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[D] Learning Resources for Intermediate Machine Learning

I’m currently 9 months into my MASc with my thesis focusing on computer vision and human movement analysis with deep learning. Right now I’d say that I’m at an intermediate level in terms of my ML knowledge. I’ve completed two university courses so far on ML/DL, Andrew Ng’s course, as well as a couple other online courses and readings. I also have taken a pattern classification course that gave me a pretty good background on statistics and its relations with ML from linear regression to HMMs.

I was wondering if anyone knows of any good resources that I can turn to now. Specifically in the area of computer vision or DL would be useful. I find that many websites, like towardsdatascience, end up being the same basics that I’ve seen many times. I’m open to any types of resources really: textbooks, papers, youtube videos, etc.

Also, I was wondering if anyone has experience with auditing classes (just sitting in and listening) during their MASc or PhD. Is it worthwhile?

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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.