Now, it's time to get into the really fun stuff (and what you picked up this book for)—deep neural networks. The depth comes from the number of layers in the neural network and for an FNN to be considered deep, it must have more than 10 hidden layers. A number of today's state-of-the-art FNNs have well over 40 layers. Let's now explore some of the properties of deep FNNs and get an understanding of why they are so powerful.
If you recall, earlier on we came across the universal approximation theorem, which stated that an MLP with a single hidden layer could approximate any function. But if that is the case, why do we need deep neural networks? Simply put, the capacity of a neural network increases with each hidden layer (and the brain has a deep structure). What this means is that deeper networks have far greater expressiveness than shallower...
