Overview
Build With
Master AIoT Skills with Raspberry Pi AI Kit
Explore AIoT with this hands-on course, guiding you from AI basics to advanced applications on the Raspberry Pi. Through DIY projects and step-by-step lessons, you’ll master essential tools like TensorFlow, Node-RED, Ultralytics, and Hailo, enabling you to build powerful AI-driven solutions in just one month. Ideal for hobbyists, students, and professionals, this beginner-friendly course provides practical skills to bring AI projects to life on resource-limited devices.
📚 Pre-requisites
| Raspberry Pi AI Kit | reComputer R1000 | Raspberry Pi 5 |
|---|---|---|
 |  |  |
| Purchase Now | Purchase Now | Purchase Now |
What You Will Learn
Chapter 1: Introduction to AI
In this chapter, we’ll cover foundational AI concepts, including an introduction to AI, Deep Neural Networks (DNNs), Convolutional Neural Networks (CNNs), and mastering computer vision. We’ll also touch on Generative AI, which drives some of the latest advancements in the field. This chapter will focus on the theory behind these essential topics, helping you understand the core of modern AI.
| Topic | Description |
|---|---|
| Introduction to Artificial Intelligence | Learn the fundamentals of Artificial Intelligence, its applications, and its impact on various fields. |
| Introduction to Deep Neural Networks (DNN) | Explore the structure and function of Deep Neural Networks, the foundation of many modern AI models. |
| Introduction to Convolutional Neural Networks (CNN) | Delve into Convolutional Neural Networks, key for image processing and computer vision tasks. |
| Introduction to Computer Vision | Understand computer vision, enabling machines to interpret and make decisions based on visual data. |
| Generative AI (GenAI) | Discover Generative AI, including large language models that can create content and interact with users. |
Chapter 2: Configuring the Raspberry Pi Environment
Here, you’ll get hands-on experience setting up your Raspberry Pi for AI projects. You’ll configure the device and install key AI frameworks like TensorFlow, OpenCV, PyTorch, and Ultralytics, along with the Hailo environment specifically designed for the Raspberry Pi.
| Topic | Description |
|---|---|
| Introduction to OpenCV in Raspberry Pi Environment | Learn how to set up and use OpenCV on the Raspberry Pi for computer vision projects, from installation to basic functions. |
| Introduction to TensorFlow in Raspberry Pi Environment | Discover the setup and basics of TensorFlow on Raspberry Pi, enabling AI model deployment on a resource-constrained device. |
Chapter 3: Computer Vision Projects and Practical Applications
This chapter moves into practical applications, starting with simple object detection tasks (like identifying specific objects with a trained model). You’ll work on a hands-on project: building an Intelligent Monitoring System that sends an alarm and screenshot via email when a person is detected.
Chapter 4: Large Language Models (LLMs)
Here, you’ll explore lightweight but powerful large language models, focusing on Ollama, an open-source framework compatible with Raspberry Pi. We’ll also introduce models like Meta's LLaMA, Google’s Gemini, and Microsoft’s Phi, alongside libraries and Python APIs to run these models on the Raspberry Pi.
| Topic | Description |
|---|---|
| Setup Ollama on RaspberryPi | Learn how to set up Ollama, an open-source large language model framework, on Raspberry Pi for AI-powered applications. |
| Run Llama on RaspberryPi | Follow the guide to run LLaMA, a lightweight yet powerful large language model, on your Raspberry Pi. |
| Run Gemma2 on RaspberryPi | Learn to deploy and run Gemma2, a state-of-the-art model, on your Raspberry Pi for AI tasks. |
| Run Phi3.5 on RaspberryPi | Get started with running Phi 3.5 on Raspberry Pi, one of the latest advancements in AI models. |
| Run Multimodal on RaspberryPi | Explore the deployment of multimodal models on Raspberry Pi to handle both text and visual data. |
| Use Ollama with Python | Learn how to integrate Ollama with Python for developing AI-powered applications and automating tasks. |
Chapter 5: Custom Model Development and Deployment
In this chapter, we’ll dive into creating a custom model with Hailo using your own data. You’ll learn to label data easily with Roboflow, generate the necessary labels, train YOLO models, and prepare the models for deployment on the Raspberry Pi.
| Topic | Description |
|---|---|
| Training Your Model | Learn how to train a custom AI model using the Hailo environment on the AI Kit, with practical guidance on data preparation and model training. |
| Convert Your Model | Discover how to convert your trained model into the ONNX format for compatibility with Hailo Edge Framework (HEF) on the AI Kit. |
| Deploy Your Model | Step-by-step guide to deploying your model as a Hailo Edge Framework (HEF) on the AI Kit, enabling efficient AI processing on your Raspberry Pi. |
Chapter 6: Raspberry Pi and AIoT
Finally, we’ll explore integrating AI and IoT (AIoT) by connecting to platforms like Node-RED, ThingsBoard, and Home Assistant. This chapter covers real-time applications embedding computer vision, such as smart retail, security systems, smart parking management, and IoT integrations with large language models for tasks like anomaly detection.