Because it is one of the most popular and accessible programming languages. It can act as a gateway language for the millions of devs out there to try out NN's without the barrier of learning another language.

Here are some other neural networks in JS:

https://github.com/harthur/brain: Neural Networks in JS

https://github.com/stevenmiller888/mind: a flexible neural network library

https://github.com/cazala/synaptic: an architecture-free neural network library for node.js and the browser

Sllightly off topic, but the Machine Learning department of the University of Tokyo has built a whole set of libraries for machine learning in Javascript: http://mil-tokyo.github.io/miljs.html

I think with JS being one of the most popular languages around, this can always be answered with 'to enable millions of devs to use this without the need to learn yet another language'

So much value. Cross-platform compatibility (browser/JS, server/Node, mobile/React-Native, robotics/Johny-Five, etc). In-built asynchronous execution of nodes (a boon in ANN architectures).

Then there's dev mindshare. So many people know JS, empowering them would add bodies to meet rising ML demand. I learned Python specifically for TensorFlow. Python's easy to learn, but like any language takes much time to master. I've mastered JS, so Python was a frustrating little reset.

All that said, this cazala/synaptic project doesn't look promising to me save as showcase. Better to focus on exposing JS APIs on existing computation-graph GPU-runnable frameworks, eg node-tensorflow (https://github.com/node-tensorflow/node-tensorflow).

Because you can put your demo online plus your browser is also a nice debugger and profiler. And you can use HTML5 for graphics/fancy stuff that works everywhere.

A better question is, "Why not?"

Is there something you'd like to share with us, Johnanneskanybal?