NumpyNet is a very simple python framework for neural networks. It meant to be a teaching tool so that people can really get under the hood and learn the basics about how neural network are built and how they work.
It includes nice visualizations of the process so that the user can watch what is going on as the models learn and make predictions. It's only dependencies are numpy, which does the math, and visdom, which does the visualizations.

Currently this project is in it's infancy. The basic functionality is there but there's still a lot to do. So get in there and add some issues you'd like to see or better yet contribute some code!

Grab NumpyNet:

git clone https://github.com/UptakeOpenSource/numpynet.git
cd numpynet

Install NumpyNet (will install visdom as well):

python setup.py install

Start visdom server locally:

visdom

Open up http://localhost:8097 in a browser

Run a demo and have some fun:

python examples.py

We have not set up integration on Travis CI yet (but it's coming soon!). For now, to test that this project is building, just do the following:

./integration.sh