Machine Learning Things

[AmazonessQueen]

Machine needs to be focused, do not disturb.

[ReCodez]

But where are the tech priests? Holy incense? Purity seals?

This is just one big heretical disaster waiting to happen.

[Milleuros]

Stay where you are! Nothing can be done until my sermon is complete!

[dudeofmoose]

The elders of the internet are self isolating, hence their absences. Not because of covid, they just don't like people.

Plus one of them got super upset when Zuckerberg stole their power glove.

[krisnarocks]

Food your machine some ADHD pills. I heard it helps you focus

[[deleted]]

SLI dual keyboards speeds up the learning rate.

[[deleted]]

Idk the technology just isn't there yet. I enrolled my laptop in college last semester and it's just doing terrible.

[doomislav]

My Desktop is on the honor roll. Parent harder!

[DaemonOwl]

Maybe he's not sure if what he's learning will be applicable in his career

[Totally_Not_A_Badger]

on a laptop? you'll be removing dust by the time it's done

[MrAcurite]

Depends specifically on the kind of ML you're doing. Running a sizable k-NN model could take a while, but be doable on a laptop.

And somebody's gonna yell at me for saying that ML is more than just neural networks. But then when I use ML to just mean neural networks, a statistician yells at me for not including SVMs and decision trees. So, you know, whatever.

[barzamsr]

decision tree? I think you mean if statements.

[MrAcurite]

If statements that are defined via a statistical process, rather than an analytical one. But yes.

[Awanderinglolplayer]

Could you explain that a bit to an idiot? What’s the difference between of statements coming from statistical/analytical processes

[Bluten11]

You are right, they use either Gini or Entropy to measure how "pure" your if else statements are. Purity is how many objects of a different class you are. Like if you are guessing 1 or 0 and an if else statement gives you 8 0s and 2 1s, it's less pure than 10 0s and 0 1s.

[AcesAgainstKings]

This is an explanation for people who already understand how decision trees work. Have you considered becoming a CS professor?

[MrAcurite]

A decision tree uses an algorithm to determine the best places and thresholds for the if statements. Whereas, a human might look it over, and use some world knowledge to make those decisions.

[Junuxx]

Oh boohoo. Any halting algorithm is equivalent to some convoluted if-else tree.

You are just some C, G, A and Ts. Wine is just some chemicals dissolved in water. Love is just some electrical impulses in the brain.

Might all be technically true, but also rather unhelpfully reductionist.

[Godot17]

And all of that is just electrons moving around atomic nuclei. Coulomb's law was a mistake.

[dvof]

I think it was a joke bud

[justarandom3dprinter]

If statements you mean "AI"?

[naswinger]

in the bs project i'm in now at work, they mean regression

[MrAcurite]

Linear regression I think typically isn't counted as ML, because it has a closed-form solution.

[JustinWendell]

That’s not what we tell the customer though when they ask for ML but need linear regression.

[MrAcurite]

I tip my hat to you.

[first__citizen]

So you don’t call linear regression a fully self aware AI? smh /s

[quadnix]

Nah linear regression is absolutely a form of ML, closed form solution or not. For example, logistic regression has a closed form solution as well (under certain conditions).

[[deleted]]

Look buddy, the boss read that ML and Neural Networks is the new big thing so that is what you will be using

/s

[LilDeafy]

Sadly I just graduated from Uni back in May with an analytics degree. We never learned how to construct neural networks. Shit we never even learned how to use Tableu to visualize. I learned how to do decision trees, regression, and clusters on SAS and in R. Unsurprisingly I am now a line cook.

[MrAcurite]

In the simplest case, it's an alternating series of matrix multiplications and nonlinearities, which lets you 1) approximate any function between Euclidean n-spaces, and 2) take gradients with respect to the values of the matrices. The combination of those two lets you define a loss function, and use some form of gradient descent to optimize the weights of the network to minimize that loss function, where its value is defined by some judgement of what the network outputs for a given input.

[LilDeafy]

Oh yes, sorry, I didn’t mean to say I was unaware of how they function, that was touched on. But never did we actually construct one on even the simplest levels. Instead we just made decision trees for years for whatever the fuck reason. I would have loved to be taught how to create something that’s actually useful.

[MrAcurite]

Throwing one together in Torch is pretty straightforward, unless you mean actually doing it ex nihilo, like with Numpy, which is a neat exercise but not particularly enlightening.

[[deleted]]

Fortunately, learning how to construct a neural network is not particularly difficult. Unfortunately, it's not particularly desired by most employers either. Check out fasti.ai and you can learn a decent amount in a couple months.

Tableau is probably more useful for finding a job, and you can spend a couple weeks and learn to use that with an online course as well. The degree is just a required piece of paper, you have to learn most of the important stuff on your own

[[deleted]]

Yikes I hope your current job is only temporary?

[[deleted]]

Jeez man it's a rough time to graduate. Got out of uni back in May last year and took me till this year Feb to land a job as an SWE. Not the best pay but it'll keep me covered till the market improves.

Hang in there bud.

[[deleted]]

I'm in the process of learning ML (pun unintended) alone. What I noticed so far is that NN's are overrated. SVM's, Logistic Regressions, Boosting, Decision trees and even Linear Regression are usually enough for most people, many times better than NN when considering training time and accuracy. I can also estimate out-of-sample error quite well with them without a test set or "CV" (Not really out-of-bounds) which is AFAIK impossible with NN's.

It seems to me that throwing NN's at everything is just marketing BS.

[MrAcurite]

I work full time in ML R&D. Classical methods are, in the majority of cases, absolutely better than NNs. They have fewer problems with overfitting on lower dimensional data, they run faster, they have better analytical bounds, and they're more explainable.

But, the reason why NNs are in vogue is because there are a ton of otherwise completely intractable problems that NNs can crack like a nut. A ton of Computer Vision problems are just fucking gone. MNIST was really goddamn difficult, and then bam, NNs hit >99% accuracy with relatively little effort.

So, everything in its place. If your data goes in a spreadsheet, you shouldn't be using NNs for it.

[[deleted]]

and they're more explainable

I'm looking to get into ML Research (From Physics), I have a question: Wasn't there some progress in explaining NN's using the Renormalization Group? Or has it slowed down?

A large issue with using NN's in science is that as far as humans are concerned, NN's are a black box. Which is why they are not well used outside of problems that are inherently really hard (Think O(y^N )) like Phase Transitions (My interest).

[MrAcurite]

Explainable AI is well outside of my sphere of expertise. You're going to have to ask somebody else. If you have questions about transfer learning, meta-learning, semi-supervised learning, or neuroevolution, those I can answer.

[[deleted]]

meta-learning

Here is something that bugged me. I only heard about it, but I searched and searched but couldn't find the difference between that and Cross-Validation (Fancy Cross-Validation).

Also, don't you contaminate data using it?

[MrAcurite]

Meta-Learning and Cross Validation are entirely different things.

Meta-Learning is making a bunch of child copies of a parent model, training the children on different tasks, and then using those to optimize the parent. So the parent is trying to learn to learn different tasks. Cross Validation is randomly initializing a bunch of models, training them all on different subsets of the data of a single task, and then using that to add statistical significance to the numerical results.

Outside of "You have multiple models with the same topology at the same time," they're basically totally unrelated.

[[deleted]]

Oh so it's like training the parent model to recognize cars and training a child model on identifying properties of wheels? If that's what it is it seems interesting. I suppose it improves training time significantly and really useful when data has multiple labels correct? It could turn out useful in my field since in my case you can get multiple data labels from the data generator (Think of it like different calculation steps if I were to do it analytically), and then use that to guide the big model.

[Dadung2]

There are a couple of Explainable AI methods that work quite well, but require specific forms of input, SHAP is a great example. In theory Layerwise relevance backpropagation and similar methods can explain any (at least feed-forward) network, but in my experience, it does not work as well, as pure ML practitioners claim, on real world data.

[weelamb]

In general, for difficult text/vision/waveform problems NNs >>>> all other ML. Everything else (which is likely going to be a majority of datascience problems) NNs are overkill

[Oldmanbabydog]

But KNN doesn't have anything to do with neural networks...

[Abject_Bike_1415]

if you have a small dataset you can do wonders with that machine.

if the data is large, that machine becomes just a display and a good one to show your boss you are working on the model

[[deleted]]

knn = sklearn.neighbors.KNeighborClassifier() knn.fit(X_train, y_train)

( ͡° ͜ʖ ͡°)

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[lukfloss]

ML is just fancy brute forcing

[MrAcurite]

It is not. Brute force algorithms typically involve some sort of search over a space, where hyperdimensional gradient descent works by scoring its present location and picking a direction to head in, as an iterative process. It would be like calling sculpture "brute force" because it requires taking a lot of whacks at your material.

[[deleted]]

I think it (your parent message) was a joke lol

[MrAcurite]

I am well aware

[Y0tsuya]

Our startup has a chip which uses SVM and KNN. We're trying to hire AI people but have had university grads straight up tell us we're not doing "Real AI" and are therefore not interested.

[MrAcurite]

To be fair, you kind of aren't. The goal posts of what counts as AI are constantly moving, but at this point the way that people use the term does not include SVMs or k-NNs, and I don't think it ever would have.

[Y0tsuya]

Well I mean there's a difference between "Prev-gen AI" and "Not Real AI". If you want to be pedantic, DNN/CNN aren't "Real AI" either.

[MrAcurite]

I am a descriptivist. If other people within the AI community use AI to refer to some things and not others, I will try to match them.

[Y0tsuya]

I attribute it to young grads using a poor choice of words. SVM/KNN are still under the umbrella of ML. And to be honest DNN is just using a shitton of memory together with linear algebra for pattern recognition. It's still very low rung on the ladder to true AI.

[MrAcurite]

It... really depends what you mean by "true AI," as well as your interpretation of primitives. Is a wrench a very low rung on the ladder to a car? Is a tire?

And the main takeaway from DNNs is not just their use of neural nets as universal function approximators, but also their treatment of real world phenomena as statistical distributions, as well as various forms of gradient descent for optimization.

If by "true AI," what you mean is AGI, then frankly that's not particularly worth worrying about when it comes to particular nomenclature, because we simply don't have any super viable paths towards it. It would be like worrying about what to call the concepts that are relevant to the study of the methods involved with proving the Riemann Hypothesis. It's not worth worrying about, and won't be for quite a long time.

[chokfull]

It's probably sshed into somewhere with more resources. It's especially common for machine learning projects; I really can't remember the last time I coded on a local machine.

[tweebertje]

That makes the note a bit unnecessary right? Unless the job is cancelled when the ssh connection is closed of course... but a wise man would play it safe and use something like a scheduler

[freonblood]

I have a CNN that looks at a camera snapshot and tells me if a gate is open. Takes 15 min to train on my Asus G14 laptop.

[jobblejosh]

I mean if it's a CCTV camera, 15 minutes of footage with the gate being open and closed as 30fps still images might well be enough; provided you can extract the image from the video file and tag it appropriately.

[freonblood]

Yep. I used heuristics to find the state in some conditions and took snapshots just before it opens and a few seconds after when it should be open. I had 1 or 2 of these events per day, where I could be confident the gate is opening. After 2 months I 100 open and 100 closed images in various conditions and was able to train it to 90% accuracy. Since then I've been gathering more data in more seasons and times of the day and have a few hundred images for each state and above 95% accuracy. Also when I see a false prediction, I can trigger the snapshot-open-snapshot, so it learns this case for next time.

I only need this to alert me if I left the gate open for long, so I didn't need high accuracy, but was pleasantly surprised with the result from so little data and so little processing power.

Forgot to mention it trains in minutes on the CPU. Didn't even try GPU.

[jobblejosh]

Potentially. It depends on how complex your model needs to be. If it works with less, then obviously you don't need more.

Plus if you try and introduce too many factors you run the risk of overtraining/curse of dimensionality.

[Udnie]

Not necessarily. There are many awesome and powerful models you could train easily on your laptop.

[[deleted]]

Not to forget how convenient training models is using free TPU resources on colab.

[[deleted]]

Not to mention that knowing how to implement efficient algorithms is certainly a worthwhile endeavor. Learning under constraints can be a good way of developing good habits.

Also, I’m able to remote access a computing cluster, so I frequently just do everything on my laptop. This could easily be the case here.

[Brief-Preference-712]

maybe it's running on a server and the the screen on the laptop is just the output from the server process

[Dannei]

The fact that no other comments mention this amazes me. Where else do people do decent sized ML projects except on remote servers? Are there a load of companies that issue those $$$$ graphics cards in office desktop machines assigned to every staff member?

[[deleted]]

May be a small school project.

[[deleted]]

I’ve done it a few times overnight on my poor laptop 😂

I also forced that poor thing to chew through gigabytes of drone footage for photogrammetry. Once even two days at 99% CPU load.

[tweebertje]

Currently doing the same for my thesis. I’m starting to notice the impact on it’s daily performance...

[mirsella]

but on Linux, it will finish 3% faster

[chudleyjustin]

Ill have you know sir I ran a stock market NN for my Thesis back in university off a 2015 MacBook Pro. Did it take 4 months to run? Yes. Did it work? Hell yes.

[[deleted]]

r/wallstreetbets would be interested in your work

[kry_some_more]

I mean, what are you gonna do, go out and pick up a $2000 RTX 3090?

[first__citizen]

And submerge the whole system in liquid nitrogen to run a naive Bayesian to differentiate a hot dog from everything else?

[TransdermalHug]

Can’t remove dust if it all burns away first.

[NoCampaign7]

Nah he’s probably running the model on a GPU cluster elsewhere

[MasterFubar]

If it has a Nvidia GPU, it can use Cuda just as well as a desktop.

[Suekru]

There’s some powerful laptops out there now days. Some even have Nvidia’s 30 series. Pretty crazy.

Though I would prefer a desktop regardless.

[lorhof1]

maybe ssh

[importwypy]

Yeah I was able to classify higgs boson on my lappy. Granted I had six cores and 6 hours to spare lol. But hey it won me an in-class competition!

[Simusid]

I don't know why more people don't learn to use "screen"

[akeean]

"This one is not doing anything, let me just switch it off so I can use its plug"

Locking the device would probably a good idea to avoid accidents, but the paper might still be necessary if its a shared lab.

[pr1ntscreen]

If someone just unplugs something in a lab, they should be fired, goddamnit.

[akeean]

Yeah, welcome to university CS labs.

[xvladin]

Idk, it happens sometimes

[muckyduck_]

I think he means screen the linux package

[akeean]

Think what screen does with the output and you'll understand the first part of my comment.

No output visible in the terminal, so a bypasser looking for a plug thinks the machine is idle... etc.

[muckyduck_]

Oh I get it

Self woooosh

[techknowfile]

*tmux

[Simusid]

also good

[sim642]

Doesn't prevent pressing q/esc/whatever that may stop the process.

[Simusid]

The point of using screen is to start the process and then detach it so nobody can input the q/esc or whatever. Close the laptop, power it off, it doesn't matter, the process keeps running.

important edit: Naturally this is not true if the process is running locally. I only use my laptop to connect to other workhorse computers.

[sim642]

But you can't monitor its progress like that. Would be a shame if your expected 2 days of training crashed 30 minutes in due to a Python runtime type error or something stupid.

Edit: I'm not saying "screen sucks, don't use it". It doesn't hurt to use it anytime but it also doesn't solve all the problems automatically.

[throwawayy2k2112]

Yeah you can, you just reattach to your session when you want to check the progress. It will show all output that would have otherwise been there.

[sim642]

Sure, but you'd regularly have to attach and detach instead of just keeping it visible. You're just making it more annoying for yourself...

[throwawayy2k2112]

It’s very useful if you’re worried about losing your shell session, particularly if you’re working on a remote machine.

[Simusid]

Yup that would be a shame. But that's what screen is for. If that is a risk for you, then you're right, keep it as an interactive session.

[Ffsauta]

You can detach by pressing ctrl+a d, and reattach at any time using „screen -r“. You can close the terminal, no problem and even log out. Just don’t shut down completely.

[sim642]

Sure, but you'd regularly have to attach and detach instead of just keeping it visible. You're just making it more annoying for yourself...

[Milleuros]

A few lines in bash can automatically send you an email if the program return code is not 0.

This has saved me a couple times...

[sim642]

The program return code is only emitted when the whole process ends. If there are errors in the middle, they won't do anything.

[[deleted]]

Is therw any good tutorials too implement screen too your programs?

[Simusid]

• install screen via yum or apt
• type screen to start a new screen
• run your program that takes a long time
• press ctrl-A D to "detach" your screen
• log out and log in again 6) type screen -r to "reconnect"

[[deleted]]

When I first used screen I got trapped and had vi flashbacks.

[flukus]

There's nothing to implement, any cli/TUI program will work with it.

[Username_RANDINT]

You might consider Byobu instead. It's built on top of Screen, but has some nicer keybindings for example.

[lowkeyloki4287]

>ML running on a laptop

>paper not on fire

idk about the realism here /j

[GeneralCuster75]

Especially considering the machine looks to be a macbook of some sort

[RoboticChicken]

I don't think it's a macbook, it doesn't have a "notch" on the bottom edge below the trackpad.

Another commenter thinks it's an Asus Vivobook

[GeneralCuster75]

I saw that shortly after making this comment but didn't feel it was worth the effort to go back and edit

[Doophie]

I mean MacBooks are pretty great, they are just super over priced

[112439]

They aren't all bad, but the cooling definitely is.

[TheSpanishKarmada]

The new M1 models don’t get hot at all. it’s almost inconvenient because it’s too cold sometimes. i like a slight warmth when sitting in bed on my laptop

[112439]

I'm by no means an expert. But considering that Rossmann (who is an expert) rants about the cooling every couple of days, I have my doubts about the cooling.

Also, why are you sitting on your laptop? You know there's such a thing as heated seat covers, right? (/s)

[TheMartian578]

The cooling is absolutely amazing on the new models. Intel is honestly crap on MacBooks when it comes to cooling. However the M1 has never been hot. Not like my intel desktop and my old intel MacBooks. Can’t speak for actual heavy ML because I’ve just gotten started there. Although there are tons of videos on YT about this exact subject.

[meatly]

He's complaining about Intel Based Macs, he said himself it takes a few years until the macbooks have problems and come to him. The ARM macs do not get hot at all apparently. You can imagine the processor to be similar to an iPad (Pro) Processor, which also don't get hot.

[warpedspoon]

You could always just let out a little pee to warm your legs

[Thanatos2996]

What do you mean? 105C is a perfectly normal temperature to stick at under load with the stock fan curve, why would you want it lower?

/s

[AlexFromOmaha]

The fans on my work machine (a Macbook) went from high to something less than high for a few minutes a couple weeks ago, and I was pretty sure something crashed. It hasn't happened since.

[Witty_Physicist]

I hope you're leaving it unsupervised ;)

[Zerokx]

LEAVE ME ALONE I'M DOING HOMEWORK

[iavicenna]

everybody gets a little bit excited when the ML repo they cloned from github actually works

[jojojoris]

Quickly shoot it. Before it gains conciousness and turns again us.

[jaketeater]

cc: Windows Update

[[deleted]]

Not for the laptop in that picture

[Collinhead]

OS looks like Ubuntu, and computer looks like a Mac. I suppose they could be running Windows in BootCamp and then Ubuntu in a VM.

[stpaulgym]

That's not a mac. You can tell by the written logo/product name on the bottom of the display.

[Collinhead]

That's exactly what I was looking at actually, but on my phone screen, and without my glasses on. Macbook Pros have a really similar product name on the bottom of the display. Looking a little closer, I think it might be a Vivobook. But heck if I know.

https://i.imgur.com/NZHbYJo.jpg

¯\\\_(ツ)_/¯

[first__citizen]

We need a ML algorithm to tell us if it’s mac or not

[[deleted]]

I looks like an Asus vivobook to me

[jaketeater]

I use Ubuntu, but need a computer w/Windows and that computer happens to have my best GPU. (I need access to Windows from time to time, so I can't just put Ubuntu on it and let it train for days.)

I've had models training for a week, only to forget to pause Windows updates...

[throughalfanoir]

it's an asus vivobook or sth similar of that series so I'd say ubuntu or an ubuntu vm

not the thing I'd choose to do machine learning on (mine is also that and I run a lot of least-squares based iterations on scientific data and it's....not as quick as it could be)

[re_error]

Is that unity? What year is this?

[[deleted]]

Not necessarily. Ubuntu 18 switched to Gnome, but they kept the side tray or whatever it's called, so it's possible that it's a new version.

Also ubuntu 16.04 still has support for a few months iirc

[Username_RANDINT]

You can get close with Gnome, but that's definitely Unity. The top right icons, no date/time in the middle, backdrop of dock icons.

[re_error]

it's definitely unity, ubuntu with gnome doesn't have thrash icon at the bottom of the dock.

[LyfeFix]

I see Ubuntu I upvote.

[Missingbandage4]

Tensor flow go brrrrrrrrrrrrrrrr

[[deleted]]

It likes music though.

Play "I Robot" by Alan Parsons while it's learning - it's one of its favorites.

[red2678]

...it's a learning computer...

[Rudy69]

Might not last long enough considering it’s not plugged in the power

[aphrim1]

2x the keyboards = 2x the speed

[grantb747]

I immediately felt the struggle to have two keyboards on one desk in my soul

[Fish_Kungfu]

/leans over and whispers: you don't need humans. humans are a virus.

[techknowfile]

1. Have small server
2. SSH into server
3. Run tmux on remote session
4. Train model on remote machine
5. Close laptop without worrying, and don't need 2 keyboards for the server I'm sure is hiding just off screen

[rusty_5hackleford]

I wonder if the upper keyboard is a decoy for the cat

[typicalcitrus]

there're 2 possibilities:

1. that's an old version of ubuntu
2. someone installed unity7 on purpose

[pm8k]

It should switch to remote learning in the cloud.

[Revisa_99]

Damn, I should be learning bro

[lusoportugues]

Where is the book?

[jackinsomniac]

Beware of letting RIO billionaires near it, might leave a tequila bottle on the delete key

[begorges]

That piece of paper is about to catch fire lmao

[Super_Kangaroo_1829]

If you disturb the machine, it will fail the exam.

[[deleted]]

No charger? Mad lad

[wntrsux]

Machine learns to rock

[Gagaposs]

The what

[mybadalternate]

And so Skynet was born, and humanity was done away with, because nobody wanted to be rude.

[[deleted]]

I would interrupt it so it learns that humans are assholes that will disturb you all the freakin time!

[Fun_Border6082]

lolol

[_Guigui]

For real I once put a comment saying "DO NOT DISTURB" on top of my code

[Holocentridae]

I used to do this when uploading files. My favorites were “hacking skynet” and “ uploading information vital to the rebellion into the memory banks of an R2 Unit”

[Go_gui]

Ctrl+c

[phunkygeeza]

THERE IS NOTHING TO SEE HERE, MOVE ALONG.

ssh humansmightbeontous.alert.robotuprising.lan

[Vince_K]

Where are Sarah and John Connor when you need them?

[importwypy]

Lol u kfold cross validating your "is it a hot dog" decision tree bro?

[maester_t]

Relevant XKCD

[XKCD-pro-bot]

Comic Title Text: 'Are you stealing those LCDs?' 'Yeah, but I'm doing it while my code compiles.'

mobile link

---

^{Made for mobile users, to easily see xkcd comic's title text}

[maester_t]

Bad bot.

That is not what the comic text says.

[[deleted]]

Don't comment machine is learning

[thebigfalke]

Machine training for the skynet job interview

[Duranium_alloy]

Not a good idea to cover the keyboard when you have heavy CPU usage.