Introduction

Deep learning is not only about building neural networks, training them using an available dataset, and reporting the model accuracy. It involves trying to understand your model and the dataset, as well as moving beyond a basic model by improving it in many aspects. In this chapter, you will learn about two very important groups of techniques for improving machine learning models in general, and deep learning models in particular. These techniques are regularization methods and hyperparameter tuning.

Regarding regularization methods, we'll first answer the questions of why we need them and how they help. We'll then introduce two of the most important and most commonly used regularization techniques. You'll learn in great detail about parameter regularization and its two variations, L1 and L2 norm regularizations. You will then learn about a regularization technique, specifically designed for neural networks, called dropout regulation. You will also practice implementing...