For classification, I have often heard about deep learning / deep neural networks as a form of representation learning. I am confused as to what "representation learning" means in this context. Which of the following is the case?
1) The output layer of the network gives a feature vector, with one output node per vector element. This feature vector is then passed into a classifier. As such, the output layer is learning a better "representation" of the data than the original input layer, which means it is more suitable when put through a classifier.
2) The output layer of the network gives a classification score for each class, with one output node per class. The score for each class is then the value of the respective output node.
Number 2 is the way I have seen artificial neural networks being used in the past, but number 1 is learning "representations". Which is the one used in deep learning?