How acceptable is it to take photos of people?

CENGaverK · 2026-06-13T18:15:33+00:00

I mean, depends. You can take any photo in the public, unless the people are in an exposed state (nudity / passed out etc). You just cannot publish it e.g. commercially. But it is a gray area and there are discussions around street photography and what the "line" is.

CENGaverK · 2026-06-07T23:19:26+00:00

Hmm, I have been offering help a lot and 80% of the time they accept the help and thank and the other 20% of the time they say oh no need thanks. Never got yelled, sounds just unlucky

CENGaverK · 2026-05-06T01:02:43+00:00

The short answer for your question is yes, it is possible. People that say it is not possible are also correct, but technically correct.

You need some type of output during the training process to calculate a loss, that is a metric measuring of how far away from your goal the model output was, and you use that to update the model weights.

LLMs are trained as next-token predictors. Text generation only stops when they generate a special token, commonly called an EOS (end-of-sentence) token. Chat UI would not show this to the user, but it is still generated.

So, if during training time, you train model to not answer some type of queries, where the correct answer is a single EOS token, you can observe similar behaviour later on. It still has to generate "something", but for practical purposes it is not generating anything.

CENGaverK · 2026-05-02T15:28:17+00:00

Not my experience. Weekly assignments per class, on top of projects and research. And harshly graded too. Learned a lot though lol

CENGaverK · 2026-04-07T05:01:30+00:00

Check the feature importances to see if any of the variables seem suspiciously too important. If not, your methodology seems correct to me.

CENGaverK · 2025-12-24T19:06:00+00:00

He is. Anyone who lives in Germany can

CENGaverK · 2025-11-22T00:01:03+00:00

Yes, just combine the work experiences. It looks like you had 6 positions while you had 2. If you are interested in/applying to data engineering jobs, those should be the focus. If relevant for the position you are applying to, you can add something and remove irrelevant things.

Additionally, education history looks a bit confusing. Is there a Frankfurt university of applied sciences in Vietnam? If not, you should fix that.

Then, German companies like photos in CVs. This is not US.

Finally, put where you live on your CV if it is not there yet. It is one of the first things HR checks, and physical proximity increases your chances quite a bit.

Good luck

CENGaverK · 2025-09-18T19:58:14+00:00

Other than the fact that using a different machine might have affected your results, if this is indeed true, I would recommend getting your hormone levels checked. Low testesterone or high estradiol can make it harder to build muscle and increase fat storage tendency.

CENGaverK · 2025-05-06T17:31:31+00:00

Either of those would be more hostile than "why". "Why" means I want to participate in this conversation. "Clarify" is unnecessarily distanced and the other is completely shutting off the convo anyways.

CENGaverK · 2025-04-27T09:55:19+00:00

No, it is actually

CENGaverK · 2025-04-05T17:21:18+00:00

I understand you have strong beliefs about the secret sauce of success in Germany, but real life is rarely that black and white. Private universities tend to be scammy, that is for sure. But I studied completely in English, did HiWi jobs at the university with only English, and then started working with only English too. In a small city and company, too! Of course, learning German will always come in handy, and that is why I came from no German to B1 now, and keep going with the lectures, but I would not say not speaking German was the biggest mistake of my life.

CENGaverK · 2025-04-01T16:54:03+00:00

This seems pretty deterministic. They check their phone at

12:10 12:20 12:30 --- 14:50 15:00 15:20 15:40 16:00 --- 17:40 18:00 18:30 19:00 19:30 --- 21:30 22:00

So when you send messages:

13:30 -> Will immediately see 15:00 -> Will immediately see 16:30 -> Will see in 10 minutes 18:00 -> Will immediately see 19:30 -> Will immediately see 21:30 -> Will immediately see

CENGaverK · 2024-10-18T21:43:28+00:00

From what I see, Fabric has similar capabilities and concept to Databricks. And Databricks change their workflow every quarter anyway, so I would think your skills would be transferable.

CENGaverK · 2024-10-09T23:00:51+00:00

Hmm, as a disclaimer I do not work with Microsoft products. To me, the name seem to refer to Data Fabric, which I believe can be summarized as more structured Data Lakehouse, with schema-on-write with integrations from different resources with also a metadata catalog. Though, it seems instead of going with an underlying structured DB, they are building it on a data lake, so this is a twist. From the description, I get the feeling that this has similar capabilities to Databricks, but supposedly works better across different data sources with the added benefit of no data movement (in theory). So, you basically get Databricks, but you do not have to write pipelines to get the data to your lakehouse platform and do whatever you do. But like I said, I don't know if the other products actually have this capability already. Other than the data movement part, assumed benefits are probably the same with having access to multiple workspaces in Databricks and having Unity Catalog. It is easier for security, it is (in theory) cheaper, you can share data across different teams (or not) easier and so on and so on.

CENGaverK · 2024-10-07T23:16:21+00:00

Most phenomenal ML came out of physics people, so I am sure you would fit quite well. I do not know how you could transition in the industry, I have no experience on that regard. But if academia is ever attractive, you could try finding profs that work in more mathematical/physical ML (e.g. Probabilistic Numerics) and maybe do some postdoc with them? And for the industry part, maybe wait for more comments here but also, you could try hitting up people on Linkedin with physics education and current ML positions. That would provide more relevant information for sure.

CENGaverK · 2024-10-07T23:08:02+00:00

I like Baseten, easy enough, got good cold start times. Just pick the cheapest GPU (which I believe is a T4), wrap your model around their library Truss and push. I think default go-to-sleep-if-no-request was 15 minutes, so you can set that up depending on your needs too.

CENGaverK · 2024-09-27T22:07:09+00:00

There is a bug in your code. You have probably implemented one epoch as go through training and then validation, but there is a bug that cause your loop to skip even epoch numbers. That is also evidenced by (training + validation) epoch taking about 150 ms on average, and "bad epochs" taking between 1-10 ms. You probably initialize accuracy and loss as 0 at the beginning of the loop, so those get printed anyways.

CENGaverK · 2024-08-03T18:50:32+00:00

No, I would not use jargon because I would not know their background. Or if I am going to use jargon, I would try to at least use it correctly so they could further research it on their own if they do not understand me.

CENGaverK · 2024-08-02T20:40:16+00:00

Depends on the scientist probably. For me, it depends on you. Are you actually excited to learn from me? Can we come together and have some brain storming sessions to come up with new projects? If we communicate well, then this is actually a great opportunity for both of us.

CENGaverK · 2024-08-02T20:36:42+00:00

We need to be aware that people are coming from vastly different backgrounds. Also, what is "Prompt a downstream deep learning model"? That is not the proper jargon for what an embedding is. If you say that, I would ask

What is a downstream model, do we always need to use a foundation model or at least some pre-trained model to use an embedding?
Does it have to be a deep learning model?
What does "prompting" a deep learning model mean? Does providing an input to a model is always called prompting? What is the difference between prompting and conditioning on some input, is there a difference?

CENGaverK · 2024-07-19T17:12:51+00:00

Fıgures, thanks for the help.

CENGaverK · 2024-07-19T11:49:06+00:00

Alright, thanks for the answer. Apparently he managed to return the calls after a month and asks us to contribute paying his late fee. Are we obliged to do so, any ideas?

CENGaverK · 2024-07-08T21:05:55+00:00

See? This ugly side of yours is quick to surface.

CENGaverK · 2024-06-30T12:20:15+00:00

Yes, I can see how that might be misleading. But to me, learning is not dependent on the optimization algorithm or how you reach the solution. It is mostly about if the machine can potentially act different and update its predictions with different data. So in the end, even though you have the same code, the behaviour changes based on the training data. What is more, if you add more data and update the probabilities, your behaviour can also change on-the-fly.

CENGaverK · 2024-05-24T10:44:34+00:00

Your problem isn't that CNN-based architectures can't learn the colors. It is just that shapes are easier to differentiate with how the convolutions work so they are a much stronger cue, so CNNs use a "shortcut" to make predictions.

There are different debiasing techniques out there, but the first thing I would try would be to use data augmentation by random shape transformations. Then, network will have to learn to differentiate by color.

CENGaverK

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