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Duration 21 hours
Course Outline
Introduction to TinyML
- Understanding the constraints and capabilities of TinyML
- Overview of common microcontroller platforms
- Comparison of Raspberry Pi, Arduino, and other boards
Hardware Setup and Configuration
- Preparing Raspberry Pi OS
- Configuring Arduino boards
- Connecting sensors and peripheral devices
Data Collection Techniques
- Capturing sensor data
- Managing 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 resources
Deployment on Raspberry Pi
- Executing TensorFlow Lite inference
- Integrating model outputs into applications
- Diagnosing and resolving performance issues
Deployment on Arduino
- Utilizing the Arduino TensorFlow Lite Micro library
- Flashing models onto microcontrollers
- Validating 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 foundational understanding of basic programming concepts
- Practical experience with using microcontrollers
- Familiarity with Python or C/C++
Target Audience
- Makers
- Hobbyists
- Embedded AI developers
