Classic Neural Networks consisted of three layers: Input – Hidden Layer – Output. “Deep” learning only means that extra hidden layers are added, allowing for more and more parameters of the network to be set.
Neural networks are systems of nodes designed to simulate the neuronal architecture of the brain, but most of them use the algorithm of backpropagation to facilitate learning, which we have no neural evidence in brains.
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