Solving Classification Problems
In the previous chapters, we learned how to set up and run MXNet, how to work with Gluon and DataLoader, and how to visualize datasets for regression, classification, image, and text problems. We also discussed the different learning methodologies. In this chapter, we are going to focus on supervised learning with classification problems. We will learn why these problems are suitable for deep learning models with an overview of the equations that define these problems. We will learn how to create suitable models for them and how to train them, emphasizing the choice of hyperparameters. We will end each section by evaluating the models according to our data, as expected in supervised learning, and we will look at the different evaluation criteria for classification problems.
Specifically, we will cover the following recipes:
- Understanding math for classification models
- Defining loss functions and evaluation metrics for classification ...
