Introduction to TinyML Training Course
TinyML involves applying machine learning techniques to microcontrollers and embedded devices with limited resources.
This instructor-led, live training session (available online or on-site) is designed for engineers and data scientists at the beginner level who want to grasp the basics of TinyML, explore its practical uses, and deploy AI models on microcontrollers.
Upon completing this training, participants will be capable of:
- Grasping the core principles of TinyML and its importance.
- Deploying streamlined AI models on microcontrollers and edge devices.
- Optimising and fine-tuning machine learning models for low-power usage.
- Applying TinyML in real-world scenarios such as gesture recognition, anomaly detection, and audio processing.
Course Structure
- Interactive lectures and discussions.
- Numerous exercises and practical activities.
- Practical implementation in a live-lab setting.
Customisation Options
- To request a tailored training for this course, please get in touch with us to arrange it.
Course Outline
Introduction to TinyML
- What is TinyML?
- The significance of machine learning on microcontrollers
- Comparison between traditional AI and TinyML
- Overview of hardware and software requirements
Setting Up the TinyML Environment
- Installing Arduino IDE and setting up the development environment
- Introduction to TensorFlow Lite and Edge Impulse
- Flashing and configuring microcontrollers for TinyML applications
Building and Deploying TinyML Models
- Understanding the TinyML workflow
- Training a simple machine learning model for microcontrollers
- Converting AI models to TensorFlow Lite format
- Deploying models onto hardware devices
Optimizing TinyML for Edge Devices
- Reducing memory and computational footprint
- Techniques for quantization and model compression
- Benchmarking TinyML model performance
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
- Fundamental programming knowledge (Python or C/C++)
- Familiarity with machine learning concepts (recommended but not essential)
- Understanding of embedded systems (optional but beneficial)
Target Audience
- Engineers
- Data scientists
- AI enthusiasts
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Introduction to TinyML Training Course - Enquiry
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