Machine learning in trading: theory, models, practice and algo-trading - page 992
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We don't pretend to be anything, that's the funny thing, if you don't know
Is it? And if you think about it, read your posts? Well, only eggs are cooler than Maxim.
Well this is some kind of a sketch, I'm just reacting adequately without pretended tolerance.
All my questions to you were quite logical
Well this is some kind of a sketch, I'm just reacting adequately without pretended tolerance.
All my questions to you were quite logical
Instead of counting and toiling, wouldn't it be better, kuma, to turn to yourself? ))
Just in time for your message about adequacy, a good proverb
Just to the message about your adequacy, a good proverb
Yuri, once again I say - the principle is correct.
But, you know, the thing - even cool ideas do not find a response from people if there is no signal (like the passport), or it is negative, as I have now. I see it in my own example - well, no positive balance, it would seem - pick up the banner, bring the work to completion, please people. No - no one is interested.
So in this case, too.
Well, no one has "equity in the sky," or even any equity at all, and that's it - the theme immediately becomes passé and uninteresting.
Conclusion: Each subject should simply have a "man with a signal". A positive one! Then life begins.
We are waiting for this man. We hope and believe.
Hi!
Well, firstly, do not cram the NS with garbage (you don't have to cram everything you can think of).
Secondly, there are a lot of algorithms for data dimensionality reduction: PCA, Fourier transform, wavelets, etc., from new "t-sne". With their help you can make 5 inputs in ns out of 100 and almost have the same quality.
Well first of all, do not clog the NS with garbage (no need to shove everything that comes into your head).
Secondly, there are many algorithms to reduce data dimensionality "PCA", Fourier transform, wavelets, etc... of the new "t-sne". With their help you can make 5 inputs in ns out of 100 and almost have the same quality.
All of the above mentioned are not applicable for various reasons. Have you tried Tsne? My question is rhetorical.
The most suitable (in my opinion, of course) are Bayesian PCA, Autoencoder, package varbvs and of late package bounceR.
Of course there are many easier methods and all of them are described in details in articles on this site.
Good luck
All of the things you listed are not applicable for various reasons. Have you tried using Tsne? The question is rhetorical.
The most suitable (in my opinion of course) are Bayesian PCA, Autoencoder, varbvs package and of late bounceR package.
Of course there are more simple methods, all of them are described in details in articles on this site.
Good luck
Why isn't it applicable?
Why rhetorical?