A must-know for anyone looking for top deep learning interview questions, this is one of the frequently asked deep learning behavioral interview questions. Correct answer option is D. The best method would be to train only the last layer as previous all layers work as feature extractors. They would have extracted key features as part of initial layers in a similar scenario. Since the data similarity is very high, we do not need to retrain the model. All we need to do is to customize and modify the output layers according to our problem statement. We use the pretrained model as a feature extractor. For example: let’s say we decide to use models trained on Imagenet to identify if the new set of images have cats or dogs. Here the images we need to identify would be similar to imagenet, however we just need two categories as our output – cats or dogs. In this case all we do is just modify the dense layers and the final softmax layer to output 2 categories instead of a 1000. Additionally training time takes longer in these type of neural nets. Hence it would save significant amount of time. Re-training last layer will take care of the new dataset at hand with a similar feature being created already and executed leveraging that. There are potentially four scenarios and they can be explained in below diagrammatical fashion.