C++ Back Propagation Neural Network Code v2

There was a lot of feedback on my neural network implementation, mostly regarding architectural suggestion so i sat down and rewrote my neural network implementation, its pretty elegant now. I seperated the training of the network from the actual network itself and so i have a basic feed forward network, a data set reader and a neural network trainer. I also renamed several data structures to make things more understandable, also i wasnt lazy and used proper header files and includes 😛

Below is an updated class diagram of the new version:

Here’s the updated implementation (with a VS2k8 solution):

The original tutorials can be found here:


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. I’m pretty overloaded with work and assignments so I haven’t been able to dedicate as much time as I would have liked to this tutorial, even so I feel its rather complete and any gaps will be filled in by my source code.

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Basic Neural Network Tutorial – Theory

Well this tutorial has been a long time coming. Neural Networks (NNs) are something that i’m interested in and also a technique that gets mentioned a lot in movies and by pseudo-geeks when referring to AI in general. They are made out to be these really intense and complicated systems when in fact they are nothing more than a simple input output machine (well at least for the standard Feed Forward Neural Networks (FFNN) ).  As with any field the more you delve into it the more technical it gets and NNs are the same, the more research you do into them the more complicated architectures, training techniques, activation functions become. For now this is just a simple primer into NNs.

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