3-Minute Read

485 words

The models nowadays are becoming larger, especially language models. Search engines need to provide real-time results, and models on mobile devices have limited memory and computational power, requiring model lightweighting. ONNX, developed by Microsoft, is a cross-platform machine learning framework that can convert models from various frameworks (such as PyTorch, TensorFlow, etc.) into the ONNX format and perform lightweighting. This article takes a PyTorch model as an example, using ONNX to…

4-Minute Read

779 words

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…

7-Minute Read

1335 words

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.

4-Minute Read

646 words

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.

2-Minute Read

384 words

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?

Recent Posts

Categories

Tags