A month or so ago I started working on a neural network implementation in Rust, from scratch. I wasn’t interested in achieving the best performance, or having all the bells and whistles. I had a simple goal of understanding NEAT. For those of you that are not familiar with it, here’s a snippet from the Wikipedia
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a form of artificial intelligence that uses evolutionary algorithms to generate artificial neural networks, parameters, topology and rules
What fascinated me is that the system starts from a totally random set of simple neural networks. Very often, the evolution finds the best architecture and weights in a relatively short amount of time. The first problem I wanted to solve was training the network to be a XOR gate. It managed to do that after a few days of me writing code and figuring out where the bugs were. I was very excited when I found out it evolved the output activation function to be a step function. It figured out the outputs are always round numbers. All by itself. Wow.
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