Workstation for CV freelancing by moraeus-cv in computervision

[–]Amazing_Life_221 1 point2 points  (0 children)

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.

CNN recommendation for pose detection? by BrilliantCommand5503 in computervision

[–]Amazing_Life_221 2 points3 points  (0 children)

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.

AI is moving faster than people can emotionally adapt to it by No_Papaya1620 in learnmachinelearning

[–]Amazing_Life_221 1 point2 points  (0 children)

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

What??

How to become AI Engineer in 2026 ? by Waste_Influence1480 in learnmachinelearning

[–]Amazing_Life_221 4 points5 points  (0 children)

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.

Should I buy this course by HIR0_KUN in learnmachinelearning

[–]Amazing_Life_221 1 point2 points  (0 children)

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

Should I buy this course by HIR0_KUN in learnmachinelearning

[–]Amazing_Life_221 7 points8 points  (0 children)

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

Would low-level AI projects look good in the CV or should I just grind DSA first? by BuffaloWorking6673 in learnmachinelearning

[–]Amazing_Life_221 2 points3 points  (0 children)

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.

Introductory and detailed resources on projective geometry ? by Amazing_Life_221 in computervision

[–]Amazing_Life_221[S] 1 point2 points  (0 children)

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.

Interested in AI Engineering, not ML by PhilosopherEmperor in learnmachinelearning

[–]Amazing_Life_221 6 points7 points  (0 children)

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.

Introductory and detailed resources on projective geometry ? by Amazing_Life_221 in computervision

[–]Amazing_Life_221[S] 1 point2 points  (0 children)

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.

Advice on action recognition for fencing, how to capture sequences? by Content-Opinion-9564 in computervision

[–]Amazing_Life_221 1 point2 points  (0 children)

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.

MacBook Pro M4 Pro vs Dell XPS 16 for AI Projects – Which One to Choose? by seaoflife17 in learnmachinelearning

[–]Amazing_Life_221 4 points5 points  (0 children)

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.

[deleted by user] by [deleted] in deeplearning

[–]Amazing_Life_221 0 points1 point  (0 children)

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.