Training regression models
In supervised learning, training is the process of optimizing the parameters of a model towards a specific objective. It is typically the most complex and the most time-consuming step in solving a deep learning problem statement.
In this recipe, we will visit the basic concepts involved in training a model. We will apply them to solve the regression model we previously defined in this chapter, combined with the usage of the functions we discussed.
We will predict house prices using the dataset seen in Recipe 1, Toy dataset for regression – load, manage, and visualize house sales dataset from Chapter 2, Working with MXNet and Visualizing Datasets: Gluon and DataLoader.
Getting ready
There are a number of concepts that we should get familiar with to understand this recipe. These concepts define how the training will proceed:
- Loss function: The training process is an iterative optimization process. As the training progresses, the model...
