Machine learning in trading: theory, models, practice and algo-trading - page 2210
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This is an open-ended conversation. Each case is unique. The simple answer is to 'teach' backwards and compare
Turning a row is roughly like making water flow upward. You can try it, but intuitively I haven't gotten to the point of understanding it yet.
Turning a row is roughly like making water flow upward. You can try it, but intuitively I haven't grown to understand it yet.
Not a row, but a train and oos
You can't do that, because the 2 years on the right are sprouting on any manure.
It's about like finding a pattern in the Seychelles that you need a down jacket in case of an ice age.
You can't do that, as the 2 year olds on the right are spiking on any manure.
Roughly like finding a pattern in the Seychelles of needing a down jacket in case of an ice age.
So it is not a general pattern, but a local one. Then change the settings to make it work in both directions.
Thank you, I will think about it. Apparently, this is not a typical situation.
Thank you, I will think about it. Not a typical situation, apparently.
The problem is to determine this set. Next, of course, we find the power of the profitable subset of settings. And if it is large relative to the initial set, we have found it.
But the initial set to determine - it must be some kind of genetics. Not in the subject, in general.
The classic logical approach is to break down the settings by importance and power (classes like), and the combinations are as if by logic meaningful and strong. meaningful and weak, not meaningful and strong, not meaningful and weak. That's if 2 setting classes, if more then geometric or worse with the dimensionality of number and value exponent ) curse... treated with a reasonable choice.
I've figured out how you can train "minimum" neuroncu from almost any package, which is designed for classification or regression
The main thing is that the package allows you to access and modify the weights of the neural network.
The recipe is as follows.
1) train the neural network, it does not matter what, the main thing is to get a model with weights
2) choose an optimization method (genetics, muravi, swarming, annealing simulation, etc.)
3) write a fitness function
4) take the weights of the neuron and represent them as parameters for optimization
that's it!!! )))
You can train neuronka for profit, or make it create some kind of mega indicator or whatever...
I figured out how you can train a "minimum" neuron from almost any package, which is sharpened for classification or regression
The main thing is that the package gives access to the weights of the neuronka and allows you to change them.
The recipe is as follows.
1) train the neural network, it does not matter what, the main thing is to get a model with weights
2) choose an optimization method (genetics, muravi, swarming, annealing simulation, etc.)
3) write a fitness function
4) take the weights of the neuron and represent them as parameters for optimization
that's it!!! )))
You can train neuronka to make a profit, or make it create some kind of mega indicator or whatever...
If you want to train a neuronc to make a profit) it needs to learn not to plummet)))) but there's one problem: the grid stops opening positions to save deposit. I've tried it. with different methods with and without stops, the result is the same, the grid eventually decides that the best way to make a profit is to keep the deposit
the neuron from the article is firing on the real world
And how are you doing?