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Hi guys, I am currently working on a DL problem where I have to classify an image dataset from Kaggle into 5 classes. The first task is to train a NN from scratch that overfits the data and then I have to modify the training process so that the network is trained without overfitting for more than double the number of epochs in the first task, keeping the same architecture, number of training images, optimizer, batch size and learnnig rate I used . I am allowed to use any architecture (resnet, alexnet, moblinet etc) or a custom model.
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