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Course Outline
Introduction to TinyML and Edge AI
- Definition and scope of TinyML
- Benefits and challenges of running AI on microcontrollers
- Overview of key tools: TensorFlow Lite and Edge Impulse
- TinyML use cases in IoT and real-world scenarios
Configuring the TinyML Development Environment
- Installation and setup of the Arduino IDE
- Introduction to TensorFlow Lite for microcontrollers
- Utilizing Edge Impulse Studio for TinyML development
- Connecting and testing microcontrollers for AI applications
Developing and Training Machine Learning Models
- Understanding the TinyML workflow
- Collection and preprocessing of sensor data
- Training machine learning models for embedded AI
- Optimizing models for low-power and real-time processing
Implementing AI Models on Microcontrollers
- Converting AI models to TensorFlow Lite format
- Flashing and executing models on microcontrollers
- Validation and debugging of TinyML implementations
Enhancing TinyML Performance and Efficiency
- Techniques for model quantization and compression
- Power management strategies for edge AI
- Managing memory and computation constraints in embedded AI
Practical TinyML Applications
- Gesture recognition using accelerometer data
- Audio classification and keyword spotting
- Anomaly detection for predictive maintenance
Security and Future Trends in TinyML
- Safeguarding data privacy and security in TinyML applications
- Challenges of federated learning on microcontrollers
- Emerging research and advancements in TinyML
Summary and Next Steps
Requirements
- Hands-on experience with embedded systems programming
- Proficiency in Python or C/C++ programming
- Foundational understanding of machine learning concepts
- Knowledge of microcontroller hardware and peripherals
Target Audience
- Embedded systems engineers
- AI developers
21 Hours
Testimonials (1)
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