I think you need to research the basics of ai hardware. Laptops are almost all worthless, with the exceptions being specially designed laptops, which still only provide mediocre performance.

I don’t have much space in my home office. Shall I still for a desktop?

There's no such thing as future proof in ai. Focus on present proofing

Literally none of them

This reminds me of when I was in grade school and we had a sub for the day because my teacher was “taking a class on how to use the World Wide Web”

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Hello, thanks for your reply. So... N(Xt ; ... , BtI) doesn't mean that the V(Xt) = Bt ?

Please just remove the question. Basically your stack overflow question is asking to debug your code. No general principles

yes, you can see my problem here https://stackoverflow.com/questions/75672909/why-by-adding-additional-information-as-number-of-sequence-on-dataset-can-avoid

Heres one paper that I can immediately think of, https://arxiv.org/abs/1409.7495. The authors use a synthetic dataset to select and enginer features of a “real” dataset. Not sure if this is what you are looking for but could be a step in the right direction.

Have you done any research at all? What did you find so far?

It's a degenerate case, not something anyone should do. If you include Y in your input, then overfitting will lead to the best generalisation. This shows that the input does affect overfitting. In fact, the more similar the input is to the output, the simpler the model can be and thus the less it can overfit.

I’m not sure I understand what your method does. If Y is the output, then you say I should also include Y as an input? And if I manage to design my model so it doesn’t just select the Y input, then I’m not overfitting? This makes sense that it doesn’t overfit, but doesn’t it also mean I am dumbing-down my model? Don’t I want my model to preferentially select features that are most similar to the output?

slow and steady wins the race tho amirite

You can ask chatgpt for this type of questions

because I've read from some paper, they saying FS and FE is different

Well selection is part of engineering, is it not?

I think feature selection and feature engineering are different

https://sciendo.com/article/10.2478/cait-2019-0001

Can you provide a reference that states that feature engineering can address overfitting?

Yes, if the features include the model's target output. Then, the overfitting would result in the model outputting that feature as is. Of course this is a useless solution, but the more similar the features are to the output, the less overfitting will be a problem and the less data you would need to generalise.

Posting assignment questions to reddit.

Udemy course: Learn chatgpt

First of all, beta_t is just some predefined variance schedule (in literature often linear interpolated between 1e-2 and 1e-4) and it defines the variance of the noise that is added at step t. What you have in (1) is the variance of sample x_t which does not have to be beta_t.

What does hold for large t is var(x_t)=1 as our sample converges to ~ Normal Gaussian with mean 0 and var 1.

Bro ask chat gpt