Now, let's look through the theory and implementation of deep learning algorithms. In this chapter, we will see DBN and SDA (and the related methods). These algorithms were both researched explosively, mainly between 2012 and 2013 when deep learning started to spread out rapidly and set the trend of deep learning on fire. Even though there are two methods, the basic flow is the same and consistent with pre-training and fine-tuning, as explained in the previous section. The difference between these two is which pre-training (that is, unsupervised training) algorithm is applied to them.
Therefore, if there could be difficult points in deep learning, it should be the theory and equation of the unsupervised training. However, you don't have to be afraid. All the theories and implementations will be explained one by one, so please read through the following sections carefully.
