Overview

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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

For AIoT objects

Raspberry Pi AI Kit	reComputer R1000
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For LLM object

Raspberry Pi 5 Starter Kit
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For Vision&LLM object

reComputer AI R2130
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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.
Introduction to Pytorch in Raspberry Pi Environment	Explore how to set up PyTorch on Raspberry Pi, enabling deep learning model training and inference on an edge device.
Introduction to Ultralytics in Raspberry Pi Environment	Learn how to use Ultralytics YOLO models on Raspberry Pi for object detection and tracking in real-world applications.
Introduction to Hailo in Raspberry Pi Environment	Get started with the Hailo AI accelerator on Raspberry Pi, covering installation, setup, and performance benefits for AI applications.

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.

Topic	Description
Running AI Tasks with Hailo -With AI Kit	Learn how to accelerate AI tasks on Raspberry Pi using the Hailo AI Kit, enabling faster and more efficient deep learning inference.
Deploying Custom AI Models Across Applications	Discover how to convert, optimize, and deploy custom AI models on the Hailo NPU for various real-world applications.
Run Clip Application with Hailo NPU	Implement OpenAI's CLIP model with the Hailo NPU on Raspberry Pi, enabling image-text understanding and classification tasks.

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.

Topic	Description
Smart Retail with reComputerR11 and AI kit	Explore how AI-powered solutions using reComputer R11 and the Hailo AI Kit can enhance smart retail applications, such as customer analytics and automated checkout.
Hailo-Powered Car Park Management with ThingsBoard	Learn how to integrate the Hailo AI Kit with ThingsBoard to build an intelligent car parking management system using Raspberry Pi.