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Course Outline
Introduction to TinyML
- Defining TinyML
- The importance of machine learning on microcontrollers
- Comparing traditional AI with TinyML
- Overview of necessary hardware and software
Establishing the TinyML Environment
- Installing Arduino IDE and configuring the development setup
- Introduction to TensorFlow Lite and Edge Impulse
- Flashing and configuring microcontrollers for TinyML projects
Constructing and Deploying TinyML Models
- Understanding the TinyML workflow
- Training a basic machine learning model for microcontrollers
- Converting AI models to the TensorFlow Lite format
- Deploying models onto hardware devices
Optimizing TinyML for Edge Devices
- Minimizing memory and computational requirements
- Techniques for quantization and model compression
- Evaluating the performance of TinyML models
TinyML Applications and Use Cases
- Gesture recognition using accelerometer data
- Audio classification and keyword spotting
- Anomaly detection for predictive maintenance
TinyML Challenges and Future Trends
- Hardware constraints and optimization strategies
- Security and privacy issues in TinyML
- Future advancements and research in TinyML
Summary and Next Steps
Requirements
- Fundamental programming skills (Python or C/C++)
- Awareness of machine learning concepts (recommended, though not mandatory)
- Knowledge of embedded systems (optional but beneficial)
Target Audience
- Engineers
- Data scientists
- AI enthusiasts
14 Hours
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 3200 € + VAT*
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