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Course Outline
Introduction to TinyML
- Defining TinyML.
- The importance of machine learning on microcontrollers.
- Contrasting traditional AI with TinyML.
- Reviewing hardware and software prerequisites.
Establishing the TinyML Environment
- Installing Arduino IDE and configuring the development workspace.
- Overview of TensorFlow Lite and Edge Impulse.
- Flashing and configuring microcontrollers for TinyML tasks.
Constructing and Deploying TinyML Models
- Exploring the TinyML workflow.
- Training a basic machine learning model for microcontrollers.
- Converting AI models into TensorFlow Lite format.
- Loading models onto hardware devices.
Enhancing TinyML Performance on Edge Devices
- Minimizing memory and computational demands.
- Methods for quantization and model compression.
- Evaluating the performance of TinyML models.
TinyML Applications and Use Cases
- Recognizing gestures via accelerometer data.
- Classifying audio and identifying keywords.
- Detecting anomalies for predictive maintenance.
TinyML Challenges and Future Trends
- Hardware constraints and optimization approaches.
- Addressing security and privacy issues in TinyML.
- Upcoming advancements and research directions in TinyML.
Summary and Next Steps
Requirements
- Fundamental programming skills (Python or C/C++)
- General knowledge of machine learning concepts (recommended, though not mandatory)
- Basic understanding of embedded systems (optional but beneficial)
Target Audience
- Engineers
- Data scientists
- AI enthusiasts
14 Hours
