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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
Delivery Options
Private Group Training
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- Pre-course call with your trainer
- Customisation of the learning experience to achieve your goals -
- Bespoke outlines
- Practical hands-on exercises containing data / scenarios recognisable to the learners
- Training scheduled on a date of your choice
- Delivered online, onsite/classroom or hybrid by experts sharing real world experience
Private Group Prices RRP from £5700 online delivery, based on a group of 2 delegates, £1800 per additional delegate (excludes any certification / exam costs). We recommend a maximum group size of 12 for most learning events.
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