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Duration 21 hours
Course Outline
Foundations of TinyML
- Exploring the limitations and strengths of TinyML
- Overview of popular microcontroller platforms
- Comparison of Raspberry Pi, Arduino, and other board options
Initial Hardware Setup and Configuration
- Setting up the Raspberry Pi operating system
- Configuring your Arduino board
- Linking sensors and external peripherals
Effective Data Acquisition Methods
- Recording data from sensors
- Processing audio, motion, and environmental inputs
- Building organized, labeled datasets
Creating Models for Edge Computing
- Choosing the right model architecture
- Training TinyML models using TensorFlow Lite
- Assessing model performance for embedded contexts
Optimizing and Converting Models
- Applying quantization techniques
- Adapting models for deployment on microcontrollers
- Enhancing memory usage and computational efficiency
Running Models on Raspberry Pi
- Executing inference with TensorFlow Lite
- Incorporating model results into broader applications
- Diagnosing and resolving performance bottlenecks
Deploying Models on Arduino
- Utilizing the Arduino TensorFlow Lite Micro library
- Transferring models to microcontroller memory
- Checking output accuracy and runtime behavior
Constructing Full-Scale TinyML Projects
- Planning comprehensive embedded AI workflows
- Building interactive, real-world functional prototypes
- Validating and polishing project capabilities
Wrap-Up and Future Directions
Requirements
- A solid grasp of fundamental programming principles
- Prior hands-on experience with microcontrollers
- Working knowledge of Python or C/C++
Target Learners
- Makers and creators
- Tech enthusiasts and hobbyists
- Developers specializing in embedded AI
