Javascript ML Libraries
Tags: javascript, machinelearning, opensource, programming
Published: 2022-04-17 14:37:26

The era of javascript seems to be primary aspect in the stages of web development. Javascript for programming machine learning offers several advantages over Python and R mainly in terms of privacy speed and staying on the device. There are various javascript ML libraries that can be used to program machine learning models.

- **[ConvNetjs](https://cs.stanford.edu/people/karpathy/convnetjs/)**

ConvNetJS is a Javascript library for training Deep Learning models (Neural Networks) entirely in your browser. Open a tab and you're training. No software requirements, no compilers, no installations, no GPUs.

- **[deeplearn](https://www.npmjs.com/package/deeplearn)**

deeplearn.js is an open source hardware-accelerated JavaScript library for machine intelligence. deeplearn.js brings performance machine learning building blocks to the web, allowing you to train neural networks in a browser or run pre-trained models in inference mode. It provides an API that closely mirrors the TensorFlow eager API. deeplearn.js was originally developed by the Google Brain PAIR team to build powerful interactive machine learning tools for the browser, but it can be used for everything from education, to model understanding, to art projects.

- **[Mind](https://stevenmiller888.github.io/mindjs.net/)**

Flexible neural networks in JavaScript. Mind lets you easily create networks that learn to make predictions.

- **[Tensorflow.js](https://www.tensorflow.org/js)**

TensorFlow.js is a library for machine learning in JavaScript which is used to develop ML models in JavaScript, and use ML directly in the browser or in Node.js.

- **[ML.js](https://github.com/mljs/ml)**

This library is a compilation of the machine learning tools developed in the mljs organization.

- **[Neataptic.js](https://wagenaartje.github.io/neataptic/)**

Neataptic offers flexible neural networks; neurons and synapses can be removed with a single line of code. No fixed architecture is required for neural networks to function at all. This flexibility allows networks to be shaped for your dataset through neuro-evolution, which is done using multiple threads.

- **[Brain.js](https://brain.js.org/#/)**

Brain.js is super simple to use. You do not need to know Neural Networks in details to work with this. Brain.js is super simple to use. You do not need to know Neural Networks in details to work with this.

- **[ML5.js](https://ml5js.org/)**

ml5.js aims to make machine learning approachable for a broad audience of artists, creative coders, and students. The library provides access to machine learning algorithms and models in the browser, building on top of TensorFlow.js with no other external dependencies.

- **[Neuro.js](https://neuro.js.org/)**

Neuro.js is machine learning framework for building AI assistants and chat-bots.

- **[Keras.js](https://transcranial.github.io/keras-js/#/)**

The Keras.js demos still work but is no longer updated. Used to run Keras models in the browser, with GPU support using WebGL.

- **[Synaptic](https://caza.la/synaptic/#/)**

The javascript architecture-free neural network library for node.js and the browser

- **[Deep Forge](https://deepforge.org/)**

DeepForge is a development environment for deep learning designed for simplicity, collaboration and reproducibility of experiments

- **[Web DNN](https://mil-tokyo.github.io/webdnn/)**

Fastest DNN Execution Framework on Web Browser

Thanks for reading 🙂 If you have any other valuable javascript ML programming related content to share feel free to drop below 👇

<center>[Twitter Post](https://twitter.com/Dhanush_Nehru/status/1405948380354732032?s=19)</center>

<a href="https://www.buymeacoffee.com/dhanushnehru" target="_blank"><img src="https://cdn.buymeacoffee.com/buttons/default-orange.png" alt="Buy Me A Book" height="45" width="174"></a>

Connect with me via [Twitter](https://twitter.com/Dhanush_Nehru)