iOrca whales trained through neural networks, that’s the future.

“ Wait, what?? That’s a horrible idea! “

Were the exact words of our NLP researcher when I talked to her about this concept. Maybe she’s right, but it’s also definitely a very interesting concept which has getting more attention in the Javascript community lately.

During the past year our team is building Bit which makes it simpler to build software using components. As part of our work, we develop ML and NLP algorithms to better understand how code is written, organized and used.

While naturally most of this work is done in languages like python, Bit lives in the Javascript ecosystem with its great front and back ends communities.

This interesting intersection led us to explore and experiment with the odd possibilities of using Javascript and Machine Learning together. Sharing from our research, here are some neat libraries which bring Javascript, Machine Learning, DNN and even NLP together. Take a look.

Brain.js is a Javascript library for Neural Networks replacing the (now deprecated) “brain” library, which can be used with Node.js or in the browser (note computation ) and provides different types of networks for different tasks. Here is a demo of training the network to recognize color contrast.

Training Brain.js color contrast recognition

Synaptic is a Javascript neural network library for node.js and the browser which enables you to train first and even second order neural network architectures. The project includes a few built-in architectures like multilayer perceptrons, multilayer long-short term memory networks, liquid state machines and a trainer capable of training a verity of networks.

Training Synaptic image-filter perceptron

This library provides fast neuro-evolution & backpropagation for the browser and Node.js, with a few built-in networks including perceptron, LSTM, GRU, Nark and more. Here is a rookie tutorial for simple training.

Developed by Stanford U PhD this popular library hasn’t been maintained for the past 4 years, but is definitely one of the most interesting projects on the list. It’s a Javascript implementation of neural networks supporting common modules, classification, regression, an experimental Reinforcement Learning module and is even able to train convolutional networks that process images.

Conventjs demo for toy 2d classification with 2-layer neural network

This Japanese-made library is built to run deep neural network pre-trained model on the browser, and fast. Since executing a DNN on a browser consumes a lot of computational resources, this framework optimizes the DNN model to compress the model data and accelerate execution through JavaScript APIs such as WebAssembly and WebGPU.

Neural style transfer example

This popular library allows you to train neural networks in a browser or run pre-trained models in inference mode, and even claims it can be used as NumPy for the web. With an easy-to-pick-up API this library can be used for a verity for useful applications, and is actively maintained.

Deeplearnjs teachable machine web-demo

Deep playground is an interactive visualization of neural networks, written in TypeScript using d3.js. Although this project basically contains a very basic playground for tensorflow, it can be repurposed for different means or used as a very impressive educational feature for different purposes.

Tensorflow web playground

This very popular library provides “modest natural-language processing in javascript”. It’s pretty basic and straight forward, and even compiles down to a single small file. For some reason, its modest “good enough” approach makes it a prime candidate for usage in almost any app in need of basic NLP.

Compromise reminds us of how simple English really is

This beautiful project is a deep learning and reinforcement learning Javascript library framework for the browser. Implementing a full stack neural-network based machine learning framework with extended reinforcement-learning support, some consider this project to be the successor of convnetjs.

A group of repositories providing Machine Learning tools for Javascript developed by the mljs organization which include supervised and unsupervised learning, artificial neural networks, regression algorithms and supporting libraries for statistics, math etc. Here’s a short walkthrough.

mljs projects on GitHub

A flexible neural network library for Node.js and the browser, which basically learns to make predictions, using a matrix implementation to process training data and enabling configurable network topology. You can also plug-and-play “minds” which already learned, which can be useful for your apps.

Really? 0/5? way to predict, mind!

Honorable mentions:

An actively maintained library for Node.js which provides tokenizing, stemming (reducing a word to a not-necessarily morphological root), classification, phonetics, tf-idf, WordNet, string similarity, and more.

Incubator-mxnet

Apache MXNet is a deep learning framework that allows you to mix symbolic and imperative programming on the fly with a graph optimization layer for performance. MXnet.js brings a deep learning inference API to the browser.

This library runs Keras models in the browser, with GPU support using WebGL. since Keras uses a number of frameworks as backends, the models can be trained in TensorFlow, CNTK, and other frameworks as well.

A development environment for deep learning that enables you to quickly design neural network architectures and machine learning pipelines with built-in version control for experiment reproduction. Worth checking out.

Not even as much of a library as a very cool demo / web game based on a chrome experiment by Google. Although I’m not sure what to do with it, it’s guaranteed to become the most enjoyable 15 minutes of your day.

Land lines by Google

What’s next?

Obviously, Javascript isn’t becoming the language of choice for Machine Learning , far from it. However, common issues such as performance, Matrix manipulations and abundance of useful libraries are slowly being bridged, closing the gap between common applications and useful Machine Learning.

You are welcome to suggest more useful libraries or projects to keep an eye on or ones you’ve been working on yourselves, which can be added to the list.