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one of the most powerful ideas in deep learning is that sometimes we can take the knowledge the new network has learned from one toss and apply that knowledge to a separate task so for example maybe kind of a new network learn to recognize objects like cats and then use that knowledge or use part of that knowledge to help you do a better job reading x-ray scans this is called transfer learning lets take a look lets say youve trained in your network on image recognition so you first take a neural network and train it on XY pairs where X is an image and Y is some object in the image as a cat or a dog or bird or something else if you want to take this new network and a gap or we say transfer what is learn to a different tasks such as radiology diagnosis or meaning really reading x-ray scans what you can do is take this loss output layer of the neural network and just delete that and delete also the waste feeding into that loss output layer and create a new set of randomly initialized
