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Course Outline
Introduction to TinyML and Edge AI
- Defining TinyML
- Benefits and challenges of running AI on microcontrollers
- Overview of TinyML tools: TensorFlow Lite and Edge Impulse
- Real-world applications of TinyML in IoT
Establishing the TinyML Development Environment
- Installing and configuring Arduino IDE
- Introduction to TensorFlow Lite for microcontrollers
- Leveraging Edge Impulse Studio for TinyML development
- Connecting and testing microcontrollers for AI tasks
Constructing and Training Machine Learning Models
- Understanding the TinyML workflow
- Gathering 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 executing models on microcontrollers
- Validating and debugging TinyML implementations
Enhancing TinyML for Performance and Efficiency
- Techniques for model quantization and compression
- Power management strategies for edge AI
- Addressing 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
- Background in embedded systems programming
- Proficiency in Python or C/C++
- Fundamental understanding of machine learning principles
- Knowledge of microcontroller hardware and peripherals
Target Audience
- Embedded systems engineers
- AI developers
21 Hours
Testimonials (1)
That we can cover advance topic and work with real-life example
