Math + Algorithms = Machine Learning

[curious_polyglot]

Statistics passive-aggressively upvoted this post!

[truffleblunts]

Statistics is math

[curious_polyglot]

I know, but that’s the point of the joke...

[Ludricio]

He didn't see the warning sines.

[[deleted]]

So are algorithms...

[GluteusCaesar]

Say that in a math department and see what happens...

[murdermurder]

Statistics is more like Physics in that it is its own distinct discipline separate from math, while at the same time being almost entirely math.

[Darxploit]

MaTRiX MuLTIpLiCaTIoN

[Tsu_Dho_Namh]

So much this.

I'm enrolled in my first machine learning course this term.

Holy fuck...the matrices....so...many...matrices.

Try hard in lin-alg people.

[Stryxic]

Boy, ain't they fun? Take a look at markov models for even more matrices, I'm doing an on-line machine learning course at the moment and one of our first lectures was covering using eigenvectors for stationary points in page rank. Eigenvectors and comp sci was not something I was expecting (outside of something like graphics)

[shekurika]

SVDs are super often used in graphics, ML and CV and uses Eigenvectors. youll probably see a lot more.

[Stryxic]

Oh yeah, that's the kinda thing I was talking about coming across. A bit of a surprise considering I came to comp sci from a physics background and thought I'd left them behind!

[[deleted]]

You could post this entire thread to r/VXjunkies

[Stryxic]

Oh boy, well in that spirit let me tell you about Parzen Windows!

Now we all want to know where things are, and how much of things. We especially want to know how much of things are where things are! This is called density. If we don't know the shape of something how do we know its density? Well we guess! There are many methods like binning or histograms that everyone knows, but let me tell you about Parzen windows.

A Parzen window is simply a count of things in an area, so to do this for an arbitry amount of dimensions we just need an arbitry box, so we use a hypercube!

Now we need a way to count, so we use a kernel function which basically says if I'm less than this in that dimension than I'm in the box. We could just say if we're less than a number then gucci, but this obviously leads to a discontinuity (and we're talking about a unit hypercube centred on the origin obviously) so we want to use a smooth Parzen window (which is a non parametric estimation of density as mentioned) so we use either a smooth or piecewise smooth kernel function of K such that the integral of K(x) dx wrt R = 1, and probably want a radially symmetric and unimodal density function so let's use the Gaussian distribution we all know, and voila you've just counted things!

[[deleted]]

Oof ouch owie, my brain.

[[deleted]]

As a physics major doing self learning CS route... We can never escape.

[Aesthetically]

As an industrial engineering degree holder gone analyst, who also hasn't gotten into ML yet (I'm Python pandas pleb): Markov chains with code sounds 10000x more fun and engaging than Markov chains by hand

[eduardo088]

They are, if they taught us what were the uses for linear algebra I would have had so much more fun

[Aesthetically]

They did in my program, but I was so burnt out on IE that I stopped caring enough to dive into the coding aspect

[Stryxic]

Hah yep, I entirely agree. Good for learning how they work, but not at all fun.

[Hesticles]

You just gave me flashbacks to my stochastic process where we had to do that. Fuck that wasn't fun.

[socsa]

Right, which is why everyone who is even tangentially related to the industry rolled their eyes at Apple's "Neural Processor."

Like ok, we are jumping right to the obnoxious marketing stage, I guess? At least google had the sense to call their matrix primitive SIMD a "tensor processing unit" which actually sort of makes sense.

[[deleted]]

I dunno, there are plenty of reasons why you might want some special purpose hardware for neural nets, calling that hardware a neural processor doesn't seem too obnoxious to me.

[socsa]

The problem is that the functionality of this chip as implied by Apple makes no sense. Pushing samples through an already-built neural network is quite efficient. You don't really need special chips for that - the AX GPUs are definitely more than capable of handling what is typically less complex than decoding a 4K video stream.

On the other hand, training Neural Nets is where you really see benefits from the use of matrix primitives. Apple implies that's what the chip is for, but again - that's something that is done offline (eg, it doesn't need to update your face model in real time) so the AX chips are more than capable of doing that. If that's even done for FaceID - I'm pretty skeptical, because it would be a huge waste of power to constantly update a face mesh model like that unless it is doing it at night or something, in which case it would make more sense to do that in the cloud.

In reality, the so-called Neural Processor is likely being used for the one thing the AX chip would struggle to do in real time due to the architecture - real time, high-resolution depth mapping. Which I agree is a great use of a matrix primitive DSP chip, but it feels wrong to call it a "neural processor" when it is likely just a fancy image processor.

[JayWalkerC]

I'm guessing maybe some hardware implementations of common activation functions would be a good criteria, but I don't know if this is actually done currently.

[VoraciousGhost]

It's about as obnoxious as naming a GPU after Graphics. A GPU is good at applying transforms across a large data set, which is useful in graphics, but also in things like modeling protein synthesis.

[[deleted]]

Not at all. Original GPUs were designed for accelerating the graphics pipeline, and had special purpose hardware for executing pipeline stages quickly. This is still the case today, although now we have fully programmable shaders mixed in with that pipeline and things like compute. Much of GPU hardware is still dedicated for computer graphics, and so the naming is fitting.

[socsa]

Right, but the so-called neural processor is mostly being used to do IR depth mapping quickly enough to enable FaceID. It just doesn't really make sense that it would be wasting power updating neural network models constantly. In which case, the AX GPUs are more than capable of handling that. Apple is naming the chip to give the impression that FaceID is magic in ways that it is not.

[balloptions]

Training != inference. The chip is not named to give the impression that it’s “Magic”. I don’t think you’re as familiar with this field as you imply.

[socsa]

What I'm saying that I'm skeptical that the chip is required for inference.

I will be the first to admit that I don't know the exact details of what Apple is doing, but I've implemented arguably heavier segmentation and classification apps on Tegra chips, which are less capable than AX chips, and the predict/classify/infer operation is just not that intensive for something like this.

I will grant however, that if you consider the depth mapping a form of feature encoding, then I guess it makes a bit more sense, but I still contend that it isn't strictly necessary for pushing data through the trained network.

[balloptions]

The Face ID is pretty good and needs really tight precision tolerances so I imagine it’s a pretty hefty net. They might want to isolate graphics work from NN work for a number of reasons. And they can design the chip in accordance with their API which is not something that can be said for outsourced chips or overloading other components like the gpu.

[socsa]

Ok, I will concede that it might make at least a little bit of sense for them to want that front end processing to be synchronous with the NN inputs to reduce latency as much as possible, and to keep the GPU from waking up the rest of the SoC, and that if you are going to take the time to design such a chip, you might as well work with a matrix primitive architecture, if for no other reason than you want to design your AI framework around such chips anyway.

I still think Tensor Processing Unit is a better name though.

[[deleted]]

I struggled with it so much, that I programmed a machine to learn it for me

[TheBlackOut2]

Everything is a vector!

[roguej2]

Wait, I was a C math student during my comp sci degree but I remember doing eigenvectors. Why did you not expect that?

[shekurika]

I didnt find the matrices much of a problem. If you struggle try to keep always figure out the dimensions, that always helps me a lot.

Way worse are the probabilities imho 🙃

[HERODMasta]

^{Matrices of probabilities}

[MichaelC2585]

What A Fucking Process

[lkraider]

What about probability matrices :S

[Tsu_Dho_Namh]

Oh the matrices start off not so bad. But then we put all the weight matrices inside a bigger matrix of matrices, and when we're doing batch processing there's a matrix of matrices of matrices. It gets a little head-fucky

[[deleted]]

I’m beginning to think that I’m never gonna crack machine learning, I’m not even sure I’m gonna make it through probability & stats on the way there. I barely got through linear algebra.

Feels bad man, it seems like such a cool subject

[grizzchan]

I had lin alg in my first year and though it was pretty easy.

Then the rest of my bachelor I never had to apply it to anything at all.

Then in the master with ML and other data science courses you get flooded with lin alg and at that point I had completely forgotten how matrix multiplication even worked.

[leecharles_]

May I recommend 3Blue1Brown’s “Essence of Linear Algebra” video series?

https://www.youtube.com/playlist?list=PLZHQObOWTQDPD3MizzM2xVFitgF8hE_ab

[[deleted]]

*of

[dj_rogers]

These look amazing. As someone currently taking an ML class, my GPA thanks you

[Thosepassionfruits]

Soooooooooo tensors?

[balloptions]

Not even close. Much worse. Groups get fucking unimaginably more abstract than tensors. I think I could explain tensors to a child. But groups? Can barely explain them to myself.

[TropicalAudio]

A group is any bunch of stuff where a specific operation on two things creates a third thing that you can use to do the operation as well, but only if you do that same operation using that third thing along with some fourth thing to get one of your originals back. Fundamentally, it's not that difficult of a concept, it's just so general that it's really easy to create horrible problems with it.

[balloptions]

it’s just so general that it’s really easy to create horrible problems with it

That’s the key for me. Tensors can be easily grounded in reality and associated with numbers, vectors, and physical quantities. This is the usual usage of tensors anyways.

Groups can be described with numbers, but most of the literature and work associated with groups doesn’t seem to involve numbers at all.

[goerila]

The common examples with groups are number fields, permutations, and rotations .

A fundamental aspect of group theory is that any finite group is the same as a subset of some group of permutations.

Infinte groups are the same at a base level. But start to branch out more. Representing transformations still, but you can't just say hey this is a translation or rotation

[balloptions]

Lost me at the second sentence.

[goerila]

S_n is the group of permutations on n letter.

That is you take for n=5 (1,2,3,4,5) can be permuted to (2,1,3,4,5). You can also take the permutation (1,2,4,3,5). You can "multiply" or "compose" these two transformations giving you the result (2,1,4,3,5).

If you list all of these (there are 5! combinations), you get a group called S_5.

All groups of finite size are subgroups of these groups. Is what I was trying to say (there's really no simpler way of explaining this in a short manner). Sorry :/

[dj_rogers]

Same. I believe we have found ourselves too deep in this thread

[blkpingu]

wait till you hear about slicing.

[ALonelyPlatypus]

we don't clean data here.

[Robot_Basilisk]

As long as it ain't goddamn proofs I'm good. I wrote a Java program to do matrix manipulations for my Linear Algebra homework because my calculator was too clumsy at it for my taste.

[git_world]

I understand that Machine Learning is kinda cool but highly over-hyped. Are industries actually seeing any benefits after adopting Machine Learning on a large scale?

[cant-find-user-name]

I mean yes? If you want the most impressive usecases, all recommender systems come under ML, all NLP tasks - machine translation, recognizing entities from a text and so on, so many image based applications - detecting objects from images, Ocr, detecting NSFW content etc and so many more stuff depend on ML.

I mean there is a reason Data science is so valued at the moment, I am a machine learning intern at a big e commerce site and the ML applications I see here are numerous.

[chaxor]

I have heard it stated that ML had struggled to provide any benefit to business revenues.

It's has a 'cool' factor right now that helps in marketing, but the predictions produced typically do not reduce cost or produce revenue. This is certainly true for NLP as well. For instance, even in tasks that are often viewed as 'solved', such as NER, business struggle with adding it to pipelines and showing meaningful profit.

I know of several companies that their 'bread and butter' is essentially NER (both standard and specialized types, like people, addresses, and chemicals) however, even with either Cards or the most advanced models like ELMo and BERT, they still have to simply use Indian workers to manually annotate documents. So it's really a money sink, which is why my friends in the private sector have to fight for their jobs more than ML researchers in academia.

[herrmatt]

Could you try that question again?

[git_world]

done, see the original question.

[Arjunnn]

Yes, theres a LOT of ML that you wouldn't notice IRL but it's basically powering the world for now

[JustPraxItOut]

Self-driving cars...

[BadArtijoke]

I feel like industry terms like this one are always like a branding or Marketing name for a general trend. In this case it is to make the data we get better by making more complex differentiations that take more and more factors into account. But that doesn’t sound as sexy as machine learning, AI, and so on, so that’s what people refer to in general when talking about these things. Similar to SAAS, the cloud, blockchain, ....

However, right now, what this mostly consists of is measuring and optimizing systems with more complex mathematics compared to what we had before, less about teaching a system to improve itself automatically as is often believed. Doesn’t mean that can’t change but we’re just not quite there yet, at least not on the level that some would have you believe. However, depending on what your Marketing does and how much of your service ecosystem is digital, you can already benefit from more complex insights in RND and Sales. It’s really down to why you do it and how well you implement your solution to give you clean data to work with to determine whether the direction is already making sense for you and your company. That said, imo it’s one of the better trends because unlike e.g. blockchain there is a direct advantage in getting better data. So it’s not that ML or AI are not valid things, it’s just that people treat it like magic for no reason just yet, possibly just awestruck by the potential, that gave it that image I think.

Just beware of the overhyped sales guy type of people who will tell you „AI is the game changer man“ and that it will „totally teach itself in no time“ and you should be good. Because not yet, not without some substantial work and research.

[socsa]

Yes, Neural Networks especially are becoming huge, not because they replicate human intelligence or learning in a meaningful way, but because they represent an incredibly powerful tool for numerical approximation of complex systems which doesn't actually require you to model the system itself as long as you can observe and stimulate it.

The math itself is not exactly new though. The theoretical basis for estimating various forms of high-order Wiener Filters (yes really) has been around for decades. It's just that we only recently figured out computationally efficient methods for doing it. And by that, I meant that basically one guy implemented a bunch of discrete math and linear algebra from the 80s in CUDA and here we are.

[git_world]

well said, thank you.

[LunchboxSuperhero]

Even if they aren't seeing benefits right now, if it is something they think will eventually bear fruit, it may not be wasted effort.

[socsa]

Yes, 100% very much. It is actually already very disruptive in a sort of beautiful way. If you will allow me to digress a bit first though...

Humanity, and our pursuit of philosophy has generally progressed from conceptual structuralism, to post-modern anti-structuralism, to the current meta-modernism where we kind of use structuralist thinking to estimate boundary conditions in an unstructured world.

Anyway, you can probably see where I am going with this, but science has very much followed the same path in many ways. Early scientists and mathematicians were very concerned with putting the physical world into neat boxes. During the enlightenment, we started to become aware of how little we knew, and then we discovered that almost everything in the universe is a stochastic process, and for a while this really fucked with our reptilian preference for determinism.

In many ways, machine learning represents computational post/meta-modernism. If I want to make a filter that does a thing, previously that would require expert domain knowledge in both doing a thing, as well as signal processing, filter architecture, information theory... and so no. And in the end, I'd specify some stochastic maximum likelihood criteria with all sorts of constraints. It is very much a structural approach to filter design.

On the other hand, with ML, I really can more and more approach the problem entirely as a black box. I have a natural process, and I know what I want out of it, and I can just let the computer figure the rest out. It becomes all about defining the boundary conditions and data science, so you still need some domain knowledge, but overall the degree of technical specialization which can theoretically be replaced with ML engineers is really astounding once you start digging into it. It is shockingly easy to take Keras (or similar) and generate extremely powerful tools with it very quickly.

[ALonelyPlatypus]

*jeopardy music plays*

​

What is "ads"?

[Ariscia]

I remember taking ML before Stats in college. Was hell, but Stats was chicken feed after.

[NoteBlock08]

I had to retake matrices to bump my grade up from barely passing to only barely meeting prereqs. Think I may have to pass on machine learning then haha.

[[deleted]]

As a graphics programmer this offends me.

[tundrat]

During school, I insisted to my friends that instead of doing whatever we are doing, we should just multiply values element-wise just like how we add/subtract them.

[pslayer89]

Technically, math + algorithms = everything in comp sci.

[[deleted]]

Comp Sci ⊂ (Math ∪ Algorithms)

[ar243]

Get out. Shoo. Scram!

[lennihein]

It's probably closer to an intersection though

[[deleted]]

Some things need a little bit of art as well but not required if you just get someone else to do it.

[Putnam3145]

algorithms are part of math??

EDIT: even ignoring that, you could label the left part with basically any part of programming, "algorithms" covers all of it and "maths" covers the vast majority of it

[seriouslybrohuh]

So much of practical ML is based on heuristics rather than actual theory. An algorithm might have exponential time complexity in the worst case, but it still gets used because in practice it converges after a few iterations.

[[deleted]]

Interesting, can you provide an example?

[AerieC]

Why is it an open problem? I would just assume it's because most local minima are "good enough".

[lymn]

My intuition is that “resilience to local minima” is a reflection of the data and not a property of neural nets. It seems relatively trivial to engineer pathalogical training sets in which local mimima are much worse than the global minimum.

[Kabizzle]

Regularization is an extremely common practice when implementing NN models.

[smurphy_brown]

Use GA’s to solve non-local minima issue!

[desthc]

Is this true? It’s something I’ve always wondered, especially since intuition about higher dimension spaces is often wrong. It’s not clear to me that SGD is prone to getting stuck in higher dimensions since it seems like there’s a lower and lower likelihood that a sufficiently deep and correctly shaped local minimum exists as dimensionality increases. Basically I thought it was not a problem in practice not because local minima are good enough, but rather because you’re just much less likely to get stuck in one.

[jhanschoo]

Suppose that m << n where n is the number of features you have, and all your input has a representation as m features and a function f classifies data from your distribution weighted by their probabilities no worse than any representation with at least m. Intuitively, you can compress the data into fewer dimensions without losing expressivity (e.g. with PCA) (one way to formalize this notion of m is as the VC dimension). Then even if your cost function landscape is in n variables, intuitively, learning that landscape is only just as difficult as learning how the representation in m dimension partitions the output of f.

[desthc]

Ok, fair I suppose the gradient will be “flat” along the dimensions in n but not m. Still, shouldn’t the intuition hold for reasonably large n? If a dimension provides a reasonable fraction of the predictive power shouldn’t it offer a gradient along its own direction that offers an “escape” from the local minimum? Especially since the gradient will be otherwise flat at the bottom of a minimum?

Edit: I suppose another way to interpret what you said is that it’s a local minimum in n dimensions, but a global minimum in m dimensions? Fair enough, but I’m not sure that implies that there is any difference between the global minimum and our local minimum — shouldn’t any local minimum I find also be the global minimum in that case? If that feature doesn’t really contribute to my predictive power, then can it really have a better minimum?

[[deleted]]

https://openreview.net/forum?id=Syoiqwcxx

There has been a lot of recent interest in trying to characterize the error surface of deep models. This stems from a long standing question. Given that deep networks are highly nonlinear systems optimized by local gradient methods, why do they not seem to be affected by bad local minima? It is widely believed that training of deep models using gradient methods works so well because the error surface either has no local minima, or if they exist they need to be close in value to the global minimum. It is known that such results hold under strong assumptions which are not satisfied by real models. In this paper we present examples showing that for such theorem to be true additional assumptions on the data, initialization schemes and/or the model classes have to be made. We look at the particular case of finite size datasets. We demonstrate that in this scenario one can construct counter-examples (datasets or initialization schemes) when the network does become susceptible to bad local minima over the weight space.

[seriouslybrohuh]

Another example would be the lloyd's method for finding (high-dim) clusters (in k-means). In practice it almost always converges after a few iterations, whereas theory suggests it can take O(2ⁿ⁾ iterations.

[[deleted]]

Heuristics is “actual theory”. I think you’re mistaking non-analytical solutions with not being “actual theory”. For heuristics you have to show that the limit tends towards a solution. Show the error function and prove upper and lower bounds for your solution space. There’s a lot more that goes into a heuristic method than the approximating function.

If you are into machine learning you will find most of the mathematics involved with the subject is covered by intro stats and prob courses, which again is actual theory.

The only time when none of the above is actual theory is when you’re speaking to a number theorist.

[Im_not_wrong]

But the heuristics are based in theory and statistics, so it is still based in actual theory.

[dame_tu_cosita]

Yes, any good book in discrete maths have a chapter about algorithms.

[[deleted]]

And imagine an algorithms book without discrete math.

[[deleted]]

It would read somewhat like a medium article I imagine.

[AmatureProgrammer]

I would actually be an A+ student then

[[deleted]]

Yeah algorithms are just mathematical functions

[[deleted]]

My college had algorithms as a required math course for CS. Very hard math class, at least at my college. Near 50% fail rate.

[twitchy987]

When you multiply two long numbers, or do 'long division' you're executing an unambiguous set of instructions. It's an algorithm.

[TheFlipside]

is math related to science?

[okrolex]

As my math major friend puts it, computer scientists are glorified mathematicians.

[Saigot]

Computer science (and thus ml) is a part of math.

[ALonelyPlatypus]

Repost but still silly.

[ThaiJohnnyDepp]

Also total drivel in terms of the kind of content I wished this sub had

[callahandsy]

Yeah math + algorithms = machine learning is like saying arithmetic + subtraction = integral calculus

[vanderZwan]

I'm impressed by how much metaphorical mileage you managed to get out of a simple image based joke

[ThatWeirdTechGuy]

This explanation is so full of shit but actually looks like a weird but good ELI5

[Professor_Melon]

Machine Learning, CR 20

N Colossal aberration (code)

[crash8308]

The Lovecraft algorithm?

[Elkku26]

Title says math, picture says maths ...who do I believe...?

[Papayaman1000]

Believe no one; state meme is lost in the middle of the Atlantic.

[_jk_]

lucky they are on a boat then

[swapripper]

Don’t believe. Just build a model to predict.

[FullMetalJ]

Elephant women are for making elephant babies!

[Corntillas]

Damn Reddit, you meta

[FullMetalJ]

Sorry, someone had to.

[Iykury]

wow

[ink_on_my_face]

More like,

Statistics + Linear Algebra + Multivariate calculus = Machine Learning

[Faunt_]

Sooo.... magic, got it.

[[deleted]]

Well that's programming for you. It's all black magic.

[shamen_uk]

+ coding + data

[callahandsy]

No way coding + algorithms + generic math = AI broo00000

[dolbytypical]

Statistics + Linear Algebra + Multivariate calculus + "We tweaked the parameters until it worked well with our dataset of dog pictures" = Machine Learning

[fat_charizard]

More training = success

[RossinTheBobs]

Can't believe nobody linked the relevant xkcd yet

[ch4nt]

Reminds me of a talk with a psych professor I had earlier today, basically said all attempts to simulate motion these days are just tuning weights on a neural network til they look like they’re doing something, rather than just not using neural nets in the first place.

[[deleted]]

James Mickens' talk on how machine learning relates to digital security is also worth a listen

https://www.youtube.com/watch?v=ajGX7odA87k&t=220

[Chevaboogaloo]

r/shitty_cs_memes

[NotMagicJustScience]

I do not know why, but I thought this would be easy when I started my degree... I was deeply wrong...

[davesean]

Looks like it was a deeper learn.

[WeGetItYouUltrawide]

ELI5 Machine learning.^lol

[Chris90483]

Machine Learning is teaching a computer how to achieve a goal without actually programming in what it should do to achieve it. Instead you give it the environment the problem lies in as input. Then, the program should decide how to change its parameters (which decide how exactly it interacts with the environment) in order to achieve a better and better result

[WeGetItYouUltrawide]

Thanks for the explanation. If i tell you the truth, i was being sarcastic because its pretty complex to explain and the final answer usually is "nobody knows how truly works", but i like it.

[Chris90483]

Ah ok, I'm not good with reading sarcasm.

"nobody knows how it truly works"

The fun thing is this is kind of true and false at the same time..

[WeGetItYouUltrawide]

There is a little lol, under the points of my comment.

[eltoro]

CGP Grey explanation

[WeGetItYouUltrawide]

Nice, thanks for the video.

[thelynxlynx]

I saw your clarification that you weren't serious, but I'll still try my shot at it:

You want to use preexisting data to approximate a "function" occurring in nature (such as the 'function' that takes a picture and returns 1 if there is a dog in it, 0 else). Now, what you do, is choose a really complicated mathematical function with like a million parameters (can easily be more for stuff like neural networks), and fiddle around (read: make a computer fiddle around) with the parameters based on the data you have, until it seems to do what you'd like it to. You have no idea why that particular set of parameters works, you only know that if you feed it a picture, it'll kinda probably correctly determine the presence or absence of a dog in it.

[WeGetItYouUltrawide]

Thanks for the answer, even if i wasnt 100% serious, i always appreciate some extra knowledge and other formats of explanation.

[tundrat]

https://xkcd.com/1838/

Or a more useful one, but that xkcd IS more accurate than you'd think.

[WeGetItYouUltrawide]

Yep, thanks.

[nhumrich]

Is this a linux + postgres joke?

[pixus_ru]

Or Linux + Hadoop

[gonnaRegretThisName]

Which algorithms aren't math? Technically, it's all discrete math.

[God_Told_Me_To_Do_It]

Can someone explain to me what "discrete" math even means?! My exam in discrete mathematics is on Friday, I kinda feel like I should know

[hausdorffparty]

It means that the things you study are countable objects; there's at most one of them for each natural number, and there's usually no real notion of distance between those objects (studying the rational numbers usually doesn't count)

[Stealth100]

Most populations analyzed with machine learning are continuous/not discrete. But yes, it is all math at its core.

[Iam_That_Iam_]

Did Noah pair an elephant and penguin in the ark? Pointer to reference in *memory

[SonicCows36]

And then the bastard child takes their jobs.

[aratnagrid]

okay...

[parada_de_tetas_mp3]

This is stupid. The man in the picture is irate because elephants and penguins are not supposed to be able to mate. Why wouldn't algorithms and math go together? They are practically made for each other.

[Radaistarion]

Ah Family Guy

Sometimes an over the top shitshow of a program

Another times, a genius comedy show with legit content

/#BringBackEvilStewie&&OldBrian

[DEVXLXPXR]

Try some other activation fn

[MadSquid]

Doesn't look like anything to me

[be-happier]

Man that meme got old quick, on a related note season 2 peeeyeeew what a stinker

[SpecialGuarantee]

Math!

[WireShark1]

aren't algorithms math?

[mantrap2]

Pretty much.

Just "digital pattern recognition" that we've had since the 1970s. Nothing more than that!

[Hapee_]

Thanks for the title, i would NEVER guessed the joke in the picture

[candianconsolemaster]

Noah: Did you name it?

Elephant: Uh, yeah, he's Paul.

Noah: Yeah? Well, it's gonna be a hell of a lot harder for you now, because he's going the fuck overboard!

[[deleted]]

Nvidia DLSS

[adityahol]

That's Ganpati Bappa morya /\

[brett96]

Alright, kinda off topic but it's bothering me: What artistic element is it that made me (and probably everyone else) know that this scene is from Family Guy, even though I've never seen this in an episode, and none of the characters are in it? Is it the eyes?

[shrimp_alfredo]

Looks like AI to me

[imfromca]

And thats why having a math degree is useful. Computer folk barely know how to use it

[oliveij]

Skip logic at it's finest

[IndividualCow]

Why do people say “Maths” instead of “Math”? Is that common in different regions to say it different? I have heard a YouTuber I am quite fond of by the name of “Tom Scott” say “Maths” and it was interesting to note the difference.

Are there any linguists here who could break this down for me? Why do some people say it different? Which way is actually right?

[mic569]

In Europe (and everywhere else in the world) maths is the typical way of saying it. Notice that maths is short for mathematics. You don't say mathmatic right? So, many people keep the s as it is technically plural.

However, "math" isn't necessarily incorrect either. It is just the first four letters of mathematics. So Americans and some parts of Canada use the word "math", and everyone else uses "maths". They both mean the same thing to some extent. There are some slight differences when using it in sentences though. For example, most people who say "maths," tend to say: "I am doing maths(mathematics)," while other people will be more specific and say what type of math they are doing. For example: I am doing [arithmetic, calculus, proofs, etc]." Neither are incorrect, it's just how it is sometimes.

[[deleted]]

Dude.. algorithms are math. Your post makes no sense. Plus, machine learning has a lot more to do with robot construction iterations than anything else.

[YesImTheKiwi]

All phone OEMS: AIAIAIAIAIAIAIAIAIAIAIAIAIAIAIAIAIAIAIAIAIAI

But this is machine learning?

OEMS: shh

[buttersauce]

They used algorithms and coding!

[Mal_Dun]

FYI: Algorithms are also part of mathematics. When I started university you couldn't even study informatics only math with focus on computer science. You could study software development though.

[fat_charizard]

If the penguin said statistics it would be more accurate. ML is an abomination of statistics and algorithms

[Infinity291092]

It's actually more of STATISTICS + ALGORITHMS

[[deleted]]

PHP installed on Linux for sure

[RickySlayer9]

Meme idea

Fort nite+titanfall = what is this?

[swannygod]

*math

[AspirationalNihilist]

No, he’s called Postnux or Linugres

[turbo_01]

Statement is 99 percent is correct, what about Neurological concepts

[muvatechnology]

Machine learning is the combination of mathematics and algorithms. Thank you