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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
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