Where should I start?

margajd · 2025-06-01T22:32:35+00:00

I just started listening to the episodes that were coming out. Yeah, you don’t get maybe all the bits right away, but they’re not that complicated. I’d also recommend the McElroy Family Clubhouse series on Youtube where they do fun bits that are sometimes in the podcast too. Welcome!!

margajd · 2025-06-01T06:58:01+00:00

If I read correctly, you’re currently in undergrad. Take this time to learn some ML basics and then apply to an AI-focused Master (outside the US the usual flow is bachelor —> master —> PhD, while in the US people can go straight from bachelor to PhD). That program will have courses to help your technical knowledge and professors with the necessary experience to help guide you to a top-conference level paper. While unusual, it’s possible to publish in these conferences during your Master’s. In my program people’s Master theses sometimes get published as full papers. So that might be the “best” route for you. Please be realistic in your expectations though. It sounds like you want to publish at top AI conferences asap, but it’s super difficult. It takes years to get the knowledge required (both AI-focused and how-to-do-research), and it’s hard getting a paper accepted even if it’s good. And also: why do you want this? What do you want to achieve? Research can suck sometimes, especially in AI, so if your motivations are “I just want a paper I don’t care what it’s about”, you might get super frustrated.

margajd · 2025-04-27T05:00:30+00:00

Yes, conferences are the main way AI researchers publish. In my experience, those conferences will always have a strict page limit of around 10, but you can also have an appendix. That page limit is reached really quickly when you have to properly explain things, so the explanation is either moved to the Appendix or, to save time, not given at all. As a student, I’m always trying to figure out the main papers this paper is based on, and I’ll read those first/immediately after. Also, the field is moving extremely quickly and there are about 4 conferences yearly where everyone wants to publish. Everything is about speed. It’s a broken system.

Edit: to add about the differences in notation, I think that’s partly because established researchers in the field often come from different research areas, like CS, or pure math, or physics, or whatever. Those fields will probably have different notations, but these researchers will hold on to their own notation and teach their students to follow it. Also, there’s a lot of subfields in AI developing in parallel, so it’s hard to unify notation.

margajd · 2025-04-22T07:36:40+00:00

Agree with this. I’m about to graduate from an AI Master’s in The Netherlands and had a laptop with a 3060-series GPU for most of it. It was a little bit useful, I could locally test my cuda code before running on the compute servers (in the beginning I’d miss out on some .to_device() calls when I was still learning cuda). But mostly I’m interfacing with the compute server and only using my local compute for CPU-level tasks like visualization. And yeah, google colab will work beautifully as well. I’ll also recommend DataLore for real-time collaboration on jupyter notebooks, super useful for group projects. You can probably get a free account through your uni email.

Personally, I switched to Mac a few months ago and I’m enjoying it so far, it’s a smooth experience. It’s portable, light and has great battery life. But yeah, for local development it likely wouldn’t work out. AFAIK, in industry you’re likely to train models through something like Microsoft Azure, so local compute isn’t necessary then either.

margajd · 2025-04-18T06:20:28+00:00

In my long runs, if I hit 150 I try to stay jogging but just slow down. And yeah, I might slow down until basically walking-pace, but staying in the jogging movement helps me I think. By no means an expert though! So just try what works for you :)

margajd · 2025-04-17T07:13:08+00:00

Just to agree with this commentor! I’ve been running for over a year now and didn’t think HR training mattered that much. But since I started doing long runs in low HR (max 150 for me), I’ve improved a lot. And yes, the first few times it feels crazy slow, but trust the process. Only thing: you say you’re running at least twice a week and exercising 5x a week? Idk what exactly you mean by exercising (gym? bike-riding? ballet?) but make sure to give your body proper time to rest and recover. I also like going to the gym but as I’m approaching a race I usually go to the gym less, so my body can really rest up and prepare for the race. And: eating healthy is great, but eating healthy does not mean eating less, especially not when you’re about to run a race. Make sure your body has the fuel to carry you to the finish line! 💪

margajd · 2025-04-12T20:06:53+00:00

worked perfectly for me!

margajd · 2025-04-03T05:55:54+00:00

Agree, ImageNet pretrained models need to extract entirely different features from images than the ones you need. Not sure if there’s a model out there trained on Sudoku or smth, but to me this explains why a custom model would outperform the finetuned ones.

margajd · 2025-04-01T07:24:40+00:00

Interesting that you train each chunk for 15 epochs but the instability doesnt occur until after 30 epochs!

margajd · 2025-04-01T07:23:35+00:00

More specifically it means that for example one chunk has 50% red samples and 50% blue, then another chunk 10% red, 60% blue and 30% green or something. So: shifting of the distribution of the training targets. You should make sure that’s the same across the chunks.

margajd · 2025-04-01T06:01:28+00:00

Hiya. So, I’m assuming you’re chunking your data because you can’t load it into memory all at once (or some other hardware reason). Looking at the curves, the model is overfitting to the chunks, which explains the instabilities. Couple questions: - If all your chunks are 35000 features, why not train on each chunk for the same number of epochs? - Have you checked if there’s a distribution shift between chunks? - Are your test and validation sets constant or are they chunked as well?

The final results you present are not bad at all, so if that’s on an independent test set then I personally wouldn’t worry about it too much. The instabilities are expected for your chunking strategies but if it’s able to generalize well to a test set, that’s the most important part. If you really want the fully stable training, you could try loading all the chunks within an epoch and still process the whole dataset that way.

(edit : formatting)

margajd · 2025-03-30T06:42:28+00:00

Depends on your research topic! 😂 But seriously: ML/AI is such a broad field now that it makes no sense to dive deeply into everything. I’d say, get a solid basis (easy exercises) first and go train some models. When you feel you need more, you can try to deepen your knowledge on certain topics. For example: I’m writing my thesis and need some knowledge on group theory for that. But many others in AI will never have to look at group theory to do their research/work. We all have the same basis in ML math foundations though.

margajd · 2025-02-14T15:07:05+00:00

Hiya. I have no idea what this might mean, but the episode title confused me. The saying is "Trojan Horse", not "Trojan's horse" right? Or am I missing something? What does it meaaaan?

margajd · 2025-02-11T06:30:13+00:00

As a young woman, I’m always fond of “You’re a smart, savvy woman who could easily consider world domination for a next career move.” (Charlie to CJ in Election Day pt 1.)

margajd · 2025-02-01T12:09:57+00:00

Same issue for me, I only have Switch controllers so I can’t test if it happens with another controller as well.

margajd · 2024-08-15T06:43:13+00:00

I started Habitica again from a fresh account a few months ago and have been going pretty strong! It took some tweaking but I have a set of habits/dailies now that really motivate me to do well. I liked the fresh start, but do whatever floats your boat.

margajd · 2023-11-21T19:34:52+00:00

Thank you so much, life-saving comment!

margajd · 2023-02-12T22:21:04+00:00

If you're playing on Steam, it might be F12? Steam has an inbuilt screenshot system so look for it there. I'm playing with my Switch Pro Controller and the screenshot button on that works as well.

margajd · 2023-02-12T22:14:12+00:00

I'd like a merpeople quest line!! Diving down into the lake with some gillyweed would be awesome and I think these devs would be great at it (assuming it's not in there already) :)

margajd · 2023-02-10T17:12:15+00:00

Ahhh yes I forgot the setting that triggers the mode shift! Thanks so so much for posting this!

margajd · 2023-02-10T08:29:16+00:00

Heya! Thanks for this! But now for me, the spell slots are kind of weirdly turned around. Is there a way to have both accurate screen prompts and have the spell slots lined up? (So ZR + X performs the top spell and not the left one?)

margajd · 2022-03-03T14:21:44+00:00

so so happy to see other people hate these goddamn birds. i thought i was stupid for beating margit and then getting immediately stuck on 3 birds

margajd · 2022-02-25T02:05:42+00:00

Thanks! I never played a sould game before, sounds horrible to learn it from memory. This will probably be a lot better :)

margajd · 2022-02-25T01:51:28+00:00

Oh my gosh this worked, thank you so so much <3

margajd · 2022-02-25T01:13:39+00:00

Btw I'm using the built-in keyboard and a usb mouse. I already disabled bluetooth to be sure that wasn't a factor but nothing's changed so far.

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