Introduction of the perception/neuron

Before we jump straight into neural networks we need to understand the individual components first such as a single neuron.
Artificial neural networks (ANN) actually have a basis in biology so we're going to see how we can attempt to mimic biological neurons of an artificial neuron (otherwise known as the perception) and then once we go through the process of how a simple perception works will go ahead and show you how you can represent that mathematically

Below let's see the biological neuron such as a brain cell

so the biological neuron works as in a simplified way through the following manner, basically you have dendrites that feed into the body of cell you can have many dendrites and what happens is electrical signal gets to pass through the dendrites to the body of the cell and then later on a single output or a single electrical signal is passed on down through an axon to, later on, connects to some other neuron and that's the basic idea we have a kind of the as many inputs of electoral signals to the dendrites goes to the body and in a single actual signal output through the axon so the artificial neuron also has inputs outputs so I go in attempt to mimic the biological neurons.

so this simple model again this just known as perception and in this case, we have two inputs so let's go and see a simple example of how can work so we have two inputs and a single output and we have started indexing at zero so we have the input of zero and input one, after the inputs can have values of features so when you have your data set you're going to have various features and these features going to be anything in from how many rooms house has or the dark images represented by some sort of pixel amount or some sort of darkness number etc.