Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Course Outline
Introduction to TinyML
- What is TinyML?
- The importance of machine learning on microcontrollers
- Differences between traditional AI and TinyML
- Overview of necessary hardware and software
Establishing the TinyML Environment
- Installing the Arduino IDE and configuring the development setup
- Introduction to TensorFlow Lite and Edge Impulse
- Programming and configuring microcontrollers for TinyML tasks
Creating and Deploying TinyML Models
- Understanding the TinyML workflow
- Training a basic machine learning model for microcontrollers
- Transforming AI models into TensorFlow Lite format
- Deploying models to hardware devices
Enhancing TinyML for Edge Devices
- Minimizing memory and computational requirements
- Methods for quantization and model compression
- Evaluating 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
Challenges in TinyML and Future Trends
- Hardware constraints and optimization strategies
- Security and privacy considerations in TinyML
- Future developments and research in TinyML
Summary and Next Steps
Requirements
- Fundamental programming skills (in Python or C/C++)
- Awareness of machine learning principles (recommended, though not mandatory)
- Knowledge of embedded systems (optional but advantageous)
Target Audience
- Engineers
- Data scientists
- AI enthusiasts
14 Hours
