How does an artificial neural network function?
In: Technology
To build a neural network, you’d need some neurons, right?
Well, the neurons we use are artificial neurons. They are mathematical machines that add up everything that comes in the back, and send it out to everything connected to the front.
But when sending things out the front, they ‘weight’ each connection – in other words, they multiply it by some value. This allows each neuron to be different.
If you connect them together, you get a neural network. If you have more than one ‘layer’ of neurons, you get a deep neural network, which can do more complicated things. If you have a loop in your network, you have a recurrent neural network, which can ‘remember’ things.
To actually get it to do what you want, you take a network, but don’t put in any of the weights. You then use an error function, that takes the set of weights for the network, tries them, and produces an ‘error value’ – basically scoring the network on how it performs.
You can then use calculus to find the set of weights that produces the lowest error value. It turns out, if you choose the right shaped network for the right job, you get a pretty good neural network.
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