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Course Outline
Foundations of TinyML
- Exploring the limitations and potential of TinyML
- Overview of prevalent microcontroller architectures
- Comparative analysis of Raspberry Pi, Arduino, and alternative boards
Hardware Preparation and Setup
- Setting up the Raspberry Pi operating system
- Configuring Arduino environments
- Interfacing sensors and peripheral devices
Data Acquisition Methods
- Recording sensor inputs
- Processing audio, motion, and environmental metrics
- Constructing annotated datasets
Model Creation for Edge Computing
- Choosing appropriate model architectures
- Training TinyML models utilizing TensorFlow Lite
- Assessing performance for embedded scenarios
Model Refinement and Conversion
- Applying quantization techniques
- Adapting models for microcontroller execution
- Optimizing memory usage and computational efficiency
Implementation on Raspberry Pi
- Executing TensorFlow Lite inference
- Incorporating model outputs into broader applications
- Diagnosing and resolving performance bottlenecks
Implementation on Arduino
- Leveraging the Arduino TensorFlow Lite Micro library
- Writing models to microcontroller storage
- Validating accuracy and runtime behavior
Assembling Complete TinyML Systems
- Architecting comprehensive embedded AI workflows
- Building interactive, real-world prototypes
- Conducting testing and iterative refinement of project features
Conclusion and Future Directions
Requirements
- A solid grasp of fundamental programming principles
- Practical experience in utilizing microcontrollers
- Proficiency in Python or C/C++
Target Audience
- Maker community members
- Technology hobbyists
- Developers specializing in embedded AI
21 Hours