Fig. 3.

DNNs have several layers of abstraction that tend to gradually convert raw data into more abstract concepts. For example, raw pixels at the input layer are first processed to detect edges (first hidden layer), then corners and textures (second hidden layer), then object parts (third hidden layer), and so on if there are more layers, until a final prediction is made by the output layer. Note that which types of features are learned at each layer are not human-specified, but emerge automatically as the network learns how to solve a given task.