Popular GitHub Repositories for Webgl References

A WebGL accelerated JavaScript library for training and deploying ML models.

Filament是一个实时物理渲染引擎，支持Android、iOS、Windows、Linux、macOS和WebGL2平台。

Creative Coding: Generative Art, Data visualization, Interaction Design, Resources.

A curated list of references for MLOps

WebGL-accelerated ML // linear algebra // automatic differentiation for JavaScript.

notes for software engineers getting up to speed on new AI developments. Serves as datastore for https://latent.space writing, and product brainstorming, but has cleaned up canonical references under the /Resources folder.

Run Keras models in the browser, with GPU support using WebGL

In this repository, I will share some useful notes and references about deploying deep learning-based models in production.

A machine learning software for extracting information from scholarly documents

Spreadsheet with AI, Code, Connections

Natural Language Processing Tasks and References

Javascript/WebGL lightweight face tracking library designed for augmented reality webcam filters. Features : multiple faces detection, rotation, mouth opening. Various integration examples are provided (Three.js, Babylon.js, FaceSwap, Canvas2D, CSS3D...).

A curated list of the latest breakthroughs in AI by release date with a clear video explanation, link to a more in-depth article, and?code

The Machine Learning & Deep Learning Compendium was a list of references in my private & single document, which I curated in order to expand my knowledge, it is now an open knowledge-sharing project compiled using Gitbook.

JavaScript/WebGL real-time face tracking and expression detection library. Build your own emoticons animated in real time in the browser! SVG and THREE.js integration demos are provided.

??? 개발자를 위한 북마크 모음집

References on Optimal Control, Reinforcement Learning and Motion Planning

My Reading Lists of Deep Learning and Natural Language Processing

A curated list of vibe coding references, collaborating with AI to write code.

A list of references on lidar point cloud processing for autonomous driving