The most common implementation of a neural network I’ve come across is the one where each neuron is modeled as an object and the neural network stores all these objects. Now I’ve seen various implementations and wait for it… here comes an OO rant: I don’t understand why people feel the need to encapsulate everything in classes. So what critical data storage do we need for our neural network? I’m just going to go over the very basic architecture. Okay so how do we implement our neural network? I’m not going to cover every aspect in great detail since you can just look at my source code. 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. This will probably occur with this tutorial in the coming week so please bear with me. I noticed mistakes and better ways of phrasing things in the first tutorial (thanks for the comments guys) and rewrote large sections. So I’ve now finished the first version of my second neural network tutorial covering the implementation and training of a neural network.
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