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Duration 21 hours
Course Outline
Introduction to TinyML
- Exploring TinyML limitations and potential capabilities
- Overview of prevalent microcontroller platforms
- Comparison between Raspberry Pi, Arduino, and alternative boards
Hardware Setup and Configuration
- Preparing the Raspberry Pi OS environment
- Setting up Arduino boards
- Linking sensors and peripheral devices
Data Acquisition Methods
- Recording sensor data
- Processing audio, motion, and environmental metrics
- Constructing labeled datasets
Model Development for Edge Devices
- Choosing appropriate model architectures
- Training TinyML models utilizing TensorFlow Lite
- Assessing performance for embedded applications
Model Optimization and Conversion
- Implementing quantization strategies
- Converting models for microcontroller deployment
- Optimizing memory usage and computational load
Deployment on Raspberry Pi
- Executing TensorFlow Lite inference
- Incorporating model outputs into applications
- Diagnosing and resolving performance issues
Deployment on Arduino
- Utilizing the Arduino TensorFlow Lite Micro library
- Flash-ing models onto microcontrollers
- Verifying accuracy and runtime behavior
Assembling Complete TinyML Applications
- Designing integrated 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 in using microcontrollers
- Proficiency in Python or C/C++
Target Audience
- Makers
- Hobbyists
- Embedded AI developers
Custom Corporate Training
Training solutions designed exclusively for businesses.
- Customized Content: We adapt the syllabus and practical exercises to the real goals and needs of your project.
- Flexible Schedule: Dates and times adapted to your team's agenda.
- Format: Online (live), In-company (at your offices), or Hybrid.
Price per private group, online live training, starting from 4800 € + VAT*
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