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. Its only dependencies are numpy, which does the math, and visdom, which does the visualizations.
git clone https://github.com/uptake/numpynet.git cd numpynet
Install NumpyNet (will install
visdom as well):
python setup.py install
Start visdom server locally:
Run a demo and have some fun:
Currently this project is in its 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!
Check out these resouces in concert with
NumpyNet for a full appreciation of how a neural network works:
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