Top AI Repos — open-source AI, indexed and scored
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
| Date | Stars |
|---|---|
| 2026-07-31 | 281 |
| 2026-08-06 | 281 |
Today
— stars today
This week
— stars this week
This month
— stars this month
Momentum
0.0
growth rate 0.00%/day
<!-- omit in toc --> # Embedded Machine Learning Courseware [](https://github.com/edgeimpulse/courseware-embedded-machine-learning/actions/workflows/mlc.yml) [](https://github.com/edgeimpulse/courseware-embedded-machine-learning/actions/workflows/mdl.yml) [](https://github.com/edgeimpulse/courseware-embedded-machine-learning/actions/workflows/spellcheck.yml) [](http://hits.dwyl.com/edgeimpulse/courseware-embedded-machine-learning) Welcome to the Edge Impulse open courseware for embedded machine learning! This repository houses a collection of slides, reading material, project prompts, and sample questions to get you started creating your own embedded machine learning course. You will also have access to videos that cover much of the material. You are welcome to share these videos with your class either in the classroom or let students watch them on their own time. This repository is part of the Edge Impulse University Program. Please see this page for more information on how to join: [edgeimpulse.com/university](https://edgeimpulse.com/university). <!-- omit in toc --> ## How to Use This Repository Please note that the content in this repository is not intended to be a full semester-long course. Rather, you are encouraged to pull from the modules, rearrange the ordering, make modifications, and use as you see fit to integrate the content into your own curriculum. For example, many of the lectures and examples from the TinyML Courseware (given by [[3]](#3-slides-and-written-material-for-tinyml-courseware-by-harvard-university-is-licensed-under-cc-by-nc-sa-40)) go into detail about how TensorFlow Lite works along with advanced topics like quantization. Feel free to skip those sections if you would just like an overview of embedded machine learning and how to use it with Edge Impulse. In general, content from [[3]](#3-slides-and-written-material-for-tinyml-courseware-by-harvard-university-is-licensed-under-cc-by-nc-sa-40) cover theory and hands-on Python coding with Jupyter Notebooks to demonstrate these concepts. Content from [[1]](#1-slides-and-written-material-for-introduction-to-embedded-machine-learning-by-edge-impulse-is-licensed-under-cc-by-nc-sa-40) and [[2]](#2-slides-and-written-material-for-computer-vision-with-embedded-machine-learning-by-edge-impulse-is-licensed-under-cc-by-nc-sa-40) cover hands-on demonstrations and projects using Edge Impulse to deploy machine learning models to embedded systems. Content is divided into separate *modules*. Each module is assumed to be about a week's worth of material, and each section within a module contains about 60 minutes of presentation material. Modules also contain example quiz/test questions, practice problems, and hands-on assignments. If you would like to see more content than what is available in this repository, please refer to the [Harvard TinyMLedu site](http://tinyml.seas.harvard.edu/) for additional course material. <!-- omit in toc --> ## License Unless otherwise noted, slides, sample questions, and project prompts are released under the [Creative Commons Attribution NonCommercial ShareAlike 4.0 International (CC BY-NC-SA 4.0) license](https://creativecommons.org/licenses/by-nc-sa/4.0/). You are welcome to use and modify them for educational purposes. The YouTube videos in this repository are shared via the standard YouTube license. You are allowed to show them to your class or provide links for students (and others) to watch. <!--
Excerpt of 83,265 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:870fb0682df7c34b, llm:Repository name: edgeimpulse/courseware-embedded-machine-learning — suggests courseware for embedded machine learning (Edge Impulse). Language: Jupyter Notebook. Purpose: educational materials for embedded ML on constrained devices.
matched fp:870fb0682df7c34b, llm:Repository name: edgeimpulse/courseware-embedded-machine-learning — suggests courseware for embedded machine learning (Edge Impulse). Language: Jupyter Notebook. Purpose: educational materials for embedded ML on constrained devices.
matched fp:870fb0682df7c34b, llm:Repository name: edgeimpulse/courseware-embedded-machine-learning — suggests courseware for embedded machine learning (Edge Impulse). Language: Jupyter Notebook. Purpose: educational materials for embedded ML on constrained devices.
matched fp:870fb0682df7c34b, llm:Repository name: edgeimpulse/courseware-embedded-machine-learning — suggests courseware for embedded machine learning (Edge Impulse). Language: Jupyter Notebook. Purpose: educational materials for embedded ML on constrained devices.
