In the previous chapter, you learned about the typical algorithm of neural networks and saw that nonlinear classification problems cannot be solved with perceptrons but can be solved by making multi-layer modeled neural networks. In other words, nonlinear problems can be learned and solved by inserting a hidden layer between the input and output layer. There is nothing else to it; but by increasing the number of neurons in a layer, the neural networks can express more patterns as a whole. If we ignore the time cost or an over-fitting problem, theoretically, neural networks can approximate any function.
So, can we think this way? If we increase the number of hidden layers—accumulate hidden layers over and over—can neural networks solve any complicated problem? It's quite natural to come up with this idea. And, as a matter of course, this idea has already been examined. However, as it turns out, this trial didn't work well. Just accumulating layers didn't make neural networks...
