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Course Outline
Introduction to Edge AI and NVIDIA Jetson
- Overview of edge AI applications
- Introduction to NVIDIA Jetson hardware
- JetPack SDK components and development environment
Setting Up the Development Environment
- Installing JetPack SDK and setting up the Jetson board
- Understanding TensorRT and model optimization
- Configuring the runtime environment
Optimizing AI Models for Edge Deployment
- Model quantization and pruning techniques
- Using TensorRT for model acceleration
- Converting models to ONNX format
Deploying AI Models on Jetson Devices
- Running inference with TensorRT
- Integrating AI models with real-time applications
- Optimizing performance and reducing latency
Computer Vision and Deep Learning on Jetson
- Deploying image classification and object detection models
- Using AI for real-time video analytics
- Implementing AI-powered robotics applications
Edge AI Security and Performance Optimization
- Securing AI models on edge devices
- Power efficiency and thermal management
- Scaling AI applications on Jetson platforms
Project Implementation and Real-World Use Cases
- Building an AI-powered IoT solution
- Deploying AI in autonomous systems
- Case studies of AI on edge devices
Summary and Next Steps
Requirements
- Experience with AI model training and inference
- Basic knowledge of embedded systems
- Familiarity with Python programming
Audience
- AI developers
- Embedded engineers
- Robotics engineers
21 Hours
Custom Corporate Training
Training solutions designed exclusively for businesses.
- Customized Content: We adapt the syllabus and practical exercises to the real goals and needs of your project.
- Flexible Schedule: Dates and times adapted to your team's agenda.
- Format: Online (live), In-company (at your offices), or Hybrid.
Price per private group, online live training, starting from 4800 € + VAT*
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