Introduction

A neural network is a supervised learning algorithm that is loosely inspired by the way the brain functions. Similar to the way neurons are connected to each other in the brain, a neural network takes input, passes it through a function, certain subsequent neurons get excited, and consequently the output is produced.

In this chapter, you will learn the following:

• Architecture of a neural network
• Applications of a neural network
• Setting up a feedforward neural network
• How forward-propagation works
• Calculating loss values
• How gradient descent works in back-propagation
• The concepts of epochs and batch size
• Various loss functions
• Various activation functions
• Building a neural network from scratch
• Building a neural network in Keras