Introduction to TinyML Training Course
TinyML involves implementing machine learning techniques on microcontrollers and embedded devices with limited resources.
This guided, live training session (available online or in-person) is designed for novice engineers and data scientists who want to grasp the core concepts of TinyML, discover its practical uses, and deploy artificial intelligence models onto microcontrollers.
Upon completing this training, participants will be capable of:
- Grasping the fundamental principles of TinyML and its importance.
- Deploying efficient AI models on microcontrollers and edge hardware.
- Optimizing and refining machine learning models to minimize power usage.
- Utilizing TinyML for practical scenarios such as gesture recognition, anomaly detection, and audio processing.
Course Structure
- Interactive lectures and discussions.
- Extensive exercises and practical practice.
- Hands-on implementation within a live laboratory setting.
Customization Options for the Course
- For information on requesting a customized training programme for this course, please get in touch with us to make arrangements.
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
Need help picking the right course?
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Introduction to TinyML Training Course - Enquiry
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