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Course Outline
Introduction to TinyML and Edge AI
- Defining TinyML
- Benefits and challenges of deploying AI on microcontrollers
- Survey of TinyML tools: TensorFlow Lite and Edge Impulse
- TinyML use cases in IoT and real-world scenarios
Setting Up the TinyML Development Environment
- Installation and configuration of Arduino IDE
- Overview of TensorFlow Lite for microcontrollers
- Utilizing Edge Impulse Studio for TinyML development
- Connecting and testing microcontrollers for AI tasks
Building and Training Machine Learning Models
- Comprehending the TinyML workflow
- Collecting and preprocessing sensor data
- Training machine learning models for embedded AI
- Optimizing models for low-power and real-time processing
Deploying AI Models on Microcontrollers
- Converting AI models to TensorFlow Lite format
- Flashing and running models on microcontrollers
- Validating and debugging TinyML implementations
Optimizing TinyML for Performance and Efficiency
- Methods for model quantization and compression
- Power management strategies for edge AI
- Managing memory and computation constraints in embedded AI
Practical Applications of TinyML
- Gesture recognition using accelerometer data
- Audio classification and keyword spotting
- Anomaly detection for predictive maintenance
Security and Future Trends in TinyML
- Ensuring data privacy and security in TinyML applications
- Challenges of federated learning on microcontrollers
- Emerging research and advancements in TinyML
Summary and Next Steps
Requirements
- Experience in embedded systems programming
- Proficiency in Python or C/C++ programming
- Fundamental understanding of machine learning concepts
- Knowledge of microcontroller hardware and peripherals
Audience
- Embedded systems engineers
- AI developers
21 Hours
Testimonials (1)
That we can cover advance topic and work with real-life example
