Neural Networks

A picture of a cat
Image provided courtesy of Reddit user TelescopicSaddlebag

Computers can seem pretty dumb sometimes, can’t they? Why can’t they just learn how to do things like we do? Learning comes so effortlessly to us humans; we don’t even remember learning something as extraordinarily complicated as speech – it just sort of happened. If I showed you 10 pictures, 5 with cats in them and 5 without (actually this is the internet, so 11 of those 10 pictures would have cats in them, but bear with me) you could easily identify which images contained cats. Because computers are basically math machines, unless you can very precisely define what a cat is, then a computer will not be very good at such a task. That’s where neural networks come in – what if we could simulate a human brain? And like a human brain, what if we could purpose our simulation to only look at cats?

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Decision Tree Learning

Which one is edible?
(Image from

Let’s say you want to learn how to classify something, but the ‘rules’ behind the classification are not obvious. For example, given some measurements about a person’s heart, can we tell if they have heart disease? What if you are hiking through the woods and want to know if a mushroom you see is edible or not? Perhaps the stem has a certain color, the head is a certain width, or it has a ‘curtain’ around it. Instead of spending years mastering which mushrooms are edible based on sight, how about you simply get a computer to tell you? Well, with decision trees you can!

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