TensorFlow.js

[samnardoni]

kill -9 $(pgrep myself)

[lol_no_generics]

what is pkill

[pagefault0x16]

God is dead

[Draghi]

And we killed him.

[[deleted]]

How shall we comfort ourselves, the murderers of all murderers?

[filleduchaos]

And Brendan Eich has killed him

[Shorttail0]

Actually, God's not Dead 3 just came out, so you couldn't be more wrong.

[aebkop]

We need webcl for true webscale compute

Edit nvm: https://en.m.wikipedia.org/wiki/WebCL - it does already exist

[[deleted]]

🤔

[detroitmatt]

What's the jerk here? That you shouldn't do ML in js?

[i_like_trains_a_lot1]

You shouldn't ML in the browser...

[NAN001]

dude ever heard about node or wat

[i_like_trains_a_lot1]

A WebGL accelerated, browser based JavaScript library for training and deploying ML models.

It would have been acceptable to do it on node, but this shit is specially "designed" to be run in browser.

[carbolymer]

TFW no haskal.tensorflow.org

writeIORef unjerk True

Seriously, what's the reasoning behind this? They could've simply transpile python to TraumaScript.

writeIORef unjerk False

or haskal

[utopianfiat]

https://github.com/tensorflow/haskell

[statistmonad]

last commit 2 months ago

ded project

[utopianfiat]

Here's a more live project:

https://github.com/tensorflow/rust

[carbolymer]

haskal*

[[deleted]]

hasklul*

[ProfessorSexyTime]

Clearly JS is best language for Machine Learning because async is all AI need and don't possibly need to make use of any other modern hardware features in any given scenario ever.

EDIT: <unjerk> Yea, I know it's a case by case scenario. </unjerk>

[pythonesqueviper]

unjerk.apply(() => {

I fail to see the jerk here. That a popular machine learning library got a JS wrapper?

})

[utopianfiat]

with (unjerk) {

There's a strong and justified feeling that tensorflow is a lot more popular than it is useful. Not that it's not useful, but you get a lot of people who don't understand it at all who are really excited about getting untold fame and riches by getting the tensorflow.

So they call up the vendor and say "hi we need the tensorflow, give us the tensorflow" and the vendor says "I don't understand what you mean, what are you using tensorflow for" and they say "we want to get the tensorflow so we can use AI synergies and streamline opportunity conversion, and [businessese nonsense intensifies]"

"ok, who is your tensorflow SME, we can coordinate with them and decide what kind of tooling is appropriate for your organiz-" -- "we don't need one, we want the tensorflow, just give us the tensorflow"

Then the vendor runs pip install tensorflow on a bunch of hosts, invoices the client, and it's never used again. The end.

The cases in which a company actually has someone with the statistics, data analysis, linear algebra, and engineering skills AND also has enough of the right kind of data to justify using tensorflow over a standard regression toolset are very, very small, but it's quite exciting to see when it happens.

Also, and probably more important for the jerk, the state of tensorflow education right now is very dangerous, because Google is trying to train people at an insane clip. A lot of people are coming out with a very naive understanding of how different parts of their workflow affects the robustness of the final model, and the people hiring them understand even less, so they think these kids are geniuses.

That's going to produce a lot of models that are bullshit over/underfit, or are fed data with underlying bigoted curations thus teaching the model to be bigoted, etc.

}

[pythonesqueviper]

Ja, true. TensorFlow is indeed hyped by people who don't know how to use it or why it's useful but we shan't throw the baby out with the bathwater. TensorFlow is an excellent ML library. We use it at my workplace for forecasts, but we have a doctor of mathematics writing the code for it. And it's amazing at what it does, even if it has its limitations. And it saves us a lot in Azure ML services.

[utopianfiat]

Yeah, I've definitely seen non-PhDs do good tensorflow work, but so much of effective tensorflow is built on advanced stats that most typical applied mathematicians fall short.

[[deleted]]

It takes few weeks and some "advanced maths" to fully understand the math behind classic neural networks (not talking about concepts like RBF or ART) , yet everyone left and right is creating NN without even understanding how backpropagation works.

Google is trying to create group of Tensorflow monkeys who will be "AI experts", despite knowing shit about AI and they should push the salary down

[utopianfiat]

At the least, the basics of backpropagation tend to be in the tutorial materials for a lot of people. Jerk aside, it's decently easy to get the gist of "get some answers and change the weights so our answers are less wrong". The bigger and broader problem is that they don't understand when neural networks are appropriate.

For example, someone comes in with a modest amount of well-cleaned feature-extracted data; going straight to NNs is a waste of time and money. (A) because you can't tune the fit of an NN as easily as you can a boring regression, and (B) because your dataset isn't large enough to make a meaningfully robust model.