In this module, we will compare Jetson with Raspberry Pi, helping you understand why Jetson is superior for AI applications. We will also guide you through flashing an SD card and selecting the optimal card for your device, ensuring a smooth setup.

In this module, we will cover the installation of essential libraries such as OpenCV and PyTorch on Jetson. We’ll explore their functionalities and guide you through setting up an AI-ready environment for efficient project development.

In this module, we will dive into computer vision basics, demonstrating image manipulation with OpenCV and PyTorch. You will learn core techniques such as edge detection, image filtering, and geometric transformations to work with images effectively.

In this module, we will introduce you to the world of object detection and explore the YOLO algorithm, including its variants and their suitability for various use cases.

In this module, we will walk you through the process of annotating a custom dataset, training a YOLO model, and applying it for number plate recognition. You will gain hands-on experience with both training and inference processes.

In this module, we will explore TensorRT, a critical tool for accelerating deep learning models on Jetson devices. We will also guide you through setting up your Jetson for TensorRT.

In this module, we will focus on optimizing the YOLOX object detection model using TensorRT. We will walk you through the conversion process, test the optimized model, and analyze performance improvements.

In this module, we will introduce DeepStream, explaining how it works and how to set up the DeepStream SDK on your Jetson device. You will also learn how to utilize DeepStream for real-time AI applications.

In this module, we will demonstrate how to integrate multiple camera feeds into the DeepStream SDK. You will learn about video streaming protocols and how to perform object detection across multiple cameras in real-time.

In this module, we will guide you through the process of implementing a vehicle detection and tracking system. You will learn how to configure the application and evaluate its performance in real-world scenarios.

In this module, we will cover the process of training a custom object detection model for number plate recognition. You will use Roboflow, Google Colab, and Paddle OCR to build and deploy an effective ANPR system.

In this module, we will introduce pose estimation and demonstrate how to use PoseNet on Jetson for accurate pose detection. We will also explore techniques to enhance PoseNet with Darknet and Mediapipe.

In this module, we will dive deeper into PoseNet for pose estimation. You will learn the full implementation process, gaining a thorough understanding of how to apply PoseNet effectively.

In this module, we will introduce you to DeepFake technology, discussing its ethical concerns and providing techniques for detecting DeepFake content using AI-based classification methods.

In this module, we will explain the role of face recognition in attendance management systems. You will learn how to implement and fine-tune such a system for accurate tracking of attendance.