Machine learning in trading: theory, models, practice and algo-trading - page 3715
You are missing trading opportunities:
- Free trading apps
- Over 8,000 signals for copying
- Economic news for exploring financial markets
Registration
Log in
You agree to website policy and terms of use
If you do not have an account, please register
and now you can do that? ;-) some people have had "iron nails" for the other one.
or does it mean some other account, different from some other account that was previously shown in the screenshots?
Altman in an interview with The Verge recognised a bubble in the AI market.
Altman, in an interview with The Verge, acknowledged a bubble in the AI market.
maxon, is there an EA without python, working on moe? you know, to make it simple and practical ok
Shalom! Of course there is.
Shalom! Of course there is.
Where do you get it?
A small portion of realism about AI from one of its creators. The translation is not very good, but the meaning is conveyed. The (extremist) Meta adverts are a bit annoying, but not enough to drown out the main theme.
The basic idea is that the current AI is pretty good at "understanding" language (predicting words) because of its discrete nature. It does worse with reality video because the physics of reality is continuous, not discrete.
Imho, it is worth trying to revitalise the thread - it was quite good.
But I want something new, the old topics on MO have already become a bit boring.
Such a new topic could be probabilistic machine learning. This is an approach in which the output of an algorithm is not a specific numerical value, but its probability distribution. Imho, this approach is more organically suited to the task of trading.
I will write about this topic as I am in the mood.
PS. I have nothing against if someone will write in the thread on any other topics related to MO.
probabilistic machine learning
In the context of familiar MO approaches (classification and regression), probabilistic MO is a generalisation of them.
1) Classification - the output of the algorithm is a discrete distribution of the class number.
2) Regression - the output of the algorithm is a normal distribution with a mean dependent on the inputs (perceived as a prediction) and a fixed variance independent of the inputs (perceived as a prediction error).