Chapter 8: Best Practices for Model Training and Performance

In order for a supervised machine learning model to be well trained, it requires large volumes of training data. In this chapter, we are going to look at a few common examples and patterns for handling input data. We will specifically learn how to access training data regardless of its size and train the model with it. After that, we will look at regularization techniques that help to prevent overfitting. Having large volumes of training data is no guarantee of a well-trained model. In order to prevent overfitting, we may need to apply various regularization techniques in our training processes. We will take a look at a number of such techniques, starting with the typical Lasso (L1), Ridge (L2), and elastic net regularizations, before moving on to a modern regularization technique known as adversarial regularization. With these techniques at our disposal, we put ourselves in a good position vis-à-vis reducing overfitting...