Neural Network Tutorial. Introduction I have been interested in artificial intelligence and artificial life for years and I read most of the popular books printed on the subject.
I developed a grasp of most of the topics yet neural networks always seemed to elude me. Sure, I could explain their architecture but as to how they actually worked and how they were implemented… well that was a complete mystery to me, as much magic as science. I bought several books on the subject but every single one attacked the subject from a very mathematical and academic viewpoint and very few even gave any practical uses or examples. So for a long long time I scratched my head and hoped that one day I would be able to understand enough to experiment with them myself. Basic Neural Network Tutorial : C++ Implementation and Source Code. So I’ve now finished the first version of my second neural network tutorial covering the implementation and training of a neural network.
I noticed mistakes and better ways of phrasing things in the first tutorial (thanks for the comments guys) and rewrote large sections. This will probably occur with this tutorial in the coming week so please bear with me. Backpropagation. The project describes teaching process of multi-layer neural network employing backpropagation algorithm.
To illustrate this process the three layer neural network with two inputs and one output,which is shown in the picture below, is used: Each neuron is composed of two units. First unit adds products of weights coefficients and input signals. The second unit realise nonlinear function, called neuron activation function. Signal e is adder output signal, and y = f(e) is output signal of nonlinear element.