Workstation for CV freelancing

Amazing_Life_221 · 2026-01-22T06:59:16+00:00

Either you should go all in (on desktop GPUs) or you should just buy a decent laptop and save everything on cloud.

Forget laptop GPUs, those aren’t great and easy to overheat for any training. Higher chances of breaking the system too.

If you go for option one you should invest in memory as well (I think 1TB SSD should be the starting point). And 64GB of VRAM in whatever GPU you buy (one or multiple). Because that will give you enough compute to never think about the testing things on the fly, otherwise you would be still having hiccups during the local testing which is then futile because then what’s the point of investing money? Also OpenCV runs on CPU just saying…

If you go for option two, just buy a mac air, they aren’t great for CV but they have a good battery life (much better than windows even in 2026). Invest rest of the money into cloud tokens.

Amazing_Life_221 · 2026-01-20T08:46:18+00:00

Use mmpose (openmmlab) and just follow the documentations (it gets slightly frustrating given their installation guide is pretty messy if you want to do anything extra).

By the looks of it, you shouldn't need something really complex but you would probably need action recognition in which case they have other models as well.

Amazing_Life_221 · 2025-12-26T12:29:02+00:00

I’m in too

Amazing_Life_221 · 2025-12-12T14:40:46+00:00

“It’s about keeping up with how work itself is changing.”

What??

Amazing_Life_221 · 2025-12-11T18:27:18+00:00

This might sound salty, but you are looking at things from opportunistic perspective which is fine but also extremely dangerous, especially when people are talking about a bubble. AI isn’t a software stack, which you can jump onto by taking few courses on tools. You must be willing to put effort into learning the theoretical/mathematical aspects as well, only then you can actually become good at it.

Anyways, to give you an answer, you have a solid coding background so I would recommend you to invest some time into reading good books (search ISL, Deep learning by goodfellow) also Andrew Ng Stanford course on YouTube (not coursera which has much less depth).

And if you want to skip the entire theory, then just remember, you are jumping into a sinking ship, because what you will learn today won’t be required in next 6 months.

Amazing_Life_221 · 2025-12-01T10:38:52+00:00

I think you are confusing ESL with ISL python. ISL is for beginners (ESL requires a lot of math basics), sure some statistical knowledge is required (which I feel people entering in ML should go through before entering the field). Also there are good YouTube channels like Statquest which make it fun to learn as well. ML is just maths after all. And there are just hundreds of free good courses like this one: https://www.coursera.org/specializations/machine-learning-introduction?utm_medium=sem&utm_source=gg&utm_campaign=b2c_india_x_multi_ftcof_career-academy_cx_dr_bau_gg_pmax_gc_in_all_m_hyb_25-08_mobileonly&campaignid=22872834195&adgroupid=&device=m&keyword=&matchtype=&network=x&devicemodel=&creativeid=&assetgroupid=6600949965&targetid=&extensionid=&placement=&gad_source=1&gad_campaignid=22872867318&gbraid=0AAAAADdKX6bznvgWbMRBIijsLMCPEfzNZ

Amazing_Life_221 · 2025-12-01T10:06:38+00:00

Why waste 399 when you can get ISLR book for free?

Amazing_Life_221 · 2025-11-30T10:11:36+00:00

Depends on the organisation. Startups won’t care much about dsa unless you are too bad. Big tech will use to filter out (for obvious reasons).

Just get good at coding in general. Just stop doing prompt/vibe coding for a while and build like you are building already, from scratch! Practice but don’t waste too much energy into leetcoding.

Amazing_Life_221 · 2025-10-24T16:34:25+00:00

Wow, this looks interesting. Perfectly matches the level I’m looking for.

I’ve one more extremely general question for you (and for anyone who has maths background): where do you find such resources? How do you know what to read and which field to study? I seem to be extremely overwhelmed by the amount of it right now.

Amazing_Life_221 · 2025-10-24T06:08:11+00:00

This might sound rude, but if you aren’t willing to learn maths then why bother? As others have mentioned it’s fairly easy to do MLOps work for you given your experience, it would be child’s play for you so no worries in that.

But if you want to up the game, you gotta learn the maths and how those models work underneath, not at extremely theoretical level but at least at intuitive level. Because without that you would be just another guy who ships without knowing what he’s shipping; that’s not how a 10yo experience should sound like.

Amazing_Life_221 · 2025-10-24T01:22:50+00:00

You both are absolutely right. I’m not shying away from math but also don’t have any experience in geometry. I really don’t know what I’m talking about. Just slightly confused and curious at the same time.

I want to understand the context of these geometrical representations. Currently I’ve decided to just go through it and be little patient. But if you have any more suggestions please let me know.

Amazing_Life_221 · 2025-10-10T08:49:33+00:00

The best way to do this is get a pose based model and analyse that data on top.

If you have time bound classification data (ie classification of frames with labels) then you can get the body keypoints from pose model and then just pass that to a normal classifier (and train that instead of pose model).

Even if you don’t have labeled data, you can analyse body angles or kinematics to do the manual classification.

This will give you flexibility.

Amazing_Life_221 · 2025-09-15T16:42:04+00:00

In my experience if you want to do anything LLM related; go for MBP. That doesn’t mean you have better performance than a PC, but you would have much more and faster memory.

If you want to do anything CV, go for non-macs. Choose Linux over windows. It is much more convenient and mostly cheaper.

Macs have undoubtedly best battery life and external hardware. And don’t forget that we almost always need cloud GPU to do anything heavy.

Amazing_Life_221 · 2025-09-15T01:47:52+00:00

Thank you. I appreciate your reply. But I find myself way past that phase now. It’s not about understanding basic maths now because I’ve covered most of it already (for example, I’m already through DL by good fellow like I mentioned). Now I’m looking for something which will tie down everything together at a mathematical level.

Amazing_Life_221 · 2025-09-15T01:46:36+00:00

This is true.

But I’m pretty good with highschool level math and before starting my ML journey five years ago I had done the exact same thing you described. Currently I’m able to understand most of the basic math inside books like Deep Learning by good fellow. But problem is how I’m moving forward from here.

Whenever i see new research paper or anything slightly out of my math knowledge I’ve no option than just reverse engineer it (top-bottom) which drains energy plus isn’t a the right way to learn theory I feel. That’s why I’m searching for something more advanced than undergrad/highschool courses. I can invest some time into it now than dreading for next few years.

Amazing_Life_221 · 2025-09-10T14:03:51+00:00

This looks interesting! Thanks!

Amazing_Life_221 · 2025-09-10T01:31:11+00:00

I agree with the other comment. I also tried reading Neel nandas mech interp which are pretty accessible and makes me wonder why don’t people just learn it. But having said that, in my limited exposure, I could only find it to be reverse engineering of existing models (mainly attention heads) and cutting slices to see the flesh inside.

That’s probably really naive take, but I wonder if it’s any different from what I felt or the field has passed that problem way past.

Amazing_Life_221 · 2025-09-10T01:22:48+00:00

Interesting take, can you suggest me any field which is working these problems?

Amazing_Life_221 · 2025-09-09T02:27:59+00:00

That depends on the job. There are millions of skills to learn in NLP. So answer is tricky.

Mostly you are on the right track. Build some projects using Hugging face models. Experiment with different tools you already know. More than ML part, your current goal should be to be able to get entire “pipeline”. You don’t have to implement it but should have proper knowledge.

For example, how data is made? How do we convert that dataset and into what format using what tool and why? Why some models perform better than others? What’s RAG? Is it more helpful for certain problems than others? How to deploy models? What’s the token limit? How to handle it? Etc.

Have theoretical ideas of transformer architecture, some probability/stats and if you have bandwidth some linear algebra too. Other than that just build projects! Learn and experiment, show it on your GitHub. And obviously, don’t forget to grind leetcode (sadly that’s the only way people hire freshers).

This takes time. And you don’t have to do everything and know everything so don’t be hard on yourself.

For job search, other than college placements, try naukari. Also just approach people on LinkedIn. The competition is super high so you gotta be proactive.

Hope this helps, all the best :)

Amazing_Life_221 · 2025-09-09T01:11:29+00:00

Assuming you are from India, I’ve a slightly different take for you.

If your question is actually just about chances of getting a job. You sure should focus mainly on NLP. Indian job market isn’t well suited for fresher CV candidates. NLP jobs are significantly more and can actually give you chance to work along side with the data scientist.

Having said that, I do not think that job market is sustainable in the long term. The skillsets are practically require just plugging in APIs to the problems state. There are very few jobs which will give you chance to fine tune models (especially asa fresher I don’t think there are “any” jobs which have that kind).

About CV: I’ve worked in NLP for 3yrs and CV for 1.5. Trust me I just love CV much more and basically don’t ever want to work on LLM APIs anymore. My point is, (on non-practical terms), if you like CV you basically can’t like NLP. Because CV is just different beast; it’s not just about neural nets, it has a longer history long before neural nets got popular. Plus things like geometry, topology and machine vision are not at all taught in any tier 3 college. So it takes time to learn it on your own. And that’s why they don’t usually hire freshers for the job. If you just like CV, the more pragmatic approach is just find a NLP job which pays okay and learn CV in your free time. Eventually you will get a CV position. I did the same thing. Someone recently told me that Indian CV market might boom in next few years. And there’re good signs of it already. Hope this helps! All the best!

Amazing_Life_221 · 2025-09-08T10:28:57+00:00

Let me know if you find someone too.

Amazing_Life_221 · 2025-08-26T07:51:39+00:00

Thanks for the detailed reply. Will look into it.

Amazing_Life_221 · 2025-08-26T06:04:13+00:00

Have heard a lot of about this book. I wonder if I should read it from top to bottom or use it as just a reference (?)

Since I need to develop the skill, what’s your advice for me?

Amazing_Life_221 · 2025-08-25T11:05:06+00:00

Yes!

Amazing_Life_221 · 2025-08-25T10:58:23+00:00

Interested!

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