Machine learning in trading: theory, models, practice and algo-trading - page 3474
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I played the second Fallout completely on a pentium3, divine game, I still remember it fondly to this day
:)
:)
And now reality is moving in on this game.
Yeah, it's very similar.
Who needs interactivity in R charts, here is a small example of the construction.
you can shrink, zoom, scroll, move, etc...
may be useful for some people
The points for interactive interaction are circled, because I did not immediately understand it myself. I was thinking of making a tester with visualisation.
Even on a weekend labour? )
there's a Fallout TV series based on the iconic game.
I watched two episodes at 1.5 speed. The plot is similar to the third folaut, but polorolevye models shuffled, which is acceptable for the entourage, but I compare it with the game and there is trouble. The series is better to watch without playing the game, but then it will be incomprehensible a lot of lore. We need to listen to the opinion of those who have not played the game.
The excess of computer graphics strains the eye. Less blood and violence, and it would be more fun to watch. Overall the scenery isn't bad. Should have dropped some of the conventions of a computer game - such as instant healing - and it would have been more immersive.
In continuation of the same thoughts as in the link.
Well, well, I'll look at the behaviour of quantum segments on all three samples (initial selection only on sample train), let's imagine that we have learned to detect them, what then - the same graphs, the same 100 iterations.
Hm, it seems that the probability of choosing the correct quantum segment looks more stable for both classes, except that at the end of the first class there is a surge to 100%, but this is against the background of a significant drop in the assortment. I assume that an error in quantum cut selection leads to a deterioration of the probability of choosing a good quantum cut at a new iteration, and several successful selections are required to rectify the situation.
At any rate, one can know for sure that there are patterns in the sample train, potentially allowing one to make money on all three choices.
But this could also be the balance in points (0.00001) on the exam sample after the last iteration.
I've watched two episodes at 1.5 speed. In terms of plot it looks remotely like the third folout, but gender role models shuffled, which is acceptable in terms of entourage, but I compare it with the game and there is trouble. The series is better to watch without playing the game, but then it will be incomprehensible a lot of lore. Need to listen to the opinion of those who have not played the game.
The excess of computer graphics strains the eye. Less blood and violence, and it would be more fun to watch. Overall the scenery isn't bad. Should have dropped some of the conventions of a computer game - such as instant healing - and it would have been more immersive.
I haven't played much, just the 4th one for a bit. But the atmosphere is cool.
Didn't know what the thumbs up and characters meant, found out through the film :)
Then you have to check on another sample of Exam2, if Exam is involved in the training )
The point is that you can get such a model randomly, and then it will be your choice, which will be effective over a long period of time. Including in the same way get EA settings in the terminal through optimisation, adjusting on history and testing on an independent sample. And I want to look in the direction of increasing the probability of choosing the right split during the training of the whole model, and here the standard principle of more-better is not suitable. After finding an effective method, we can abandon exam and test samples, and either improve the number of examples in the sample, or bring the training closer to the date of trade start.
This sounds quite interesting :CRPS-based online learning for nonlinear probabilistic forecast combination https://www.sciencedirect.com/science/article/abs/pii/S0169207023001371
https://cran.rstudio.com/web/packages/profoc/index.html https://profoc.berrisch.biz/