Now that you understand the process of building, training, and testing models, you will see that expanding these simple networks to increase performance is relatively straightforward. You will find that nearly all models we build consist, essentially, of the following six steps:
- Import data and create iterable data-loader objects for the training and test sets
- Build and instantiate a model class
- Instantiate a loss class
- Instantiate an optimizer class
- Train the model
- Test the model
Of course, once we complete these steps, we will want to improve our models by adjusting a set of hyper-parameters and repeating the steps. It should be mentioned that although we generally consider hyper-parameters things that are specifically set by a human, the setting of these hyper-parameters can be partially automated, as we shall see in the case of...