An autoencoder can be called deep so long as it has more than one pair of layers (an encoding one and a decoding one). Stacking layers on top of each other in an autoencoder is a good strategy to improve its power for feature learning in finding unique latent spaces that can be highly discriminatory in classification or regression applications. However, in Chapter 7, Autoencoders, we covered how to stack layers onto an autoencoder, and we will do that again, but this time we will use a couple of new types of layers that are beyond the dense layers we have been using. These are the batch normalization and dropout layers.
There are no neurons in these layers; however, they act as mechanisms that have very specific purposes during the learning process that can lead to more successful outcomes by means of preventing overfitting or reducing numerical instabilities. Let's talk about each of these and then we will continue to experiment with both of these on a...
