On most dating apps, swiping right on a profile means sending a match request, while swiping left skips the profile. Once both users swipe right, they can start messaging each other. There’s a common belief that since most heterosexual dating apps have more men than women, the best strategy for men is to swipe right on every profile and see who matches. However, as a data scientist, I’d like to explain why this might not be the most effective approach.

The productization of ChatGPT has set off another wave of AI fever in the world. This time, unlike AlphaGo, everyone can register for an account to play. In addition to text-generating AI, there is also image-generating AI that produces images that are indistinguishable from human works, and anyone can try it out for free. Even people who don’t know coding can easily use them, which will definitely affect the work of machine learning engineers, but I think machine learning engineers can…

I have 6 years working experience as a machine learning engineer (though job title not exact “machine learning engineer”, but the responsibility is), started this role before the deep learning boom. My job is translate business problems into machine learning solvable problems and productionize the models with backend engineers. Sometimes, I also need to develop the data pipeline. I will share about the math that machine learning engineers need in this article.

Self-supervised learning is a very popular topic that has gained attention recently, and I personally believe that it is a very promising field in machine learning. This article will introduce what self-supervised learning is and its related applications.

As the world’s largest machine learning competition platform, Kaggle always has ongoing competitions with prizes. More importantly, if you are looking for a machine learning or data science-related job, achieving good results on Kaggle can significantly enhance your resume.

Someone asked this question on PTT (in Chinese). He trained a rectal cancer detection model on MRI images with 5 fold cross validation, but out-of-fold AUC were less than 0.5 in every folds. After some searched on Internet, he found someone said: oh, if you reverse the label (switch class 0 and 1), than you can get AUC better than 0.5, your model still learnt something. In my humble opinion, it is very dangerous to reverse label on a worst than random model. So, how to solve it?