Machine learning in trading: theory, models, practice and algo-trading - page 1747
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What the hell is going on?
What was it?) Glad they brought it back)))
I'm having trouble understanding the mathematical principle of NS.
Searching for the highest hill in the clouds, the altitude is not visible behind the clouds. Low-frequency find the beginning of elevation and survey near them, where there are no elevations do not survey. You can survey areas of the beginning of elevations and not take small areas. Kind of smart sampling. But in any case, it is an algorithm. In any case, full search with a very small probability will not lose to different options, with any logic of search, through and through, at both ends to start, the probability of finding a faster search in the search with the logic of the search is higher than in a full sequential.
I'm failing to understand the mathematical principle of NS.
You are not trying to understand it - you are trying to make it up.
To understand the mathematical foundations of NS, you should read Kolmogorov-Arnold-Heht-Nielson theory.
You are not trying to understand it - you are trying to make it up.
To understand the mathematical foundations of NS you should read the Kolmogorov - Arnold - Hecht-Nielson theory.
It is rarely explained clearly. And few people can understand it from formulas)))))
You're not trying to understand it - you're trying to make it up...
through backpropagation of invariant definition error and search for local or global extremum of neuron function by Newtonian or quasi-Newtonian optimization methods, adjusting different gradient steps
This will be clearer for Peter
Search for the highest hill in the clouds, the height is not visible behind the clouds. Low-frequency find the beginning of elevation and survey near them, where there are no elevations do not survey. You can survey areas of the beginning of elevations and not take small areas. Kind of smart sampling. But in any case, this is an algorithm. In any case, a complete search with a very small probability will not lose to different options, with any logic of search, through and through, at both ends to start, the probability of finding a faster search in the search with the logic of the desired higher than in full sequential.
ahahahah )))
Search for the highest hill in the clouds, the height is not visible behind the clouds. Low-frequency find the beginning of elevation and survey near them, where there are no elevations do not survey. You can survey areas of the beginning of elevations and not take small areas. Kind of smart sampling. But in any case, this is an algorithm. In any case, a complete search with a very small probability will not lose to different options, with any search logic, through and through, at both ends to start, the probability of finding a faster search in the search with the logic of the desired higher than in full sequential.
through backpropagation of invariant definition error and search for local or global extremum of neuron function by Newtonian or quasi-Newtonian optimization methods, adjusting different gradient steps
This is more understandable for Peter