Merge pull request #31 from bclswl0827/main

fix: Fixed broken links
Seeed-Projects · Dec 30, 2024 · 7e8a6a5 · 7e8a6a5
2 parents 6f0214a + 2227138
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diff --git a/articles/Chapter_4-Large_Language_Model/Run_Gemma2_on_RaspberryPi.md b/articles/Chapter_4-Large_Language_Model/Run_Gemma2_on_RaspberryPi.md
@@ -11,7 +11,7 @@ Let's install [Gemma 2](https://ollama.com/library/gemma2:2b), a high-performing
 
 ## Install Ollama 
 
-Please refer to this [article](https://github.com/Seeed-Projects/Tutorial-of-AI-Kit-with-Raspberry-Pi-From-Zero-to-Hero/blob/main/articles/Chapter%204%20-%20Large%20Language%20Model/Setup_Ollama_on_RaspberryPi.md)
+Please refer to this [article](./Setup_Ollama_on_RaspberryPi)
 
 
 ## Install and run gemma2

diff --git a/articles/Chapter_4-Large_Language_Model/Run_Llama_on_RaspberryPi.md b/articles/Chapter_4-Large_Language_Model/Run_Llama_on_RaspberryPi.md
@@ -16,7 +16,7 @@ The 1B and 3B models were pruned from the Llama 8B, and then logits from the 8B
 
 ## Install Ollama 
 
-Please refer to this [article](https://github.com/Seeed-Projects/Tutorial-of-AI-Kit-with-Raspberry-Pi-From-Zero-to-Hero/blob/main/articles/Chapter%204%20-%20Large%20Language%20Model/Setup_Ollama_on_RaspberryPi.md)
+Please refer to this [article](./Setup_Ollama_on_RaspberryPi)
 
 ## Install and run llama
 

diff --git a/articles/Chapter_4-Large_Language_Model/Run_Multimodal_on_Raspberry.md b/articles/Chapter_4-Large_Language_Model/Run_Multimodal_on_Raspberry.md
@@ -12,7 +12,7 @@ The LLaVA-Phi-3 is an end-to-end trained large multimodal model designed to unde
 
 ## Install Ollama 
 
-Please refer to this [article](https://github.com/Seeed-Projects/Tutorial-of-AI-Kit-with-Raspberry-Pi-From-Zero-to-Hero/blob/main/articles/Chapter%204%20-%20Large%20Language%20Model/Setup_Ollama_on_RaspberryPi.md)
+Please refer to this [article](./Setup_Ollama_on_RaspberryPi)
 
 ## Install and run llava
 

diff --git a/articles/Chapter_4-Large_Language_Model/Run_Phi3.5_on_Raspberryi.md b/articles/Chapter_4-Large_Language_Model/Run_Phi3.5_on_Raspberryi.md
@@ -12,7 +12,7 @@ Let's run the 4-bit quantization (`Q4_0`), which will need 2.2GB of RAM, with an
 
 ## Install Ollama 
 
-Please refer to this [article](https://github.com/Seeed-Projects/Tutorial-of-AI-Kit-with-Raspberry-Pi-From-Zero-to-Hero/blob/main/articles/Chapter%204%20-%20Large%20Language%20Model/Setup_Ollama_on_RaspberryPi.md)
+Please refer to this [article](./Setup_Ollama_on_RaspberryPi)
 
 
 ## Install and run Phi3.5

diff --git a/articles/Chapter_5-Custom_Model_Development_and_Deployment/Convert_Your_Model.ipynb b/articles/Chapter_5-Custom_Model_Development_and_Deployment/Convert_Your_Model.ipynb
@@ -8,7 +8,7 @@
     "sidebar_position: 2\n",
     "---\n",
     "\n",
-    "> You can get this [Notebook](https://github.com/Seeed-Projects/Tutorial-of-AI-Kit-with-Raspberry-Pi-From-Zero-to-Hero/blob/main/articles/Chapter%205%20-%20Custom%20Model%20Development%20and%20Deployment/Convert%20Your%20Model.ipynb) on GitHub."
+    "> You can get this [Notebook](https://github.com/Seeed-Projects/Tutorial-of-AI-Kit-with-Raspberry-Pi-From-Zero-to-Hero/blob/main/articles/Chapter_5-Custom_Model_Development_and_Deployment/Convert_Your_Model.ipynb) on GitHub."
    ]
   },
   {

diff --git a/articles/Chapter_5-Custom_Model_Development_and_Deployment/Deploy_Your_Model.ipynb b/articles/Chapter_5-Custom_Model_Development_and_Deployment/Deploy_Your_Model.ipynb
@@ -8,7 +8,7 @@
     "sidebar_position: 3\n",
     "---\n",
     "\n",
-    "> You can get this [Notebook](https://github.com/Seeed-Projects/Tutorial-of-AI-Kit-with-Raspberry-Pi-From-Zero-to-Hero/blob/main/articles/Chapter%205%20-%20Custom%20Model%20Development%20and%20Deployment/Deploy%20Your%20Model.ipynb) on GitHub."
+    "> You can get this [Notebook](https://github.com/Seeed-Projects/Tutorial-of-AI-Kit-with-Raspberry-Pi-From-Zero-to-Hero/blob/main/articles/Chapter_5-Custom_Model_Development_and_Deployment/Deploy_Your_Model.ipynb) on GitHub."
    ]
   },
   {

diff --git a/articles/Chapter_5-Custom_Model_Development_and_Deployment/Training_Your_Model.ipynb b/articles/Chapter_5-Custom_Model_Development_and_Deployment/Training_Your_Model.ipynb
@@ -8,7 +8,7 @@
     "sidebar_position: 1\n",
     "---\n",
     "\n",
-    "> You can get this [Notebook](https://github.com/Seeed-Projects/Tutorial-of-AI-Kit-with-Raspberry-Pi-From-Zero-to-Hero/blob/main/articles/Chapter%205%20-%20Custom%20Model%20Development%20and%20Deployment/Training%20Your%20Model.ipynb) on GitHub."
+    "> You can get this [Notebook](https://github.com/Seeed-Projects/Tutorial-of-AI-Kit-with-Raspberry-Pi-From-Zero-to-Hero/blob/main/articles/Chapter_5-Custom_Model_Development_and_Deployment/Training_Your_Model.ipynb) on GitHub."
    ]
   },
   {

diff --git a/articles/Overview.md b/articles/Overview.md
@@ -8,10 +8,6 @@ sidebar_position: 1
 [![Issues](https://img.shields.io/github/issues/Seeed-Projects/Tutorial-of-AI-Kit-with-Raspberry-Pi-From-Zero-to-Hero?style=for-the-badge)](https://github.com/Seeed-Projects/Tutorial-of-AI-Kit-with-Raspberry-Pi-From-Zero-to-Hero/issues)
 [![MIT License](https://img.shields.io/github/license/Seeed-Projects/Tutorial-of-AI-Kit-with-Raspberry-Pi-From-Zero-to-Hero?style=for-the-badge)](https://opensource.org/licenses/MIT)
 
-
-
-
-
 **Build With**
 
 [![Python.js](https://img.shields.io/badge/Python-3776AB?style=for-the-badge&logo=python&logoColor=white)](https://www.python.org/)
@@ -48,53 +44,48 @@ In this chapter, we’ll cover foundational AI concepts, including an introducti
 
 | Topic | Description |
 |-------|-------------|
-| [Introduction to Artificial Intelligence](https://github.com/Seeed-Projects/Tutorial-of-AI-Kit-with-Raspberry-Pi-From-Zero-to-Hero/blob/main/articles/Chapter%201%20-%20Introduction%20to%20AI/Introduction_of_Artificial_Intelligence.md) | Learn the fundamentals of Artificial Intelligence, its applications, and its impact on various fields. |
-| [Introduction to Deep Neural Networks (DNN)](https://github.com/Seeed-Projects/Tutorial-of-AI-Kit-with-Raspberry-Pi-From-Zero-to-Hero/blob/main/articles/Chapter%201%20-%20Introduction%20to%20AI/Introduction_to_DNN.md) | Explore the structure and function of Deep Neural Networks, the foundation of many modern AI models. |
-| [Introduction to Convolutional Neural Networks (CNN)](https://github.com/Seeed-Projects/Tutorial-of-AI-Kit-with-Raspberry-Pi-From-Zero-to-Hero/blob/main/articles/Chapter%201%20-%20Introduction%20to%20AI/Introduction_of_Convolutional_Neural_Network.md) | Delve into Convolutional Neural Networks, key for image processing and computer vision tasks. |
-| [Introduction to Computer Vision](https://github.com/Seeed-Projects/Tutorial-of-AI-Kit-with-Raspberry-Pi-From-Zero-to-Hero/blob/main/articles/Chapter%201%20-%20Introduction%20to%20AI/Overview_of_Computer_Vision.md) | Understand computer vision, enabling machines to interpret and make decisions based on visual data. |
-| [Generative AI (GenAI)](https://github.com/Seeed-Projects/Tutorial-of-AI-Kit-with-Raspberry-Pi-From-Zero-to-Hero/blob/main/articles/Chapter%201%20-%20Introduction%20to%20AI/Introduction_of_Large_Language_Model.md) | Discover Generative AI, including large language models that can create content and interact with users. |
-
+| [Introduction to Artificial Intelligence](./Chapter_1-Introduction_to_AI/Introduction_of_Artificial_Intelligence) | Learn the fundamentals of Artificial Intelligence, its applications, and its impact on various fields. |
+| [Introduction to Deep Neural Networks (DNN)](./Chapter_1-Introduction_to_AI/Introduction_to_Deep_Neural_Network) | Explore the structure and function of Deep Neural Networks, the foundation of many modern AI models. |
+| [Introduction to Convolutional Neural Networks (CNN)](./Chapter_1-Introduction_to_AI/Introduction_of_Convolutional_Neural_Network) | Delve into Convolutional Neural Networks, key for image processing and computer vision tasks. |
+| [Introduction to Computer Vision](./Chapter_1-Introduction_to_AI/Introduction_of_Computer_Vision) | Understand computer vision, enabling machines to interpret and make decisions based on visual data. |
+| [Generative AI (GenAI)](./Chapter_1-Introduction_to_AI/Introduction_of_Large_Language_Model) | 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](https://github.com/Seeed-Projects/Tutorial-of-AI-Kit-with-Raspberry-Pi-From-Zero-to-Hero/blob/main/articles/Chapter%202%20-%20Configuring%20the%20RaspberryPi%20Environment/Introduction_to_OpenCV.md) | 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](https://github.com/Seeed-Projects/Tutorial-of-AI-Kit-with-Raspberry-Pi-From-Zero-to-Hero/blob/main/articles/Chapter%202%20-%20Configuring%20the%20RaspberryPi%20Environment/Introduction_to_TensorFlow_in_Raspberry_Pi_Environment.md#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 OpenCV in Raspberry Pi Environment](./Chapter_2-Configuring_the_RaspberryPi_Environment/Introduction_to_OpenCV) | 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](./Chapter_2-Configuring_the_RaspberryPi_Environment/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](https://github.com/Seeed-Projects/Tutorial-of-AI-Kit-with-Raspberry-Pi-From-Zero-to-Hero/blob/main/articles/Chapter%204%20-%20Large%20Language%20Model/Setup_Ollama_on_RaspberryPi.md) | Learn how to set up Ollama, an open-source large language model framework, on Raspberry Pi for AI-powered applications. |
-| [Run Llama on RaspberryPi](https://github.com/Seeed-Projects/Tutorial-of-AI-Kit-with-Raspberry-Pi-From-Zero-to-Hero/blob/main/articles/Chapter%204%20-%20Large%20Language%20Model/Run_Llama_on_RaspberryPi.md) | Follow the guide to run LLaMA, a lightweight yet powerful large language model, on your Raspberry Pi. |
-| [Run Gemma2 on RaspberryPi](https://github.com/Seeed-Projects/Tutorial-of-AI-Kit-with-Raspberry-Pi-From-Zero-to-Hero/blob/main/articles/Chapter%204%20-%20Large%20Language%20Model/Run_Gemma2_on_RaspberryPi.md) | Learn to deploy and run Gemma2, a state-of-the-art model, on your Raspberry Pi for AI tasks. |
-| [Run Phi3.5 on RaspberryPi](https://github.com/Seeed-Projects/Tutorial-of-AI-Kit-with-Raspberry-Pi-From-Zero-to-Hero/blob/main/articles/Chapter%204%20-%20Large%20Language%20Model/Run_Phi3.5_on_Raspberryi.md) | Get started with running Phi 3.5 on Raspberry Pi, one of the latest advancements in AI models. |
-| [Run Multimodal on RaspberryPi](https://github.com/Seeed-Projects/Tutorial-of-AI-Kit-with-Raspberry-Pi-From-Zero-to-Hero/blob/main/articles/Chapter%204%20-%20Large%20Language%20Model/Run_Multimodal_on_Raspberry.md) | Explore the deployment of multimodal models on Raspberry Pi to handle both text and visual data. |
-| [Use Ollama with Python](https://github.com/Seeed-Projects/Tutorial-of-AI-Kit-with-Raspberry-Pi-From-Zero-to-Hero/blob/main/articles/Chapter%204%20-%20Large%20Language%20Model/Use_Ollama_with_Python.md) | Learn how to integrate Ollama with Python for developing AI-powered applications and automating tasks. |
-
+| [Setup Ollama on RaspberryPi](./Chapter_4-Large_Language_Model/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](./Chapter_4-Large_Language_Model/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](./Chapter_4-Large_Language_Model/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](./Chapter_4-Large_Language_Model/Run_Phi3.5_on_Raspberryi) | Get started with running Phi 3.5 on Raspberry Pi, one of the latest advancements in AI models. |
+| [Run Multimodal on RaspberryPi](./Chapter_4-Large_Language_Model/Run_Multimodal_on_Raspberry) | Explore the deployment of multimodal models on Raspberry Pi to handle both text and visual data. |
+| [Use Ollama with Python](./Chapter_4-Large_Language_Model/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](https://github.com/Seeed-Projects/Tutorial-of-AI-Kit-with-Raspberry-Pi-From-Zero-to-Hero/blob/main/articles/Chapter%205%20-%20Custom%20Model%20Development%20and%20Deployment/Training%20Your%20Model.md) | 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](https://github.com/Seeed-Projects/Tutorial-of-AI-Kit-with-Raspberry-Pi-From-Zero-to-Hero/blob/main/articles/Chapter%205%20-%20Custom%20Model%20Development%20and%20Deployment/Convert%20Your%20Model.md) | 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](https://github.com/Seeed-Projects/Tutorial-of-AI-Kit-with-Raspberry-Pi-From-Zero-to-Hero/blob/main/articles/Chapter%205%20-%20Custom%20Model%20Development%20and%20Deployment/Deploy%20Your%20Model.md) | 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. |
-
+| [Training Your Model](./Chapter_5-Custom_Model_Development_and_Deployment/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](./Chapter_5-Custom_Model_Development_and_Deployment/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](./Chapter_5-Custom_Model_Development_and_Deployment/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.
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