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Course Outline
Introduction to Edge AI for Computer Vision
- Overview of Edge AI and its benefits
- Comparison: Cloud AI vs Edge AI
- Key challenges in real-time image processing
Deploying Deep Learning Models on Edge Devices
- Introduction to TensorFlow Lite and OpenVINO
- Optimizing and quantizing models for edge deployment
- Case study: Running YOLOv8 on an edge device
Hardware Acceleration for Real-Time Inference
- Overview of edge computing hardware (Jetson, Coral, FPGAs)
- Leveraging GPU and TPU acceleration
- Benchmarking and performance evaluation
Real-Time Object Detection and Tracking
- Implementing object detection with YOLO models
- Tracking moving objects in real-time
- Enhancing detection accuracy with sensor fusion
Optimization Techniques for Edge AI
- Reducing model size with pruning and quantization
- Techniques for reducing latency and power consumption
- Edge AI model retraining and fine-tuning
Integrating Edge AI with IoT Systems
- Deploying AI models on smart cameras and IoT devices
- Edge AI and real-time decision-making
- Communication between edge devices and cloud systems
Security and Ethical Considerations in Edge AI
- Data privacy concerns in edge AI applications
- Ensuring model security against adversarial attacks
- Compliance with AI regulations and ethical AI principles
Summary and Next Steps
Requirements
- Familiarity with computer vision concepts
- Experience with Python and deep learning frameworks
- Basic knowledge of edge computing and IoT devices
Audience
- Computer vision engineers
- AI developers
- IoT professionals
21 Hours
Testimonials (1)
I genuinely enjoyed the hands-on approach.
