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Course Outline
Introduction to Edge AI and NVIDIA Jetson
- Overview of edge AI applications
- Introduction to NVIDIA Jetson hardware
- Components of the JetPack SDK and development environment setup
Setting Up the Development Environment
- Installing JetPack SDK and configuring the Jetson board
- Understanding TensorRT and model optimization strategies
- Configuring the runtime environment
Optimizing AI Models for Edge Deployment
- Techniques for model quantization and pruning
- Accelerating models using TensorRT
- Converting models to ONNX format
Deploying AI Models on Jetson Devices
- Executing inference with TensorRT
- Integrating AI models into real-time applications
- Enhancing performance and minimizing latency
Computer Vision and Deep Learning on Jetson
- Deploying image classification and object detection models
- Utilizing AI for real-time video analytics
- Implementing robotics applications driven by AI
Edge AI Security and Performance Optimization
- Securing AI models on edge devices
- Managing power efficiency and thermal conditions
- Scaling AI applications across Jetson platforms
Project Implementation and Real-World Use Cases
- Developing an AI-powered IoT solution
- Deploying AI in autonomous systems
- Case studies of AI implementation on edge devices
Summary and Next Steps
Requirements
- Prior experience with AI model training and inference
- Foundational knowledge of embedded systems
- Proficiency in Python programming
Target Audience
- AI developers
- Embedded system engineers
- Robotics engineers
21 Hours
Testimonials (1)
That we can cover advance topic and work with real-life example
