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+<div align="center">
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+<h1>Nvidia Jetson Edge AI Quick Start Series</h1>
+
+<img src="https://files.seeedstudio.com/wiki/wiki-platform/S-tempor.png" /><br>
+
+**English** | [**中文简体**](./docs/cn/README.md)
+
+</div>
+
+---
+
+
+
+
+
+
+
+
+
+
+## Course Overview
+This course is designed for beginners in edge computing, aiming to help students master edge AI technology by introducing edge computing hardware platforms, the basics of Python programming, and AI project development. Through practical projects, particularly the development of an intelligent surveillance system, the course encourages students to apply the knowledge they’ve learned to real-world problem-solving, fostering critical thinking and innovation.
+
++ **Course Platform**: reComputer J1020 v2 (Jetson Nano 4GB)
++ **Programming Language**: Python
++ **Tools Used**: OpenCV, PyTorch, YOLOv8
++ **Project Goal**: Intelligent Surveillance System
+
+## 🌱 Getting Started
+This course includes 26 lessons, each focusing on different AI skills and applications. You can start with any lesson of interest; each lesson includes both theoretical explanations and practical code examples.
+
+Each lesson includes:
+
++ **Course Video**: A brief introduction to the lesson content.
++ **Reading Materials**: Detailed course instructions and code implementation.
++ **Code Examples**: Python code examples supporting common AI libraries like OpenCV, PyTorch, etc.
+
+### Course Structure
+The course is divided into four main modules:
+
+1. **Basics of Edge AI Programming**: Learn the basics of edge computing and Python programming.
+2. **Introduction to Computer Vision Applications**: Master OpenCV and the fundamentals of computer vision.
+3. **Advanced Deep Learning**: Learn neural networks, convolutional neural networks (CNN), and object detection techniques.
+4. **Intelligent Surveillance System Development**: Develop an AI-powered intelligent surveillance system project.
+
+---
+
+## 🛠️ Course Setup and Requirements
+To successfully follow along with the course, you’ll need the following equipment and tools:
+
+### **Hardware Requirements**
++ **reComputer J1020 v2** (based on Jetson Nano 4GB)
++ **128GB NVMe M.2 SSD**
++ **Logitech C270 HD Camera**
+
+### **Software Requirements**
++ JetPack OS (for Jetson platforms)
++ Python 3.x development environment
++ AI libraries like OpenCV, PyTorch, YOLOv8
++ Visual Studio Code (recommended for writing Python code)
+
+---
+
+## 🔍 Further Learning
+After completing each lesson, you can deepen your understanding through these resources:
+
++ [OpenCV Documentation](https://opencv.org/) - Explore more on OpenCV for image processing and computer vision applications.
++ [PyTorch Tutorials](https://pytorch.org/tutorials/) - Learn how to use PyTorch for deep learning modeling.
++ [AI Project Hub](https://developer.nvidia.com/embedded/community/jetson-projects) - Discover additional AI projects to further develop your edge AI applications.
+
+---
+
+## 📋 Course Content
+The course includes GitHub text-based content and corresponding video lessons on Bilibili.
+
+| Lesson No. | Topic | Objectives | Video Link |
+| --- | --- | --- | --- |
+| **1. Basics of Edge AI Programming** | | | |
+| ✅ 00 | Introduction | Understand the course structure and learning path, clarify learning objectives and expected outcomes. | |
+| ✅ 01 | Introduction to Edge AI and Jetson Hardware Platform | Understand the course structure and edge AI concepts; familiarize with the reComputer J1020 v2 hardware platform and JetPack OS. | ✅ Video Link |
+| ✅ 02 | Python Development Environment Setup & Basics | Master basic Python syntax and AI programming tools; write simple Python programs. | ⏳ Video Link |
+| ⏳ 03 | Python Control Flow | Understand conditional statements and loops; write programs with control flow and optimize code. | ⏳ Video Link |
+| ⏳ 04 | Python Functions and Modules | Understand functions and modules; master function calls, parameters, and return values; write reusable modules. | ⏳ Video Link |
+| ⏳ 05 | Python Data Structures | Master operations on data structures like lists and dictionaries to solve real-world problems. | ⏳ Video Link |
+| ⏳ 06 | Python File Handling and Exception Handling | Learn file reading/writing and exception handling to improve program robustness. | ⏳ Video Link |
+| ⏳ 07 | Python Practical Exercise | Use Python knowledge to complete small projects and experience AI tools. | ⏳ Video Link |
+| **2. Introduction to Computer Vision Applications** | | | |
+| ⏳ 08 | Overview of Image Processing | Understand the basics of digital images and master the use of the OpenCV library to display images. | ⏳ Video Link |
+| ⏳ 09 | Basic Operations in OpenCV | Learn image scaling, rotation, cropping, and filter applications. | ⏳ Video Link |
+| ⏳ 10 | Video Stream Processing in OpenCV | Understand the concept of video streams; implement video capture and real-time processing. | ⏳ Video Link |
+| ⏳ 11 | Edge Detection and Morphological Operations | Use the Canny algorithm for edge detection; master morphological operations (erosion, dilation). | ⏳ Video Link |
+| ⏳ 12 | Contour Detection and Feature Extraction | Learn contour detection and feature matching for image registration. | ⏳ Video Link |
+| ⏳ 13 | Basics of Face Detection | Understand the basics of face detection and implement it using OpenCV. | ⏳ Video Link |
+| ⏳ 14 | Computer Vision Practical Project | Complete a computer vision project to develop teamwork and practical skills. | ⏳ Video Link |
+| **3. Advanced Deep Learning** | | | |
+| ⏳ 15 | Overview of Deep Learning and Project Introduction | Understand the basics and applications of deep learning; introduce the intelligent surveillance project. | ⏳ Video Link |
+| ⏳ 16 | Dataset Preparation and Preprocessing | Learn data collection and preprocessing techniques to prepare for model training. | ⏳ Video Link |
+| ⏳ 17 | PyTorch Basics | Master Tensor operations and autograd; write PyTorch programs. | ⏳ Video Link |
+| ⏳ 18 | Building Neural Network Models | Build neural network models; understand activation functions and training processes. | ⏳ Video Link |
+| ⏳ 19 | Convolutional Neural Networks (CNN) | Understand CNN structure and convolution operations; build an image classification model. | ⏳ Video Link |
+| ⏳ 20 | Transfer Learning and Pre-trained Models | Understand transfer learning and pre-trained models; improve model performance through fine-tuning. | ⏳ Video Link |
+| ⏳ 21 | Object Detection Basics | Learn object detection principles; prepare for YOLOv8 practice. | ⏳ Video Link |
+| ⏳ 22 | YOLOv8 Practice | Use YOLOv8 for object detection; train and test models. | ⏳ Video Link |
+| **4. Practical Project - Intelligent Surveillance System Development** | | | |
+| ⏳ 23 | Intelligent Surveillance System Development (Part 1) | Implement real-time object detection and integrate it into the surveillance system. | ⏳ Video Link |
+| ⏳ 24 | Intelligent Surveillance System Development (Part 2) | Add alert features and data recording to enhance system usability. | ⏳ Video Link |
+| ⏳ 25 | Model Optimization and Performance Improvement | Use tools like TensorRT to optimize models and improve inference speed. | ⏳ Video Link |
+| ⏳ 26 | Advanced Feature Development | Explore behavior analysis and anomaly detection features to enhance system intelligence. | ⏳ Video Link |
+
+---
+
+## 🙏 Contributions
+If you have suggestions for course improvement or spot code issues, please help us enhance the course by creating an [Issue](#) or [Pull Request](#).
+
+---
+
+## 📜 Open Source License
+This course is released under the [MIT License](LICENSE), meaning you can:
+
++ ✅ Freely use, modify, and distribute the course content
++ ✅ Use the course for commercial and non-commercial purposes
++ ✅ Use the course for teaching in educational institutions
++ ✅ Develop new courses or training materials based on this course
+
+---
+
+### Educator’s Guide
+We especially encourage educators to use this course for teaching:
+
++ Feel free to adjust the content and pace to suit your teaching needs
++ Integrate this course into your existing curriculum
++ Share teaching experiences and suggestions for improvement
++ We recommend acknowledging the original course source when using it
+
+---
+
+## 🎒 Other Courses
+Our team has developed additional introductory courses to help you expand your learning horizons:
+
++ [XXXX](about:blank)
+
+
+## 🎒 Additional Courses
+In addition to the Nvidia Jetson Edge AI Quick Start Series, this repository also includes materials for other courses. Expand your knowledge by exploring these additional resources:
+
+### TinyML with Wio Terminal Course
+Materials for the **TinyML with Wio Terminal Course** are available in this repository. This course covers the basics of TinyML using the Wio Terminal platform. 
+
+For a detailed course description, please visit the [Seeed Studio Wiki](https://wiki.seeedstudio.com/Wio-Terminal-TinyML/).
+
+
+