4080 16 GB should be actually the 4070 TI
3090 24GB would be a better choice specially with VRAM, you can also get an used card which would be much cheaper

Good points. I'd have to agree with you that the 4090s definitely do seem to be the most cost-efficient.

Good point. I guess I'll just have to wait and see what the performance of the 6000 looks like. However, I think the decision is likely going to be just going with the 4090s. Thanks again for the insight!

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Try to get an answer using either GTP-3 chat or GPT-3 playground, at any rate it could help you refine your question.

Just using it to keep track of users honestly. Nothing much from there.

The 4080 is slightly faster, but after the 4090, the 3090 is the best bang for the buck in DL, and VRAM is invaluable, while performance is generally not.

I would go with the 3090 for the 24GB of ram. I don’t believe the performance difference will be that big of an issue. However not having enough ram will just make doing something things very difficult

👀😏

Look, honestly, I ve got fantastic results with your tool that I would consider using paid version. Shouldn't say that😁😜

First of all. If you have no idea where to start then you cannot be certain your problem can be solved with ML.

If you want a useful answer then please ask an actual question.

Didn’t suspend haha! It just reached its limit due to high volume usage so it was temporarily out. The new approach will be to rate limit the API calls made per user. Based on what I see I’ll evaluate from there.

That's wonderful! hope it will be helpful for you :) You could do proper gaming with that too, I played Cyberpunk 2077 @ 1080P with Ray tracing Ultra preset with DLSS at about 55-60 fps

What??

Is this a meme or serious? How the fuck is that possible?

The name "Chat bot" is missleading. It can really help you to program and other things. I use it a lot together with github co-pilot.

Here is an example from another guy using the 'chat bot': https://www.reddit.com/r/GPT3/comments/zalfyg/i_asked_chatgpt_to_make_me_unity_c_code_that/?sort=old

Why does it need a login

Start by reading some existing approaches: https://research.nvidia.com/publication/2021-04_appearance-driven-automatic-3d-model-simplification

When you hear a person play violin in a subway-analog. When you hear a violin played over your cell phone while on hold—digital. Biological neural systems allow for an infinite level of tuning due to their analog nature. ANNs have a limit defined by their processors.

Your example as you have literally explained it entails just deleting all repeats (ambiguous if 121 -> 12 or 121, or for that matter if 121 is possible as an input). Either way a simple for loop could solve it. But you are a self proclaimed smart person who eats for loops for breakfast, so it seems your have over simplified your description of the problem.

Machine learning is essentially the following:

• For sets X and Y, take a collection of some functions F = {f: X -> Y}
• Take a probability distribution D on X x Y. Note this could be of the form (x, g(x)) for fixed unknown function g where only x is random.
• Take a loss function L: F -> R, eg L(f) = ExpectedValue( distance(f(x),y) )
• Sample data from the distribution
• ???
• Have an f in F that is close to minimizing L

So tell us what F, X, Y, D, L are.

It's fantastic, really great. I was using other nlp tool but this is the best. But lately you suspend it due to too much traffic. Did you rethink business model or new month approaches 😜

So vectors to vectors are extremely abstract and it's hard to understand what those vectors are. ML/DL are functions at the end of the day that are learned from observing a lot of examples of vectors -> vector transformations (inductive learning).

If these transformations are fairly easy to model, you might just be able to solve the function directly. Or you might need simple ML algorithms. If it's very complex, you might need stronger models and/or more data.

For instance image -> vector of probabilities over possible categories requires some powerful models where as a vector of 1 feature (height) -> probability of not hitting the head on the roof requires a basic model.

Like mentioned in another answer, it should convert an Array of vectors into another Array of different vectors. The vectors represent a 3d model and so the general shape of the model should stay the same, just represent through less vectors.

Again, unless you describe your actual problem, it's very hard to help. ML is a vast field with a lot of different approaches that come with their tradeoffs depending on the specific problem.

Simply throwing diffusers without understanding the space is like wanting to cut a cake but using a random household object like a chair to do so.

Can you explain how I should phrase my problem more precisely? What information would you need in order to understand it better? In an abstract way my example explains precisely what it should do. If you change the numbers with vectors you have the exact case.

And so I also googled professionally beforehand and thought I'd also get the opinion of the pros without bias.

I will take a look at autocorrect, thank you :)
What I thought would be best are diffusion models, since denoising, etc. seems like the same concept.