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Duration 14 hours
Course Outline
Introduction to TinyML
- Defining TinyML
- The importance of running machine learning on microcontrollers
- Contrasting conventional AI with TinyML
- Reviewing necessary hardware and software prerequisites
Configuring the TinyML Environment
- Setting up the development environment with Arduino IDE
- Overview of TensorFlow Lite and Edge Impulse
- Programming and configuring microcontrollers for TinyML tasks
Developing and Deploying TinyML Models
- Navigating the TinyML development lifecycle
- Training basic machine learning models suitable for microcontrollers
- Transforming AI models into TensorFlow Lite format
- Installing models onto physical hardware devices
Optimizing TinyML for Edge Devices
- Minimizing memory usage and computational load
- Applying quantization and model compression techniques
- Evaluating TinyML model performance through benchmarking
TinyML Use Cases and Applications
- Implementing gesture recognition via accelerometer data
- Performing audio classification and keyword detection
- Detecting anomalies for predictive maintenance purposes
TinyML Obstacles and Emerging Trends
- Addressing hardware constraints and optimization approaches
- Considering security and privacy issues in TinyML
- Exploring future innovations and research directions in TinyML
Recap and Subsequent Actions
Requirements
- Fundamental programming skills (in Python or C/C++)
- Basic knowledge of machine learning principles (recommended, though not mandatory)
- General understanding of embedded systems (optional, but beneficial)
Target Audience
- Software and hardware Engineers
- Data Scientists
- Enthusiasts of Artificial Intelligence
