Introduction
In the previous chapter, you learned about the mathematics of neural networks, including linear transformations with scalars, vectors, matrices, and tensors. Then, you implemented your first neural network using Keras by building a logistic regression model to classify users of a website into those who will purchase from the website and those who will not.
In this chapter, you will extend your knowledge of building neural networks using Keras. This chapter covers the basics of deep learning and will provide you with the necessary foundations so that you can build highly complex neural network architectures. We will start by extending the logistic regression model to a simple single-layer neural network and then proceed to more complicated neural networks with multiple hidden layers.
In this process, you will learn about the underlying basic concepts of neural networks, including forward propagation for making predictions, computing loss, backpropagation for computing...
