Abstract
This chapter focuses on neural networks and how we can build them to perform machine learning, by closely mimicking the human brain. We will start by determining what neural networks are and how similarly they are structured to the neural network in humans. Then, we will deep dive into the architecture of neural networks, exploring the different layers within. We will explain how a simple neural network is built and delve into the concepts of forward and backward propagation. Later, we will build a simple neural network, using TensorFlow and Keras. In the final sections of this chapter, we will discuss deep neural networks, how they differ from simple neural networks, and how to implement deep neural networks with TensorFlow and Keras, again with performance comparisons to simple neural networks.
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