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Course Outline
Introduction to TinyML
- Understanding the constraints and capabilities of TinyML.
- Review of common microcontroller platforms.
- Comparing Raspberry Pi against Arduino and other boards.
Hardware Setup and Configuration
- Preparing the Raspberry Pi OS.
- Configuring Arduino boards.
- Connecting sensors and peripherals.
Data Collection Techniques
- Capturing sensor data.
- Handling audio, motion, and environmental data.
- Creating labeled datasets.
Model Development for Edge Devices
- Selecting appropriate model architectures.
- Training TinyML models using TensorFlow Lite.
- Evaluating performance for embedded applications.
Model Optimization and Conversion
- Quantization strategies.
- Converting models for microcontroller deployment.
- Optimizing memory and computational efficiency.
Deployment on Raspberry Pi
- Running TensorFlow Lite inference.
- Integrating model output into applications.
- Troubleshooting performance issues.
Deployment on Arduino
- Utilizing the Arduino TensorFlow Lite Micro library.
- Flashing models onto microcontrollers.
- Verifying accuracy and execution behavior.
Building Complete TinyML Applications
- Designing holistic embedded AI workflows.
- Implementing interactive, real-world prototypes.
- Testing and refining project functionality.
Summary and Next Steps
Requirements
- A solid understanding of fundamental programming concepts.
- Prior experience with microcontroller usage.
- Familiarity with Python or C/C++.
Target Audience
- Makers.
- Hobbyists.
- Embedded AI developers.
21 Hours
