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Description
Students will learn the main concepts of deep learning, understand how to apply deep learning to data streams from cameras and other IoT sensors, and learn how to structure successful deep learning projects on resource constrained devices (Arduino / Rasberry Pi). Students will learn about deep learning architectures using TinyML / TensorFlow Lite and the constraints / requirements of Edge AI. Students will master not only the basic theory, but also learn how to diagnose errors and prioritise directions in deep learning projects. Students will practice all these ideas in Python and TensorFlow.
Aim: To give the students the understanding and practical experience of applying deep learning to sensor data and develop the skill set to design and implement deep learning systems for IoT devices. The module learning objectives are:
- Understand AI / machine learning terminology
- Understand deep learning opportunities and limitations
- Understand different types of deep learning model
- Implement deep learning models in Python using TensorFlow Lite
- Prepare data for model training (with Edge Impulse)
- Embed AI on sensor devices (Arduino / mobile phone / Raspberry Pi)
- Document and share project information on GitHub to support reproducible research
- Provide peer feedback to fellow students on project work
- Present design decisions and prototypes to receive critical feedback
Module deliveries for 2024/25 academic year
Last updated
This module description was last updated on 8th April 2024.
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