- Play your AI Kit from Beginner to Expert -
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This course is designed to teach you how to harness the power of AI on the Raspberry Pi, with a particular focus on using an AI kit to perform essential computer vision tasks. Throughout the course, you'll learn how to integrate AI into real-world IoT (Internet of Things) applications, from object detection and image classification to more complex visual recognition tasks. We will guide you step-by-step through setting up your Raspberry Pi, using AI frameworks, and deploying these models in various practical scenarios. Whether you are a hobbyist, a student, or a professional, this course will provide you with the foundational knowledge and hands-on experience necessary to bring AI-driven solutions to life on resource-constrained devices like the Raspberry Pi.
| reComputer AI R2130 |
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| Raspberry Pi 5 Starter Kit |
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| Raspberry Pi AI Kit | reComputer R1100 |
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Introduction to Machine Learning with Python
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
Programming Computer Vision with Python
Deep Learning for Computer Vision
Deep Learning for Natural Language Processing: Creating Neural Networks with Python
⏳ Indicates in progress, ✔️ indicates completed.
- ✔️ Introduction of Artificial Intelligence
- ✔️ Introduction of Deep Neural Network
- ✔️ Introduction of Convolutional Neural Network
- ✔️ Introduction of Computer Vision
- ✔️ Introduction of Large Language Model
- ✔️ Introduction to Pytorch in Raspberry Pi Environment
- ✔️ Introduction to TensorFlow in Raspberry Pi Environment
- ✔️ Introduction to OpenCV in Raspberry Pi Environment
- ✔️ Introduction to Ultralytics in Raspberry Pi Environment
- ⏳ Introduction to Hailo in Raspberry Pi Environment
- ✔️ Setup Ollama on RaspberryPi
- ✔️ Run Llama on RaspberryPi
- ✔️ Run Gemma2 on RaspberryPi
- ✔️ Run Phi3.5 on RaspberryPi
- ✔️ Run Multimodal on RaspberryPi
- ✔️ Use Ollama with Python
- ✔️ Training
- ✔️ Converting
- ✔️ Deploying
Open for everyone to contribute
See the open issues for a full list of proposed features (and known issues).
Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.
If you have a suggestion that would make this better, please follow this Contributor Guidelines and contribute your own code.
Don't forget to give the project a star! Thanks again!
Distributed under the MIT License. See LICENSE for more information.