Actually, the term deep learning had already been coined back in the 80s, when neural networks first began stacking two or three layers of neurons. As opposed to the early, simpler solutions, deep learning regroups deeper neural networks, that is, networks with multiple hidden layers—additional layers set between their input and output layers. Each layer processes its inputs and passes the results to the next layer, all trained to extract increasingly abstract information. For instance, the first layer of a neural network would learn to react to basic features in the images, such as edges, lines, or color gradients; the next layer would learn to use these cues to extract more advanced features; and so on until the last layer, which infers the desired output (such as predicted class or detection results).
However, deep learning only really started being used from 2006, when Geoff Hinton and his colleagues proposed an effective solution...
