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[D] Specific tips on Machine Learning research in a PhD

I am a new Machine Learning PhD and my topic is roughly vision, i.e. semantic/instance segmentation, and to be honest I am a little lost.

How exactly, specifically do you conduct research in this field? How does the day to day work look like?

  • Do you think of new NN architectures and test them experimentally?
  • Do you download others models and just try them out with own datasets?
  • How do you keep track on different architectures, papers, etc. Maybe make an excel document with all the papers you’ve read with a short summary?

I would be really interested in how the day to day work of other researchers in the field looks like and what specific tips you might have.

submitted by /u/schrowawey
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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.