Hi Oleksandr, I'm working half-time on a team doing simulations of spiking neural networks (on scala). Since the topic is "new" to me, I started following a MOOC on traditional machine learning and putting some of my code in here:
https://github.com/guillep/neural-experiments Also, I wanted to experiment on hand-written characters with the Mnist dataset (yann.lecun.com/exdb/mnist/) so I wrote a reader for the IDX format to load the dataset. https://github.com/guillep/idx-reader If you want I can take a look and we can discuss further :) Guille On Tue, Mar 21, 2017 at 11:30 AM, Oleksandr Zaytsev <olk.zayt...@gmail.com> wrote: > 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 >
