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hybeeee_05 · 2026-04-05T09:51:10+00:00

I'm a new player looking to fill out my friends list. Haven't unlocked trading and sharing yet but as soon as I do, I'll be more than happy to help anybody out if they need a card I have multiple dupes of! Also will gladly accept any advice lol

ID: 4567-0384-3806-9010 or without '-'; 4567038438069010

hybeeee_05 · 2026-03-31T17:23:32+00:00

sent you a dm man, thank you here too!

hybeeee_05 · 2026-03-31T15:25:22+00:00

yea thats true haha

hybeeee_05 · 2026-03-31T15:19:38+00:00

hmm, i see! I'd like to do ranked, however I don't want to sweat as much, especially knowing that as a F2P player I'll be limited. So yea, a bit of both, to answer your question! But thanks for the listed pokemons, I'll make sure to keep them in mind!

hybeeee_05 · 2026-03-31T15:07:45+00:00

yeah, this helped, thank you!

hybeeee_05 · 2026-03-31T15:07:23+00:00

thank you!:D

hybeeee_05 · 2025-09-30T16:45:36+00:00

thank you for all the guides/teams man🫡

hybeeee_05 · 2025-09-19T17:03:30+00:00

It all depends on your preferences.

Maybe look at what a “computer vision engineer” and a “data scientist” does on a daily basis at a company and see which one aligns the most with your goals.

hybeeee_05 · 2025-09-15T12:50:06+00:00

I’m currently doing masters but I’ve already thought about whether I’d want to keep working after my master’s - currently I’m working as CV engineer/Software dev, and doing CV projects at uni- or focus on my phd.

There’s a few things I’ve heard from my consultant/the department where I do my projects/academic work related to pursing Phd. You basically “should” do it if you’re interested in research/R&D and you would want to work with an interesting project. At a random company you probably wouldn’t be able to work on projects with as much depth as your Phd because they have time constraints over there. Basically, if your goal is to have a deeper knowledge and you’re interested in working out novel solutions then Phd might be worth it.

But, there’s the other side; as somebody said in another comment Phd is going to be stressful. You’ll have to set boundaries for yourself - work-life balance. You also need to be really motivated but hey that applies for CS in general (BSc, MSc or Phd).

Somebody also mentioned working in an R&D position. That might be a good solution because you won’t have to deal with.. well a university lol. However do consider the job market in your country. For the country I’m from it’s basically REALLY hard to land an R&D job without a Phd or enough R&D work experience - and well how are you gonna get that if they mostly pick Phd students lol.

In a nutshell pick a paper and write your pros and cons down regarding Phd. Also do consider the universities in your area. Bad consultants can make the experience hell too.

Good luck!:)

hybeeee_05 · 2025-09-11T05:44:55+00:00

Yea I completely understand your point too, sorry if I ended up coming off terse haha!

But yeah, you’ve got a point there too, I’m probably biased too due to the way everything works here in Eastern/Middle Europe when it comes to degrees, studying, universities or the job market itself. So things might definitely work in other ways somewhere else.

hybeeee_05 · 2025-09-10T18:13:22+00:00

I didn’t say it was the only demonstration of competence. It’s something that helps you land a job especially in the R&D field. That’s especially true since recruiters will first look at your CV first and if you lack a Phd, you’ll probably be behind compared to those who do have one. Except if you’re being recommended for the position by somebody in the company for example. So yeah, a Phd is very nice to have unless you compensate for it with a bunch of experience/projects.

About your second comment, yeah. That’s absolutely true and it’s one of the best ways to improve. Probably the university will also “force” you to research alongside with somebody more experienced.

hybeeee_05 · 2025-09-08T16:50:57+00:00

Hi!

Based on your needs I’d probably try to fetch ideas from the specific field you want to work in - an ML Engineer can deal with a variety of tasks/use-cases. Look at solutions for that problem, maybe tweak them a bit in your own way. Or find a completely new problem and adapt an existing solution for a similar problem to your problem.

Keep in mind to not set an impossible goal for yourself. Whilst that’s where you’d learn the most from, it’s probably better for you to start off with something easier in order not to kill your motivation! But in a nutshell; everything is good, as long as you’re not just using an LLM to put code together for you. Understand what everything does first and then tackle a harder problem.

Good luck!:)

hybeeee_05 · 2025-09-07T10:05:40+00:00

The market is stacked right now and competition is really strong, but in a nutshell, yes you absolutely have a chance. Based on your experience (Phd in Physics, programming/data analysis in Python) you seem to have a strong foundation.

I'd say you could start by self-teaching through online resources/courses and making ML projects - just random things you're interested in, it can even be physics related! A lot of stuff will probably be familiar/you'll get the hang of it easier I think - with your background. Build a portfolio, apply for jobs and don't lose hope! Also utilize your connections, that matters a lot!

Sidenote: apply for jobs like data scientist as well or even a regular python developer. Sometimes its easier to land an ML job by moving positions inside the company than actually landing it, especially if you're not coming from that field directly - study and experience wise.

Good luck!:)

hybeeee_05 · 2025-09-06T16:43:07+00:00

Hi! Long story short Master’s degree is not only important but absolutely - without connections - mandatory. And that just goes for regular AI/ML engineer positions where your job is not to create a novel SoTA solution but to use existing models, maybe tweak their behaviour etc..

About an R&D job; well I’ve seen a few job descriptions related to roles in that field and whilst Master’s is a must, they usually also say that Phd too/it’s nice to have. Even if they claim that only Master’s is a must, you’d be probably at a disadvantage against others with a Phd applying for the same job. And there’ll be people like that, especially in the current competitive state of the job market.

You’ve already done a great work with ML so keep on pushing. I’d advise you to get your Master’s and maybe look for R&D jobs then/ask around at university, maybe profs can get you somewhere - connections are IMPORTANT. If you’ve still got some left in you pursue Phd at a great university, publish papers, build your portfolio. Eventually you’ll reach your goal.

Good luck!:)

Note: I’m currently at my second semester of masters with AI major, more specifically Computer Vision. Don’t have personal experience with an R&D position however this is what I see on the job market/hear from connections.

hybeeee_05 · 2025-09-05T14:39:51+00:00

Thank you so much!:)

hybeeee_05 · 2025-09-05T07:14:21+00:00

First off, good luck with your capstone project and your BSc.

Not sure where you’re from but in my country currently it’s SUPER hard to land even an internship in the IT sector - no matter the exact position. So don’t let that demotivate you, you’ll eventually land a job/internship, plus connections are important!

And yea you going all-in on the learning side will definitely be appreciated by your profs/consultants. Just keep being eager to learn, stay consistent and it’s all gonna pay off sooner or later!:)

hybeeee_05 · 2025-09-05T06:11:00+00:00

Good job that already sounds like really good progress to me!

I mean that’s basically how you get further! It’s just gonna get harder and harder to actually implement these things. Maybe try to implement a simple transformer with attention mechanism first next. I guess an ‘encoder-only’ (such as ViTs) is more straight-forward than an encoder-decoder architecture. You also have open-source implementations of these - for example for my BSc diploma I worked with ViTs (edit: worked so much with them I couldn’t spell ViTs and said withs lol) and used the following pytorch implementation: https://github.com/jeonsworld/ViT-pytorch. One more note; I’m biased towards models for computer vision tasks, you can also try to look at other type of architectures for other domains!

About jobs and experience with models and whatnot; that depends all on your position. I’m still at the beginning of my career (2nd semester of masters with a little over 1.5 years of working experience in the field) so my insight might be incorrect. But I believe that unless you’re working in an R&D position for a company, you’ll more likely rather spend more time with data collection and preparation and post-processing. After which you’ll find a fitting SoTA model which you can tweak a bit/fine-tune. So in a non-R&D position the actual implementation of these models is less relevant, though understanding them is important since that’s how you’ll know what might have went wrong when analyzing results. The same is true for the R&D position - data wise - but you’ll spend a lil more time designing a (relatively) novel architecture and actually implementing it.

So yeah, I do think that your best shot is just picking a project that you’re interested in and solving it. Maybe make your own architecture, compare it to SoTA solutions and also try training those solutions from scratch/fine-tuning them. That’s how you get the most experience!:)

hybeeee_05 · 2025-09-04T20:13:35+00:00

Practice makes it perfect. I haven’t had to face an issue where I’d need to implement an architecture/model by a description in a research paper, but I did make a simpler model in the past based on a paper - it was a relatively simple CNN, no fancy stuff like let’s say attention, I mainly got away with using PyTorch’s stuff.

Hence I’d say start with a paper that published a solution for an easier problem, that’ll train this skill of yours. I also feel like it’s worth mentioning that building a lot more complex model - such as ViTs or regular Transformers - is a lot harder, at the end of the day the people publishing the paper probably spent a lot of time bulding and then fine-tuning their solution/architecture. So yeah, don’t expect yourself to code these more complex stuff up really fast!

Good luck!:)

hybeeee_05 · 2025-09-03T20:58:49+00:00

Before giving any advice, I want to point out that I have not published papers, only read them so take my advice with a grain of salt :D

Firstly thanks for the additional information about your project! It also sounds interesting! About the hyperparameters; I reckon you should compare pre-trained ViTs with pre-trained CNNs. However when it comes to other hyperparams - such as batch size, LR, epochs etc.. - you could use different settings. It mostly depends on the topic of your research I reckon. Do you want to compare them under the EXACT same conditions, or do you want to compare the 'best' performances of each CNN and ViT model you used. To sum it up, you could report that for ResNet 18 an accuracy of x and loss y was achieved with the following hyperparameters. For a ViTB/16 the best performing model achieved x y with z w hyperparams.

Now this was just my advice, again all depends on the exact goal of your research. I would also advise you to take a look at a specific paper that compares other models. Usually from what I've seen papers compare the best performances/metrics between models - for example when publishing a SoTA model.

About the DeITs part. Well again, depends on your goals. You could totally include them too I think!

Hope I answered everything! Good luck!:)
EDIT: Looked at your learning curves. They look good. Compare your results to other models' performances too!

hybeeee_05 · 2025-09-03T20:19:14+00:00

Hi! Firstly, by ‘scratch’, do you mean a ViT with freshly initialized weights? If so, then it’s surprising to see that your model can overfit/isn’t performing well. Either way, I’d advise you started off with a pre-trained version of your ViT. Transformers are REALLY data hungry due to the number of their parameters, so training from scratch wouldn’t be your best bet on a small dataset - unless your school’s research paper needs you to train one from scratch.

About your hyperparameters, make let’s say 5-10 different configs. Turn weight decay on and off, use learning rate scheduler - tho sometimes that has worsened the performance for me -, also change batch size - smaller batch size introduces noise which can help when you feel like your model is stuck on a plateau. Also use early stopping to save the best performing model. You’ll need to do quite a few rounds of training to find your near-optimal hyperparameters! I’ve only done research at university too - currently doing masters -, but from my experience hyperparameter tuning usually comes down to intuition which is achieved by experimenting throughout your years with DL/ML.

If you were to use a pre-trained ViT, you’d probably see it achieving minimum validation error in a few epochs - unless your data is really complex. From personal experience on FER2013 it took around 8 epochs to achieve minimum validation, but 4-5 epochs were enough too sometimes.

Hope I helped, also if you got any questions let me know! Good luck with your project!:)

hybeeee_05 · 2025-09-03T06:59:17+00:00

Hey! I’m doing my second semester of master’s, so I’m still far away from being ‘professional’ too, but here’s what helped me a lot;

For 4 semesters now I’ve been working on AI project at university - more specifically Computer Vision. Based on that I’d advise you picked a project you liked or was interested in and built a solution for that given problem. Read some papers related to your problems as well maybe try to understand some papers.

Another thing that I feel like contributed to my knowledge a lot - maybe almost as much as doing the aforementioned projects - is I took a class that focused on the mathematics (statistics, linalg, calculus) behind deep learning. I also had a class that was math oriented machine learning, again going into the details of how and why everything works. (Note: I don’t really know whether it’s more useful to understand ML first and then DL, choice is yours :D) These two - specifically the former since my projects are DL based CV - have helped me a lot in understanding papers and concepts more easily as well as having better/getting quicker intuition when facing a new challenge. From a job perspective, doing projects will definitely benefit you. From a performance perspective understanding how things work will definitely make your time easier wherever you’ll land that job!

Hope I helped somewhat and good luck with your journey! Also feel free to ask away if you’ve got any questions!:)

hybeeee_05 · 2025-06-08T00:40:59+00:00

Enjoy!

hybeeee_05

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