What a neural network is and how it’s used to learn cars to drive themselves
In: Technology
A neural network is lots and lots of very simple equations/algorithms connected together. These very simple units feed into other units which feed into other units. There can be anywhere from a few dozen to millions and millions of units all joined up in a huge web. When putting together a neural network, you start with a bunch of random equations – and they can’t do anything useful.
This is where training comes in – you plug in some data to the neural network where you already know the correct answer (e.g an image of a person, labelled as “person”, or an image of a cat labelled “cat”), or you have a defined goal where you have some mathematical criteria where you can say how good the response is. The answer the network gives, minus the real answer gives you an error term. You use this error to work out what adjustments you can make to the network to get you to the correct answer, and you make a very small fraction of that adjustment. You then keep showing the network hundreds, thousands or millions of other data/answer pairs, and every time make a very small adjustment. Over time the network learns its required task.
You can use neural networks to do things that classically have been very hard to program. For example they can do image recognition, or speach recognition and all sorts of other things.
With self driving, lots of neural networks are connected. You might have one for recognising cars/vehicles/pedestrians and another for road markings, and another for reading signs. These can connect to neural networks that control steering inputs and throttle (and connect to classical rule based programming too!). By meticulously training all these parts you can create a self driving car.
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