Document motivation and benefits (and limits) of doing this in JS?

[jefffriesen]

I'm was really happy to see this library and your post (Hacker's Guide to Neural Networks). I work mostly with Javascript on the server and client and am considering building a some machine learning functionality in our web application. We are also working towards taking it offline, so a pure JS solution is really interesting.

However, a lot of people dismiss JS as a good language for this kind of heavy, computational work. Other hits against it are no immutable data structures (although now we have a couple great options), floating point math (0.1*0.2), and it's not statically typed.

I think it would be interesting to talk more in the intro to this repo about

• What motivated you built it in JS
• If or how JS floating point math problems would affect results
• Limitations on size of learning data sets
• For large data sets, presumably it's recommended to train on the server and load the saved trained networks in the browser. How big are saved trained networks for large data sets? Giving some rough guidelines would help determine if this is even an option for some projects.

I was interested in these questions previously, but then read through the Hacker News thread on your post (https://news.ycombinator.com/item?id=8553307). Cynical comments as usual, but there are good questions in there.

Thanks

[jefffriesen]

If ever there is a good example for going ML in the browser, it's this: http://playground.tensorflow.org/

Closing...