The library allows you to formulate and solve Neural Networks. Current support includes:
The code is available on Github under MIT license and I warmly welcome pull requests for new features / layers / demos and miscellaneous improvements. The library is also available on npm for use in Nodejs, under name convnetjs.
This project was started by @karpathy. I am a PhD student at Stanford studying Machine Learning and Computer Vision and I've worked on Deep Learning both as part of my research and as an intern at Google (multiple times). In early versions of this code I chose to go first for simplicity, core concepts and most common use cases, though many bells and whistles can be added over time to add modeling flexibility and improve training times.
The downside, of course, is that we're paying about an order of magnitude of efficiency in speed compared to native implementations, but once I get around implementing the core operations in WebGL this could be effectively eliminated.
I elaborate in much more detail on the back story behind the project, my motivations and potential uses in this interview with Data Science Weekly.