Solving Regression Problems

In the previous chapters, we learned how to set up and run MXNet, work with Gluon and DataLoaders, and visualize datasets for regression, classification, image, and text problems. We also discussed the different learning methodologies (supervised learning, unsupervised learning, and reinforcement learning). In this chapter, we are going to focus on supervised learning, where the expected outputs are known for at least some examples. Depending on the given type of these outputs, supervised learning can be decomposed into regression and classification. Regression outputs are numbers from a continuous distribution (such as predicting the stock price of a public company), whereas classification outputs are defined from a known set (for example, identifying whether an image corresponds to a mouse, a cat, or a dog).

Classification problems can be seen as a subset of regression problems, and therefore, in this chapter, we will start working with the latter ones...