What is a Neural Network, Inspired by the Human Brain?

Neural networks are machine learning algorithms inspired by the structure and function of the human brain.

Just as the human brain processes information through countless connections between nerve cells (neurons), neural networks also process data through a complex structure of artificial neurons implemented in code.

A Perceptron is the most basic form of an artificial neural network. It refers to the structure of an artificial neuron that receives data as input and makes a simple decision.

Similar to how the human brain processes information through connections between neurons, a perceptron processes data through a simple structure of connected artificial neurons.

A perceptron comprises the following components.

The data received from an external source.

It can be numerical information like temperature, size, weight, or unstructured data such as images or text.

A numerical value indicating the importance of each input. The larger the weight, the greater the impact of that input on the output.

An additional value added to the product of inputs and weights that serves as a threshold to determine how easily the neuron activates.

The activation function determines the output by assessing whether the sum of inputs and weights exceeds a specific threshold (Threshold).

The Perceptron typically uses a simple activation function known as the step function.

In a step function, if the sum of the inputs times the weights exceeds a certain threshold, the output is 1; otherwise, it is 0.

In the next lesson, we will delve deeper into how perceptrons operate and the structure of neural networks.