Our example of artificial neural network is very simple and only contains one hidden layer. Can we add more layers? Of course we can! The next step in complexity could be something similar to the following diagram:
We added a new hidden layer with two neurons, but we could add more layers and more neurons per layer. The architecture of a network depends on the specific use we give it. Multilayer artificial neural networks are often known as deep neural networks.
The output of a deep network is calculated in analogy with the single layer one, considering all inputs to each neuron, the activation function, and the addition of all the inputs to the output neuron. Looking at the preceding diagram, it is clear that each layer in the network is affected by the previous one. It is usually the case that, in order to solve complex problems, each layer learns a...
