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Author Topic: [ANN][GNT] Golem Project - The world’s most powerful supercomputer  (Read 338772 times)
S3MKi
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February 18, 2018, 12:36:40 AM
 #2701

when brass will ready? what are doing team?

R
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February 18, 2018, 12:40:35 AM
 #2702

when brass will ready? what are doing team?

I remember learning about golemn back when I was first introduced to cryptocurrency back in mid 2017 and thought brass was just around the corner. Guess I was wrong - I'm sure it is a difficult thing to develop but really needs to be done this year to restore confidence in the project.

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February 20, 2018, 08:37:51 AM
 #2703

team has been fooling us for a very long time now, only empty promises is what we have been hearing for a very long time  Cry
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February 20, 2018, 10:36:43 AM
 #2704

when brass will ready? what are doing team?

Information from reddit: "Brass is still estimated to be released early 2018. If a more exact release date is going to be announced, it will be posted here, as well as on the blog, on twitter etc."
Who in a strong drawdown - can only wait that they will fulfill their promises. If this does not happen, they will soon be crushed by competitors.
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February 20, 2018, 10:58:26 AM
 #2705

They cant fix the date of BRASS because they are rightfully afraid of not to release. And huge dump coming after it with a big disappoint and also disbelief.
I sold my GNT and waiting for some solid development. İf this purgatory continues, the situation of this priceless token is going worse.

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February 20, 2018, 11:10:35 AM
 #2706

I think this is a situation with most of these ICOs, much promises but have delivered little, I won't go hard on Golem team for now because none of the projects in the cloud computing space have really deliver any product to the space. One thing i think will help the team is the capitalisation, they collect huge amount of Ethers during their ICO, I think the price of Ether was around $7 then and should have enough to work with
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February 20, 2018, 11:32:02 AM
 #2707

when brass will ready? what are doing team?

I am sure the whole thread is filled only with this one question and there is no answer for this from the past one year or so.I am pissed off with the team communication and feel others are pretty good in this department.


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February 20, 2018, 02:46:43 PM
 #2708


Tell me, is there a purse for storing tokens?
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February 20, 2018, 02:50:45 PM
 #2709


Tell me, is there a purse for storing tokens?
If you want a newbie friendly mobile wallet then i will suggest you to use eidoo they are very good when it comes to ui.
You can use mew if you wanna store and forget for long term.. much of news is that eidoo is launching its desktoo wallet soon.




















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February 20, 2018, 03:19:08 PM
 #2710


Tell me, is there a purse for storing tokens?
If you want a newbie friendly mobile wallet then i will suggest you to use eidoo they are very good when it comes to ui.
You can use mew if you wanna store and forget for long term.. much of news is that eidoo is launching its desktoo wallet soon.

Eidoo is going to come up with a web version too similar to myetherwallet but this is not planned for Q1 as they are currently focussed on exchange and ICO engine and the desktop looks to be having a similar app like features.


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February 21, 2018, 07:13:16 PM
 #2711

https://www.reddit.com/r/GolemProject/comments/7xh8rp/welcome_to_the_february_edition_of_golem_teams/
Brass Beta will be released at the end of March, beginning of April.
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February 21, 2018, 08:05:21 PM
 #2712

To all golem investors:

https://youtu.be/oUbpGmR1-QM?t=60
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February 22, 2018, 04:57:39 AM
 #2713

The issue here is that the team has thrown out their timeline, most of the current deadlines are not fixed, a lot  of hype was around this project then but nothing real has been delivered by the team over a year after their ICO, which is very interesting.
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February 22, 2018, 06:36:50 AM
 #2714

To all golem investors:

https://youtu.be/oUbpGmR1-QM?t=60

 Cheesy

I mean, it is kinda sad, but I cannot help myself

 Cheesy
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February 22, 2018, 08:32:20 AM
 #2715

You people need to learn to be a bit more positive. Making money in crypto is not that easy.

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February 22, 2018, 08:37:45 AM
 #2716

You people need to learn to be a bit more positive. Making money in crypto is not that easy.

The last half year it was, that's why most take it for granted.  Undecided
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February 22, 2018, 11:57:48 AM
 #2717

You people need to learn to be a bit more positive. Making money in crypto is not that easy.

Agreed, making money is not easy but when it is made can easily be done 10 to 20 times the investment.Golem is not for people thinking to make it as fast as possible returns in short term.


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February 23, 2018, 09:13:35 AM
 #2718

I am also upset by the fact Golem has failed to respect their own timeline, even amended one. However, what they do is a pioneering work and it s not easy to do something like that.

This is why I have faith and respect for the Golem team. They will deliver and the smart money will be waiting for that.

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February 23, 2018, 03:48:12 PM
 #2719

coding activity is growing so i guess we need to have bit of patience..

https://coincheckup.com/coins/golem-network-tokens/analysis/github

"between 'lives' we all have a great laugh about the parts we have performed in the 'play', and look forward to and have great fun preparing the next chapters to act out."
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February 23, 2018, 05:58:39 PM
 #2720

Teach your Golem how to learn



Ok, so here’s the thing: machine learning is taking over the world.

Like, not in the literal, skynet’ish, space-oddysey’ish, matrix’ish way. At least for now.

How then? Well, in the ordinary, economic’ish way. Data is pouring, algorithms are trained, money is being made.

And since Golem is the future of computing, we would like to be able to run ML tasks on it.

There is, however, a problem. Ordinary machine learning algorithms are, at least at this moment, not trivially parallelizable. They can be run in parallel, but for a large part of them, it is only possible with high-bandwidth, low-latency connections.¹

But, there are algorithms, which can be easily parallelized, and which are crucial in the modern machine learning workflow, even if hidden behind the scenes. I’m talking about hyperparameter optimization stuff.

“What’s that?”, you may ask. Short answer: they are algorithms for optimizing algorithms. Let’s say, we have a toy neural network, with only one layer, for which we want to set a number of hidden neurons. If we set it too high, the network will overfit, if we set it too low — it will not learn as much as it could². Since the network’s training algorithm — gradient descent — doesn’t change the structure of the network, it just optimizes parameters (weights) of the model, we have to find it in some other way. How then?

Well, we can guess. If you are an experienced machine learning engineer or researcher, the guess will probably we quite good. But, that’s obviously not the best way to do it.

Another way is to just check all possible configurations (in some range, since there is an infinite amount of them). This will produce the best possible answer, but will take too long to be practical.

We can combine these two approaches by guessing a number of configurations, based on our intuition, then try them all and select the best.

So, how do we guess them? We can base it on our intuition, we can select them randomly… or we can use one of many hyperparameter optimization algorithms that exist to do this job for us.³

Since “trying out” every configuration in practice means training the whole network with this parameter, and then checking the result, the whole procedure can be easily parallelized.

So, this is our first, proof-of-concept machine learning algorithm working on Golem. We implemented it over the summer, so you can see how it works here. And here⁴ is the full description of the ML task, with experiments justifying various implementation choices.

If you have any comments or doubts, send them to contact@golem.network and we would be happy to address them.

[1] Better algorithms are developed all the time, but we have yet to see a real distributed gradient descent. However, the prospects are looking good, for example see Lian et al.

[2] Yes, we currently control the overfitting by using other techniques, but this is only a toy example.

[3] In fact, these algorithms usually perform better than random search or even human experts. See Bergstra et al. or Snoek et al.

[4] It is one of our many research resources we made publicly available, to ensure the company and our process of developing Golem are more transparent than ever. We described it here.

https://blog.golemproject.net/teach-your-golem-how-to-learn-e32fc366a976

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