Machine Learning library for the web and Node.
machinelearn.js is a Machine Learning library written in Typescript. It solves Machine Learning problems and teaches users how Machine Learning algorithms work.
Using yarn
$ yarn add machinelearn
Using NPM
$ npm install --save machinelearn
On the browsers
We use jsdeliver to distribute browser version of machinelearn.js
<script src="https://cdn.jsdelivr.net/npm/machinelearn/machinelearn.min.js"></script>
<script>
const { RandomForestClassifier } = ml.ensemble;
const cls = new RandomForestClassifier();
</script>
Please see https://www.jsdelivr.com/package/npm/machinelearn for more details.
By default, machinelearning.js will use pure Javascript version of tfjs. To enable acceleration
through C++ binding or GPU, you must import machinelearn-node
for C++ or machinelearn-gpu
for GPU.
yarn add machinelearn-node
import 'machinelearn-node';
yarn add machinelearn-gpu
import 'machinelearn-gpu';
We welcome new contributors of all level of experience. The development guide will be added to assist new contributors to easily join the project.
machinelearn.js provides a simple and consistent set of APIs to interact with the models and algorithms. For example, all models have follow APIs:
fit
for trainingpredict
for inferencingtoJSON
for saving the model's statefromJSON
for loading the model from the checkpointTesting ensures you that you are currently using the most stable version of machinelearn.js
$ npm run test
Simply give us a :star2: by clicking on
We simply follow "fork-and-pull" workflow of Github. Please read CONTRIBUTING.md for more detail.
Great references that helped building this project!
Thanks goes to these wonderful people (emoji key):
Jason Shin 📝 🐛 💻 📖 ⚠️ |
Jaivarsan 💬 🤔 📢 |
Oleg Stotsky 🐛 💻 📖 ⚠️ |
Ben 💬 🎨 📢 🐛 💻 |
Christoph Reinbothe 💻 🤔 🚇 👀 |
Adam King 💻 ⚠️ 📖 |
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