Deep Learning for Computer Vision from Scratch

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In this series, I will talk about computer vision, ranging from very fundamental topics to very advanced ones.
From June 2023 to January 2024, our team at the Centre for Intelligent Autonomous Manufacturing Systems, Queen’s University Belfast, conducted an extensive data collection project aimed at advancing human intention inference. Our goal was to validate...
I first heard of the contextual bandit algorithm a couple of years back as an undergrad. I never gave much thought to it. Recently, I started working on reinforcement learning for the thrill of picking up my HRI research again and shaking off the dus...

Assurance Packs for AI Systems

The issue and ethical conundrum with equality of outcome is that those who advocate for this ideal want everyone to end up on the same level, hence the advocacy for equal redistribution of results, regardless of merit, effort, or individual differenc...

One of the interesting research projects I collaborated on with colleagues in China last year was using vision to predict the tendency of developing Alzheimer’s disease. We used images from diverse clinical datasets, which gave us a base and provided...

I know the hype is all around LLMs right now, but deep learning for computer vision continues to drive advancements in AI too – from your smartphone to applications at airports. Perhaps you would like to learn how to build yours.
This past summer, I had the privilege of teaching a 3-day course on Deep Learning for Computer Vision.
The course materials are now open source and available on GitHub: DL4CV

In the course, we began with a recap of Python and an introduction to PyTorch, then explored image computation techniques. We progressed from coding a simple neural network from the ground up to building basic architectures and advancing to deep neural networks such as VGG, ResNet, and YOLO. We also performed inference for emotion recognition, traffic tracking, and simple object tracking.
The repository contains:
Lecture slides
Practical code samples (in PyTorch)
Datasets for hands-on projects
Step-by-step notebook tutorials
Feel free to explore, fork, and contribute. Your feedback is always welcome. #DeepLearning #ComputerVision #OpenSource