Machine learning in trading: theory, models, practice and algo-trading - page 597
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Imho, of course, but here every page of the branch, you need to start with the slogan from SanSanych - "garbage in, garbage out. And all your cognitive and creative talents should first of all be aimed at reducing the garbage at the input, and only then try to put an extreme load on the computer hardware.
Are you also a fan of digging through garbage in search of edible things?
Are you also a fan of rummaging through garbage in search of edible things?
I made a non-rodynamic classifier scheme, which is easy to implement
maybe something is missing? any ideas? :)
the number of neurons can be added, now there are 2 in the hidden layer
I made a non-rodynamic classifier scheme, which is easy to implement
maybe something is missing? any ideas? :)
i can add the number of neurons, now there are 2 in a hidden layer
2) Are the weights of the neuron itself available?
3) As a medium, you can take the derivative of a close or fast MA of order 1-4. Or increments.
4) I would put the size of the hidden layer equal to the input.
Imho, of course, but here every page of the branch, you need to start with the slogan from SanSanych - "garbage in - garbage out". And all your cognitive and creative talents should first of all be aimed at reducing the garbage at the input, and only then try to extremely overload the computer hardware.
I made a non-rodynamic classifier scheme, which is easy to implement
maybe something is missing? any ideas? :)
you can add the number of neurons, now there are 2 in a hidden layer
You will only waste your time. On the real data it will not work.
For example: One wrong answer of the NS, and it will affect all subsequent ones.
This is not a slogan from SanSanych. At least google it.
That's for sure - it's a sign on the "Statistics" building.
That's for sure - it's a sign on the "Statistics" building.
Only a waste of time. It will not work on the data reaad.
For example: One wrong answer of the NS, and it will affect all subsequent ones.
Not only. There are many other places where this sign hangs).
I'm sticking to the theme, so to speak.
But in statistics, it's a matter of principle.
And it's all about correlation, because one of the basic concepts and the meanest, and all because correlation ALWAYS has a value and no value "no value = NA". If you think back to the Middle Ages, several hundred years were devoted to this - finding correlations where there can't be any in principle.
When I started to learn R, I was struck by how much this NA is carried around in it.