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コース概要
Introduction to Edge AI
- Definition and key concepts
- Differences between Edge AI and Cloud AI
- Benefits and challenges of Edge AI
- Overview of Edge AI applications
Edge AI Architecture
- Components of Edge AI systems
- Hardware and software requirements
- Data flow in Edge AI applications
- Integration with existing systems
Setting Up the Edge AI Environment
- Introduction to Edge AI platforms (Raspberry Pi, NVIDIA Jetson, etc.)
- Installing necessary software and libraries
- Configuring the development environment
- Initializing the Edge AI setup
Developing Edge AI Models
- Overview of machine learning and deep learning models
- Training models for edge deployment
- Model optimization techniques
- Tools and frameworks for Edge AI development
Deploying Edge AI Applications
- Steps for deploying models on edge devices
- Monitoring and managing deployed models
- Real-time data processing and inference
- Case studies and examples
Use Cases and Applications
- Industry-specific applications of Edge AI
- Case studies in healthcare, automotive, and smart homes
- Success stories and lessons learned
- Future trends and opportunities in Edge AI
Ethical Considerations and Best Practices
- Ensuring privacy and security in Edge AI
- Addressing bias and fairness
- Compliance with regulations and standards
- Best practices for responsible AI deployment
Hands-On Projects and Exercises
- Developing a simple Edge AI application
- Real-world projects and scenarios
- Collaborative group exercises
- Project presentations and feedback
Summary and Next Steps
要求
- An understanding of basic AI and machine learning concepts
- Experience with programming languages (Python recommended)
- Familiarity with general computing concepts
Audience
- Developers
- IT professionals
14 時間
