Yeah I mean it's obviously an overreaction right? And a concerningly intrusive one at that- surely they could've have checked telemetry and verified at least authentic-seemimg behavior on your part. But beyond the knee jerk reaction which can be reasonable chalked up to poor governance at a startup, so, fine, it's like how'd they end up with stockfish on an undocumented dependencies blacklist? I've thought too much about this already lol. Glad you got your access restored though

Super duper sketchy. I have a pre-existing call w their sales folks tm (we're evaluating internally at the startup where I work), debating bringing this up

Wait... forreal though? Have you tried to make a fuss out of it? This seems like the kind of outcome they'd want to avoid.

Sorry for not coming here with an actual answer but why would they ban stockfish?

It's so you know not to drive at all when it's snowing out. Go nowhere, stay inside. Outdoors is too dangerous

Never heard of this Portra truck brand. Clearly don't work good

Yeah this is pretty standard stuff, idk what dude's on about

I'm a big fan of how they don't mention the product that this paper may or may not mostly underpin until the last sentence of the abstract. Like idk what exactly to glean from that fact, but I sure do like it.

Writing a deep net in numpy 10 years ago continues to prove to be one of the most valuable personal development tasks I've ever taken on. It's a lot, it's totally unnecessary, but dang does it get you learned up good. For a minute there in the beginning of my career, whenever I tried a new model/algorithm, I wouldn't use it for anything serious until after I'd implemented from "scratch" (with numpy, so not technically "scratch," but you get the idea). This was an insane amount of overhead and tons of extra work that would make zero sense for a person who's well into their career. But again gosh dang did it get things to stick.

ETA: I occasionally wish that instead of diving so deep on methods and their details, I'd spent more time building my math skills. I can keep up with most ML math, but theory papers go over my head, and I not infrequently find myself wishing I had deeper linear algebra knowledge.

My wife does pretty theoretical statistical learning work and has had success with JMLR

It's much easier to find those opportunities when you're already in an academic setting. But I don't think I or anyone here is going to give you the answer you want to here, frankly.

Well my point kind of was that you can change your GPA. Enroll in a master's, absolutely murder your coursework, and not only will your gpa improve overall, but also the improvement will come from coursework that's far more relevant to the grad school admissions process.

I might look into masters level programs before applying to another round. I think you'll find that your GPA is at least as much of a barrier to entry as your lack of research experience WRT PhD programs- at least those that are worth 5+ years of your life. Get an MS, ideally from somewhere reputable, but more importantly, crush every single task you're assigned. If that proves infeasible, perhaps reconsider.

ETA: This is a bit harsh, but why would someone want to work with you when you have so little ML exposure? I meant there are nigh on infinite ways to learn this stuff outside of coursework. I mean I do this nonsense for a living (granted, my research is very applied and very business-driven) and, big homie, I ain't got but an associates. I'd say don't be out here with this "I can't get exposure because I already graduated," that's a bad look.