synaptic

For anyone interested, here is a sampling of available ML libs in JavaScript. The first two, including tensorflow.js, are sub-libraries of the major ML projects, and the others are custom JS libs: [tensorflow.js](https://github.com/tensorflow/tfjs) [mxnet.js](https://github.com/dmlc/mxnet.js/) [propel.js](https://github.com/propelml/propel) - this uses tensorflow.js under the hood [ml5](https://github.com/ml5js/ml5-library) - also uses tensorflow.js [webdnn](https://github.com/mil-tokyo/webdnn) [brain.js](https://github.com/BrainJS/brain.js) [mljs](https://github.com/mljs/ml) [synaptic](https://github.com/cazala/synaptic) edit: added the ml5 lib mentioned in a subsequent comment.

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

Three suggestions: 1. Embedded. The world is creating tons of new products with increasing levels of technological sophistication. Much of the libraries, tooling, and learning materials needed are yet to be created. Start with Arduino on the ESP32. 2. IoT security. Devices like the ESP8266 and ESP32 are quickly finding their way into many different applications, but as far as I know, no one has vetted their RTOS. This is dangerous because they are networked and difficult for consumers to understand in a tangible way. Likewise how can physical security be protected with these chips and others like it? 3. Machine learning. Be an early adopter and discover ways to use this new technology. There's a great-looking JS ML framework that you can use to get your feet wet without jumping to R, C++, or Python. https://github.com/cazala/synaptic

Fascinating. Many years ago, when I wanted to become a programmer and I didn't know anything about code, I used to fantasize and be amazed by programs. Code was like dark magic. This is how I feel today about machine learning. Neural networks, liquid state machines. It's wonderful voodoo to my eyes. I hope one day I get to work in that field, it seems so useful for solving big world problems. I have notice a definite rise in articles being written and shared on HN about it lately, that's great. For those completely in the dark, I found this library to have great wiki pages about the basics of neural network programming. Great read, I recommend it. https://github.com/cazala/synaptic/wiki/Neural-Networks-101

If you haven't already, check out Synaptic [1] by Juan Cazala. I use it in many of my ML projects and it contains a lot of useful examples and documentation. I recommend you take a look at the source as well, which is easy enough to understand. [1] https://github.com/cazala/synaptic

Yes, the success metrics are measured on the training set. I just ran a measurement with a separately generated testing set consisting of 52k characters which yielded a success rate of 98.53% on letters a-z. This turns out to be almost exactly the same as the rate presented on the repo page (98.52%). Will upload the result for your perusal as soon as possible. You will have to browse the synaptic neural network library [1] for an answer to your second question. [1]: https://github.com/cazala/synaptic EDIT: A separate testing set is now generated in addition to the training set. I updated the success rates of the examples in the readme accordingly.

Thanks. To answer your first question, I (perhaps naively) assumed that the synaptic library used correct naming for its network prototypes [1]. My implementation contains a few modifications to the one defined as 'Architect.Hopfield' [2], which is why I decided to put it in a separate file. It also helps a visitor to know how the network is defined without needing to browse the source of the synaptic library. [1]: https://github.com/cazala/synaptic/blob/master/README.md#hop... [2]: https://github.com/cazala/synaptic/blob/master/src/architect...