 of #DataScience28: Transfer Learning

Transfer learning is a machine learning technique that allows models to use knowledge gained from previous tasks to improve performance on new, similar tasks. Transfer learning has become an important tool in the field of machine learning because it can dramatically reduce the amount of time and data needed to train models, and it can lead to better performance on a wide range of tasks. The basic idea behind transfer learning is that a model can learn features that are useful for one task and then reuse those features for another task. For example, a model trained to recognize images of cars might learn to recognize the wheels, headlights, and grille of a car. Those features could then be reused in a model trained to recognize images of trucks, even though the truck images were not part of the original training data.