So what exactly is going on here, what is it that the network is identifying? Essentially, we are flattening our 3D view of the world into a 2D line or curve. A typical example of how this line may look normalized is as follows:
Normalized input pointsThose inputs represent the normalized view the neural network is training for, or perhaps, against. If you trained the network to recognize that line, then the warning sound should go off when it detects the said line. Of course, the more points you add, the better your recognizer may or may not work. We will leave it up to you to further test the network on your ...
