convnetjs

Andrej Karpathy's blog has examples of JavaScript implementation of Neural Networks. https://github.com/karpathy/convnetjs

The current frameworks are deeply (ha!) unsuitable for any serious work, by which I mean anything where a neural network would approach outperform other methods. However there is some possibility that technologies like WebCL and replacements will overcome this limitation by allowing access to GPU resources. Further reading: [1][2][3] [1] https://github.com/waylonflinn/weblas [2] https://github.com/karpathy/convnetjs/issues/13 [3] https://github.com/mikeseven/node-webcl [4] https://github.com/stormcolor/webclgl

Question: can js call out to native libraries as well ? I'm wondering why BLAS/LAPACK is not callable from JS. there are all these questions about how to use LAPACK from js.. and none seem to work right. [1] http://stackoverflow.com/questions/21990243/use-emscripten-w... [2] https://github.com/software-engineering-amsterdam/MLitB/issu... [3] https://github.com/karpathy/convnetjs

specific field would be machine learning. I regularly follow Karpathy's blog and his convnetjs library [1], and also Harthur's brain library [2]. yes, I also found a lot of papers cryptic and hard to follow [1] https://github.com/karpathy/convnetjs [2] https://github.com/harthur/brain

With all due respect, Karpathy is serious -- and funnily enough he implemented deep learning (convolutional neural nets) in Javascript[1]. Admittedly he did it mainly for fun + browser demos (it's on the front page of Stanford's Convolutional Neural Networks for Visual Recognition course[2], which he is an instructor for), but for learning about the concepts, it's a good enough language. When the students understand the concepts and what they're interested in, then we can nudge them to focus on choice of languages. Even then it's a dangerous domain as there's so many options. Deep learning and want to focus on algorithms or only previously had high level experience? Python with Theano is a good bet and can take advantage of the CPU or GPU. Even Python + numpy. Replicating existing work in the literature and want to take advantage of the s

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....