Deep Learning with JavaScript

Chapter 3. Adding nonlinearity: Beyond weighted sums

This chapter covers

• What nonlinearity is and how nonlinearity in hidden layers of a neural network enhances the network’s capacity and leads to better prediction accuracies
• What hyperparameters are and methods for tuning them
• Binary classification through nonlinearity at the output layer, introduced with the phishing-website-detection example
• Multiclass classification and how it differs from binary classification, introduced with the iris-flower example

In this chapter, you’ll build on the groundwork laid in chapter 2 to allow your neural networks to learn more complicated mappings, from features to labels. The primary enhancement we will introduce is nonlinearity—a mapping between input ...