This section is one of the most important, so you need to make sure you understand it quite well in order to grasp the full concept of our application. We will be introducing the network graph that will be used for training and prediction.
But first, let's define the hyperparameters of the model. These are predefined constants that play a significant role in determining how well the model performs. As you will learn in the next chapter, our main task is to tweak the hyperparameters' values until we're satisfied with the model's prediction. In this case, an initial set of hyperparameters is selected. Of course, for better performance, one needs to do some optimization on them. This chapter won't focus on this part but I highly recommend doing it using techniques from the last chapter of this book (Chapter 6, Improving...
