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