What are Neural Networks and How Do They Work? - Mac Jarvis
The Mathematics Behind How Computers Recongise Things
How do we recognise things? It may seem too obvious to even answer a question like this as, in the end, we all are able to recognise a cat from a dog, a pencil from a pen, and a mountain from a building. However, perhaps the more interesting question here is what are the mechanisms behind being able to recognise these objects, and differentiate them from possibly quite similar seeming objects?
This is certainly not an article about the psychology of learning and development but rather the ways in which we can draw certain basic parallels between how we are able to learn, and how we could possibly teach machines, fittingly called neural networks, how to learn to recognise these objects on their own. Our brains contain an extensive library of images from cats to dogs to pencils to pens. We may not be able to perfectly remember the exact appearance of every single cat we have ever seen, and it would be a rather bizarre phenomenon if we could, but we are able to gauge a gist of what that cat looked like. This gist is likely some amalgamation of shapes, colours, shades and features that allow us to distinguish it from other four-legged creatures but we leave that to our brains to handle subconsciously, instantaneously.