I started by implementing some simple threshold neurons. The current goal is a multilayer perceptron (similar to the one in scikit-learn), and maybe other kinds of networks, such as self-organizing maps or radial basis networks.
I could try to implement a deep learning algorithm, but the big issue with them is time complexity. Probably, it would require the use of GPU, or some advanced "tricks", so I should start with something smaller. Also, I want to try different kinds of design approaches, including those that are not based on highly optimized vector algebra (I know that it might not be the best idea, but I want try it and see what happens). For example, a network, where each neuron is an object (normally the whole network is represented as a collection of weight matrices). It might turn out to be very slow, but more object-friendly. For now it's just an idea, but to try something like that I would need a small network with 1-100 neurons. Yours sincerely, Oleksandr
