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Transfer Learning is a powerful and an increasingly common technique for training deep learning models quickly with a minimal training set.

To understand the basic notion of Transfer Learning, consider a model X is successfully trained to perform task A with model M1. If the size of the dataset for task B is too small preventing the model Y from training efficiently or causing overfitting of the data, we can use a part of model M1 as the base to build model Y to perform task B.

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