In this article you’ll learn about Neural Networks. What is a neural network? The human brain can be seen as a neural network —an interconnected web of neurons .
In Machine Learning, there exist an algorithm known as an Aritifical Neural Network. They mimic biological neural networks. A network always starts with a single unit: the perceptron.
Related Course: Deep Learning A-Z: Hands-On Artificial Neural Networks
A single perceptron is the basis of a neural network.
A perceptron has:
- one or more inputs
- a bias
- an activation function
- a single output
A perceptron receives inputs. It then multiplies them by some weight. Then they are send into an activation function to produce an output. We also add a bias value, it allows you to shift the activation function.
A neural network is created by adding layers of perceptrons together: the multi-layer perceptron (MLP) algorithm.
We’ll have several layers:
|the input layer||takes your data|
|the hidden layer||any layers between the input and output are called hidden layers|
|output layers||the output results|
Graphically that looks like this (source: Wikipedia):