Machine learning in trading: theory, models, practice and algo-trading - page 1950

[Renat Akhtyamov]

Valeriy Yastremskiy:

The main thing is not to lose face.)

That's what I'm talking about.

Learn not to lose, not to look for an entrance to the deal.

Not to lose is to earn

[Maxim Dmitrievsky]

Tag Konow:
You've been traumatized. See a psychiatrist or something. I don't want to add to your trauma. Stay out of the way.

Here we go )) shame on you.

[Mihail Marchukajtes]

Renat Akhtyamov:

That's what I'm talking about.

Learn not to lose, but not to look for an entrance to the deal.

Not to lose is to earn

This is nonsense. Not to lose is to sit on the fence, where you cannot make money. There is no point in this statement....

[Renat Akhtyamov]

Mihail Marchukajtes:
Nonsense. Not to lose is to sit on the fence, where you can't make money either. There is no sense in this statement....

Misha, with your brain, turn off the robot

not to lose is to make money

You open a trade, it goes into deficit,

The question is how to do it.

you'll figure out how to do it and turn the robot on.

You can't do it by hand.

this is the grail

[Mihail Marchukajtes]

Renat Akhtyamov:
Misha, with your brain, turn off the robot

I went through this a long time ago, it doesn't help..... In the sense of the phrase "The main thing is not to lose". This is self-deception....

[Mihail Marchukajtes]

Eh Ma, so I think to go more video to record now on the market itself? In order to dispel a number of misconceptions among those gathered....

[Valeriy Yastremskiy]

Mihail Marchukajtes:
"The main thing is not to lose. It's self-deception....

Who would argue) but it's not about the face)

[mytarmailS]

elibrarius:

So, have you tried reducing anything?

[Forester]

mytarmailS:

So, have you tried reducing anything?

No.
Summer. Lots of other things to do. I'll experiment in the future when I have time.

[Rorschach]

Rorschach:

I want to try to pull the values of input layer weights, I need to look how.

For a network with 1 neuron and 100 inputs. On the left all inputs, in the center the last 10. On the right 10 neurons 100 inputs.

The same network is heavily over-trained.

Weights for the grid to intersect with ma(100) 100 inputs on the left, ma(50) 100 inputs on the right.