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Duration 21 hours
Course Outline
Foundations of TinyML
- Exploring the limitations and strengths of TinyML
- An overview of prevalent microcontroller platforms
- Comparative analysis of Raspberry Pi, Arduino, and alternative boards
Hardware Preparation and Setup
- Setting up the Raspberry Pi OS
- Configuring Arduino hardware
- Linking sensors and peripheral devices
Data Acquisition Methods
- Recording sensor readings
- Processing audio, motion, and environmental inputs
- Constructing annotated datasets
Designing Models for Edge Hardware
- Choosing appropriate model architectures
- Training TinyML models using TensorFlow Lite
- Assessing performance metrics for embedded applications
Optimizing and Converting Models
- Applying quantization techniques
- Transforming models for microcontroller compatibility
- Enhancing memory usage and computational efficiency
Implementation on Raspberry Pi
- Executing TensorFlow Lite inference
- Merging model outputs into broader applications
- Resolving performance-related challenges
Implementation on Arduino
- Leveraging the Arduino TensorFlow Lite Micro library
- Writing models to microcontrollers
- Validating accuracy and runtime behavior
Creating End-to-End TinyML Solutions
- Architecting comprehensive embedded AI workflows
- Building interactive, practical prototypes
- Conducting tests and refining project features
Conclusion and Future Directions
Requirements
- A foundational grasp of basic programming principles
- Prior experience operating microcontrollers
- Proficiency in either Python or C/C++
Target Audience
- Makers
- Technology hobbyists
- Embedded AI developers
