In this chapter, we covered a variety of methods that are used to regularize the parameters of a neural network. These methods are very important when it comes to training our models because they help ensure that they can generalize to unseen data by preventing overfitting, thereby performing well on the tasks we want to use them for. In the following chapters, we will learn about different types of neural networks and how each one is best suited for certain types of problems. Each neural network has a form of regularization that it can use to help improve performance.
In the next chapter, we will learn about convolutional neural networks, which are used for computer vision.
