I just made the transition in the other direction. I did a Masters in computer vision, then worked at a startup doing computer vision and machine learning work for 2 years. I recently transitioned into app dev. There are 2 levels to ML/AI, being a researcher and being an engineer. The researcher actually creates new models, architectures, etc. You're going to need to be talented at math, as well as pursue a PhD to ha…
Thank you for your post. Two questions for you: 1) Did your Masters cover non-deep-learning vision (classical vision?) in sufficient detail? There is a ton of math in there. Going from being a shallow user of OpenCV to a deep one seems a big jump. I'm not sure a Masters focused solely on classical vision would get someone there (let alone one covering other things like ML, DL, etc.). 2) Did you end up training large…
2) I did a thesis for my Masters program, using generative adversarial networks (GANs) for image compression. It was by no means novel, or a breakthrough, but what I did learn (and this is so obvious it's painful to write this) is that you should pretty much never train from scratch and that you should always use transfer learning. As far as what I did at my last company, it was basically taking state of the art models from the MMDetection python package, fine tuning them to our use case, and then deploying them. So I wasn't really doing anything from scratch.
Happy to chat more about your specific use case if you're interested! You can email me at zbellay at gmail dot com.