How does an artificial neural network function?
In: Technology
Data is first converted into numerical inputs. For an image, this could be the colour of each pixel, for text it could be the words themselves. For example, the sentence “over here over there” might be converted into 0102 (1 number per unique word).
Now you have a bunch of input numbers, so you can perform mathematical operations on them. For example, you could multiply 2 inputs together, or add them, or add only half of the first the 2x the other. We call this bunch of mathematical operations a “layer”.
These processed outputs are fed into another layer, which does the exact same thing. It happens again and again until you get an output (say the final output is 1.7). Sometimes there can be multiple outputs, but I’m going to stick with classification as an example.
We compare these outputs to some validated test data, for example if we fed Romeo and Juliet into the same bunch of layers and got an output of 1.7, we would call all outputs near 1.7 “Shakespeare text” while an output of 1.2 is “nursery rhymes”.
Now we semi-randomly adjust the mathematical operations until the neural network becomes more accurate. For example, maybe adding is more useful than multiplying, or maybe one input data is useless (eg the word “the” is too common to be useful).
The semi-random adjustments will make it so the most useful data is kept, and the “best” way to process the data to get the most accurate results is found.
After the neural network is trained and tested on good data, you can use it on other new data. Using the same set of mathematical operations and method to covert data into numerical input, you will probably get an output which is useful.
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