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
