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Transfer learning has become quite popular in the field of image classification and Natural Language Processing. Here we take a pre-trained model and then we try to retrain it for the new problem. So if you remember from our data augmentation tutorial, we had flowers dataset where we are trying to classify five type of flowers. So in this video we will use a Mobilenet pre-trained model from Google's Tensorflow hub and we will use that pre-trained model to classify our flowers dataset and you will see that previously it used to take many epochs to train the complete model and achieve high accuracy. In this case using a pre-trained model it takes only like two or five iteration or epochs to get a superb accuracy. So using transfer learning saves lot of computation power because many times these pre-trained models that you can get from places like Tensorflow hub they are trained on millions of images. If you try to train that model on your computer it might take days or even months. But...
