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Course Outline
Introduction to Edge AI and NVIDIA Jetson
- Survey of edge AI use cases
- Overview of NVIDIA Jetson hardware specifications
Establishing the Development Environment
- Installation of JetPack SDK and configuration of the Jetson board
- Exploring TensorRT and strategies for model optimization
- Setting up the runtime environment
Optimizing AI Models for Edge Deployment
- Applying TensorRT for model acceleration
- Translating models to ONNX format
Deploying AI Models on Jetson Devices
- Executing inference via TensorRT
- Embedding AI models into real-time application workflows
- Enhancing performance and minimizing latency
Computer Vision and Deep Learning on Jetson
- Implementing image classification and object detection models
- Applying AI for real-time video analysis
Edge AI Security and Performance Optimization
- Protecting AI models on edge devices
- Scaling AI applications across Jetson platforms
Project Implementation and Real-World Use Cases
- Developing an AI-driven IoT solution
Conclusion and Future Directions
Requirements
Target Audience
- AI Developers
- Embedded Systems Engineers
- Robotics Engineers
21 Hours
Testimonials (1)
That we can cover advance topic and work with real-life example
