+1 to this from someone who learned the math behind ML in a PhD and was looking forward to being a gatekeeper :) My favorite academic paper ever [0] was a comp

+1 to this from someone who learned the math behind ML in a PhD and was looking forward to being a gatekeeper :) My favorite academic paper ever [0] was a comparison against a bunch of dimensionality reduction algorithms and 100 year old PCA was tough to beat! Glad I was able to pivot my career out of AI and ML. My PhD wasn't at Stanford, MIT, et al so I couldn't find any jobs doing the "actual research" - if they existed at all outside academia. EDIT to add another funny "frustration" paper more directly related to ML [1]. I consider DR is more of a data analysis thing. [0]: van der Maaten, et al. Dimensionality Reduction: A Comparative Review https://members.loria.fr/moberger/Enseignement/AVR/Exposes/TR_Dimensiereductie.pdf https://members.loria.fr/moberger/Enseignement/AVR/Exposes/T... [1]: Dacrema, et al. Are We Really Making Much Progress? A Worrying Analysis of Recent Neural Recommendation Approaches https://arxiv.org/pdf/1907.06902.pdf https://arxiv.org/pdf/1907.06902.pdf