convnetjs

karpathy is being too modest here. He's also created his own convnet.js [1], which you can play with online [2] (complete with relevant, working demos), etc. [1] https://github.com/karpathy/convnetjs [2] http://cs.stanford.edu/people/karpathy/convnetjs/

Frankly, the reason there's hype around neural nets again is because of the newer ways they can be trained to augment backprop. Neural nets have made a lot of progress in representation learning in recent years. A major problem that I'm hoping to fix is having the industry catch up to what academia and some of the bigger companies are doing with nets now. Neural nets are from a silver bullet, and shouldn't be used where feature introspection is a huge requirement (this is why decision tree/random forest is popular), but they are far from being what they were in the 90s. Note that I have a commercial interest in this so there's going to be inherent bias in my opinions. To be fair, javascript isn't a scientific computing language. To do most neat training with neural nets, you're going to want to either scale them out, add more layers, and

Here's a similar project which has more functionality: https://github.com/karpathy/convnetjs

If you're interested in modern neural networks/deep learning in the browser, Kaparthy's ConvNet.js [1], (MNIST demo [2]) is a better project from both a technical and pedagogical perspective. [1]: https://github.com/karpathy/convnetjs [2]: http://cs.stanford.edu/people/karpathy/convnetjs/demo/mnist....