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Course Outline
Introduction to TinyML
- Defining TinyML
- The importance of machine learning on microcontrollers
- Comparing traditional AI with TinyML
- Hardware and software prerequisites overview
Establishing the TinyML Environment
- Installing the Arduino IDE and configuring the development environment
- Getting started with TensorFlow Lite and Edge Impulse
- Flashing and configuring microcontrollers for TinyML use
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 physical hardware
Optimizing TinyML for Edge Devices
- Minimizing memory and computational demands
- Methods for quantization and model compression
- Benchmarking 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 limitations and optimization strategies
- Security and privacy concerns in TinyML
- Future advancements and research in TinyML
Summary and Next Steps
Requirements
- Foundational 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
