One of the most, if not the most, well-known models in Artificial intelligence (AI) is the "YOLO" model series. When I was in school, YOLO used to mean something else. And yet, here I am 15 years later writing an article about it -- who would have thought? YOLO (You Only Look Once) is a popular set of object detection models used for real-time object detection and classification in computer vision. Originally developed by Joseph Redmon, Ali Farhadi, and Santosh Divvala, YOLO aims to achieve high accuracy in object detection with real-time speed.

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Computer vision can easily distinguish between well-defined shapes, for instance, a sphere and a cube. Things go awry with less distinct forms. It's easy for the human eye to differentiate between a cat and a dog -- you know what is what. But computers have no such innate capability, and even the most advanced computer vision algorithms often mistake a cat for a dog and vice versa. Computers have to be trained rigorously to classify fuzzy objects.

I took a year off Medium and never posted a single article on computer vision. Looking back, the last article I posted here was December 12, 2020 and boy was it a long a time ago. The truth is that I was torn between my game project and also a million other things like engineering studies, competitions and projects, internships and my time in self-learning computer vision skills. But, it was a great year nonetheless and I have to say, I am proud of myself that I am still able to continue to learn about computer vision and even got an internship in this field. Needless to say, I learnt a lot from the job and here's the breakdown.

If you're ready to take on a brand new challenge, and learn about AI techniques that you've never seen before in traditional supervised machine learning, unsupervised machine learning, or even deep learning, then this course is for you. Moreover, the course is packed with practical exercises that are based on real-life examples. So not only will you learn the theory, but you will also get some hands-on practice building your own models. There are five big projects on healthcare problems and one small project to practice.

Imagine being able to embark on a real-time computer vision project in a few hours, with no code to build a traffic control system, a warehouse monitoring system, or an in-store point of sale optimization system. Like the apps that are built on top of smartphone operating systems, these smart computer vision projects can use a multitude of proprietary and vendor algorithms. Because they are built on top of BrainFrame, an operating system for computer vision that comes with a Smart Vision AI Developers Kit, they take a fraction of the time to build than other computer vision projects. BrainFrame is one of the core products of Aotu.ai, started by two founders, Stephen Li and Alex Thiel. Stephen applied his experience building out the Android operating system to BrainFrame.

My aim, as always, was to keep the projects as diverse as possible so you can pick the ones that fit into your data science journey. If you're a beginner, I would suggest starting with the PalmerPenguins dataset as most folks aren't even aware of it right now. A great chance to get a head start. I would love to hear your thoughts on which open source project you found the most useful. Or let me know if you want me to feature any other data science projects here or in next month's edition.

Recently I had my first job interview for a machine learning position. It didn't go well and I was rejected thirty minutes after the interview took place. I talked about some of my projects and how I improved them and issues I faces. But some of the issue didn't seem to be relevant to machine learning for ads. For example, I was detecting lane lines and choosing a good color space for the transform that would detect the lanes under low light, bright light, etc.

Computer vision, as a subfield in the general artificial intelligence (AI), is a technology can be visualized and easily found in a large number of state-of-art applications. In this project, undergraduate students performed research on a landmark recognition task using computer vision techniques. The project focused on analyzing, designing, configuring, and testing the two core components in landmark recognition: feature detection and description. The project modeled the landmark recognition system as a tour guide for visitors to the campus and evaluated the performance in the real world circumstances. By analyzing real-world data and solving problems, student's cognitive skills and critical thinking skills were sharpened. Their knowledge and understanding in mathematical modeling and data processing were also enhanced.