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Course Outline
Introduction to TinyML and Edge AI
- Defining TinyML.
- Advantages and challenges of implementing AI on microcontrollers.
- Overview of TinyML tools: TensorFlow Lite and Edge Impulse.
- Use cases of TinyML in IoT and real-world applications.
Setting Up the TinyML Development Environment
- Installing and configuring the Arduino IDE.
- Introduction to TensorFlow Lite for microcontrollers.
- Utilizing Edge Impulse Studio for TinyML development.
- Connecting and testing microcontrollers for AI applications.
Building and Training Machine Learning Models
- Understanding the TinyML workflow.
- Collecting and preprocessing sensor data.
- Training machine learning models for embedded AI.
- Optimizing models for low-power and real-time processing.
Deploying AI Models on Microcontrollers
- Converting AI models to the TensorFlow Lite format.
- Flashing and running models on microcontrollers.
- Validating and debugging TinyML implementations.
Optimizing TinyML for Performance and Efficiency
- Techniques for model quantization and compression.
- Power management strategies for edge AI.
- Memory and computation constraints in embedded AI.
Practical Applications of TinyML
- Gesture recognition using accelerometer data.
- Audio classification and keyword spotting.
- Anomaly detection for predictive maintenance.
Security and Future Trends in TinyML
- Ensuring data privacy and security in TinyML applications.
- Challenges of federated learning on microcontrollers.
- Emerging research and advancements in TinyML.
Summary and Next Steps
Requirements
- Experience in embedded systems programming.
- Familiarity with Python or C/C++ programming languages.
- Fundamental knowledge of machine learning concepts.
- Understanding of microcontroller hardware and peripherals.
Target Audience
- Embedded systems engineers.
- AI developers.
21 Hours
Testimonials (1)
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