Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Duration 21 hours
Course Outline
Foundations of TinyML
- Analyzing the constraints and potentials of TinyML
- Surveying prevalent microcontroller platforms
- Evaluating Raspberry Pi against Arduino and alternative boards
Hardware Preparation and Configuration
- Setting up the Raspberry Pi operating system
- Configuring Arduino board parameters
- Integrating sensors and peripheral devices
Data Acquisition Strategies
- Capturing information from sensors
- Processing audio, motion, and environmental inputs
- Constructing labeled datasets
Model Creation for Edge Computing
- Choosing appropriate model architectures
- Training TinyML models using TensorFlow Lite
- Assessing performance for embedded applications
Model Refinement and Conversion
- Implementing quantization techniques
- Adapting models for microcontroller integration
- Optimizing memory usage and computational efficiency
Implementation on Raspberry Pi
- Executing TensorFlow Lite inference
- Incorporating model outputs into software applications
- Diagnosing and resolving performance bottlenecks
Implementation on Arduino
- Leveraging the Arduino TensorFlow Lite Micro library
- Writing models to microcontrollers
- Validating precision and execution logic
Developing Comprehensive TinyML Systems
- Architecting integrated embedded AI processes
- Building interactive, practical prototypes
- Testing and iterating on project capabilities
Conclusions and Future Directions
Requirements
- Fundamental grasp of basic programming principles
- Prior experience in operating microcontrollers
- Proficiency in Python or C/C++
Target Audience
- Makers
- Hobbyists
- Developers focused on Embedded AI
