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
Take a look at our travis.yml for integration testing using Travis CI. For local testing use ./integration.sh.