Following the general hype around TypeScript’s rise among the most established programming languages, Kalimdor is here to bring TypeScript in machine learning in order to help you solve your ML problems as well teach you how ML algorithms work.
Kalimdor is a browser-based machine learning library written primarily in TypeScript, and as it’s stated in the official blog post:
Using the library, Javascript developers can compete in Kaggle competitions as equally as the Python developers!
Kalimdor is a library very similar to ScikitLearn, that runs directly on the browsers and Node.js environment and, in fact, many APIs are direct translations from ScikitLearn and rewritten into Typescript.
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Some of the features of Kalimdor include:
- Supervised and unsupervised models including Decision Tree, Random Forest, PCA, KMeans and SVM
- It supports utilities including train_test_split, confusion_matrix, and KFold
- Machine Learning on the browser and Node.js
- Learning APIs for users
- Low entry barrier
For more information on this ML library, head over to the GitHub repo.
Getting started
If you are eager to get started with Kalimdor, you can set up a Node.js environment and install Kalimdor.js by running:
# using Yarn $ yarn add kalimdor -save # using NPM $ npm install kalimdor -save-dev
The post Meet Kalimdor — A machine learning library written in TypeScript appeared first on JAXenter.
Source : JAXenter
