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Course Outline
Introduction to TinyML
- Exploring the constraints and potential of TinyML
- Overview of prevalent microcontroller platforms
- Comparative analysis of Raspberry Pi, Arduino, and alternative boards
Hardware Setup and Configuration
- Preparing the Raspberry Pi OS environment
- Configuring Arduino boards for development
- Connecting sensors and peripheral devices
Data Collection Techniques
- Capturing high-quality sensor data
- Processing audio, motion, and environmental inputs
- Constructing labeled datasets for training
Model Development for Edge Devices
- Choosing appropriate model architectures
- Training TinyML models utilizing TensorFlow Lite
- Assessing performance metrics for embedded scenarios
Model Optimization and Conversion
- Applying quantization strategies
- Converting models for microcontroller compatibility
- Optimizing memory usage and computational efficiency
Deployment on Raspberry Pi
- Executing TensorFlow Lite inference
- Integrating model outputs into application logic
- Diagnosing and resolving performance bottlenecks
Deployment on Arduino
- Leveraging the Arduino TensorFlow Lite Micro library
- Flashing models onto microcontroller hardware
- Validating accuracy and execution stability
Building Complete TinyML Applications
- Architecting comprehensive embedded AI workflows
- Implementing interactive, real-world prototypes
- Testing and iterating on project functionality
Summary and Next Steps
Requirements
- Fundamental knowledge of programming principles
- Practical experience with microcontroller operations
- Proficiency in Python or C/C++
Target Audience
- Makers
- Hobbyists
- Embedded AI developers
21 Hours
