Machine learning

>machine learning

What a fucking joke.

Other urls found in this thread:

arxiv.org/abs/1612.05159
arxiv.org/abs/1701.07875
arxiv.org/abs/1412.6980
youtube.com/watch?v=_PwhiWxHK8o
twitter.com/SFWRedditVideos

>why is this thing more boring than my sci-fi media let me to believe? WAAAAHHH

don't take comp sci

It isn't even that uninteresting. It's just difficult to understand

>people can actually comprehend that mess
>tfw I'm retarded and can't

...

Of course you can't comprehend it if you can't read it

> try a bunch of shit with some hill climbing and regressions thrown in

B R A V O
R
A
V
O

Rollercoaster tycoon

It's just linear algebra.

Any undergrad with a math minor can read it. It's a fucking joke because it's super easy even though people pretend machine learning is anything special, when in fact machine learning is just a meme way of saying you don't suck pajeet dick at designing algorithms.

It's the standard formula for gradient descent, which is also the least intuitive way of describing it possible.

>comp sci meme

>t. undergrad

>which is also the least intuitive way of describing it possible.
It's searching for an extremum is what I get. The formulas themselves are high school level. Two partial derivatives of a very simple function. I have no idea what's behind this, since I don't know much about cs, but the math is relatively easy.

is tgis lambda calculus? i want to learn this.

You don't even need to go to college to understand it honestly. I'm just saying, it's not complicated and neither is machine learning, even though it's this new trendy thing to talk about.

Most ML basically boils down to:
> hill climbing
> modeling things into a search problem
> statistical regression
> trial and error

Yeah pretty much. He's describing how a support vector machine algorithm is designed.

>codemonkeys who want to "learn ML" because of popsci hype then lose interest as soon as they see sigma notation or a partial derivative

It's okay user, not everyone is capable of understanding freshman level math. Maybe you could cheer yourself up by writing a web app with node?

P=NP

What is this symbol supposed to be?

L2 norm of the weights I think

This

>you know, not everyone is a genius like me. maybe you should you should do some retard things?

well id learn to if you posted a guide

It's just applied statistics, it's not hard if you have some knowledge in statistics and math

machine learning is a meme, should rather be called adaptive optimisation or something

There are more forms of machine learning than neural networks.

Decision trees are a simple example that don't rely on optimizations.

Actually you anons have a point, no need to be an elitist dick. If you want to get started, try Andrew Ng's course on coursera, which includes a nice quick intro to the relevant linear algebra. Then do cs231n or course.fast.ai if you like deep learning, or tutorials for kaggle competitions if you want more widely applicable ML skills.

I unironically thought people just built a dumb network, trained it on input until it produced the expected output and things just magically worked without anyone caring how

How is that difficult? Linear regression and backpropagation are literally sub 70 IQ tier

Squared norm of the weights

That's deep learning research, yep

but its still an optimisation problem

give me a hot opinion on genetic algos senpai

yeah welcome to any science major, shits just math get used to it

Applied to what? Depending on the search space you're probably better off with a log grid search, but they can be useful sometimes

that was more or less what i was asking, what are some typical applications
i learnt it at uni and haven't seen anyone actually using it except for basic shit like function generation based on plot and path finding

>computer science is science
B-but I wanna hack people and learn obscure programming languages

>Actually you anons have a point, no need to be an elitist dick.
im making a joke
>L2
anyway i have no idea what this is so how is that elitism?

This is regularization term. Regularization prevents overfitting to training data.

Support Vector Machines - had it two weeks ago - it's fucking simple as fuck. You want to get weights and biases that minimize error function. In order to do that you can use many approaches - i.e. gradient descent or lagrange multipliers. The derivatives dL/dW and dL/db in the picture are used to find the argmin of the cost function.

Fucking easy as fuck, git gud scrub

I meant no need for *me* to be an elitist dick, sorry.

L2 loss is just the square of every element in a matrix. We add this term to the loss function to encourage the model to learn smaller/less noisy weights so it overfits less/generalizes better to data it hasn't seen before

OPs pic is a support vector machine, which are relatively well understood/theoretically grounded.

Deep learning is the bit that's notorious for working extremely well without being well understood, but researchers definitely care how/why it works. I think it's because DL is "joint training of a feature hierarchy" and real world unstrructured data is often hierarchical in nature

Right, sorry. You don't see them used much because they are very close to bruteforcing the search space, and there are very few real world problems where they can be applied naturally.

They are mostly used to find new, more efficient designs for components (like pic related, it's an antenna) or other algorithms (I think there was a NIPS paper about genetically selecting neural networks architectures).
I think Elon Musk/OpenAI is now trying to push a genetic approach to reinforcement learning, but I don't remember where I read it.

OpenAI published a paper on "Evolution strategies" for RL, which is pretty much using GAs to optimise the parameters of a policy network.
It works "only" ~10 times worse than actor critic on atari and has the advantage that it scales better across many nodes than SGD

This. OP is a brainlet who is afraid of numbers and symbol combinations.

Yup. Even a super simple linear regression algorithm is technically "machine learning". Gotta love the names marketers give to these. Then again I am not complaining. Big Data™ and other fancy names will only provide more job openings for people who graduated with a degree in data analytics/operations research.

Hang on, so the stuff I did in matlab for mechanical engineering (e.g. calculate gear changes for a given torque when driving on an incline) is something that could be applied to some flavour of the month computering?

Yes, you're a genius.

Can I suck your dick?

>I know optimization
Do you feel in charge?

>L2
It's a generalisation of distance.
In the real line, the distance between x1 and x2 is |x1-x2|.
In the plane R^2, the (L2) distance between (x1, x2) and (y1, y2) is root[ (x1-y1)^2 + (x2-y2)^2 ].
For R^n the (L2) distance between 2 points A=(x1,x2,...,xn) and B=(y1,y2,...,yn) is
Root[ (x1-y1)^2 + (x2-y2)^2 + ... + (xn-yn)^2 ].

The reason we call it L2 is because you have square root and then a sum of squares under the root.

Similarly you can have L3 distance, by taking cubic root, and sum of cubes (instead of squares) under the root.

You can have L5, L7, etc. distance for every prime. It has to be a prime, i.e. you can't have L4 distance, because to call it distance (formally, norm or metric), it has to satisfy 3 basic properties, and the 3rd property, called the triangular inequality (if we have three points, A, B and C, distance from A to B is smaller or equal than distance from A to C plus distance from C to B), can't be proved unless it's a prime (the 3rd property is proved through an inequality called Minkowski, which is proved by another inequality called Hölder which only works for prime powers).

So L2 means just that: you have a square root, and then some squares inside the root.
This generalises not only for points, but for other objects as well, like functions. The L2 "distance" of 2 (intégrable) functions f, g is root of integral[ |f-g|^2 ].

That's why we call it L2 norm and not just distance, because it's far more general and distance doesn't make much sense for objects other than points.

>this
>difficult

pls ranjeed, stick to java

he doesn't need those blackboards to explain that

that is why there is no efficiency in engineering in these years...

machine learning will never be implemented properly

it will end up as some really gargantuan fucking mess that will just hog data
nothing more

Math requires a lot of studying to understand. You can't just look at it with no background and get it like a super genius in a movie.

Looks like fun to me.

>who is Will Hunting

>draw a line between two values
>machine learning

yeah
>something's supposed to be """"""machine"""""" learning
>still have to learn shit on my own
fucking garbage

first time i hear about the prime requirement for distances
Are there any actual examples for say the L4 norm not fullfilling the triangle inequality?
As a physicist I'd say, "L0, L1, L2, Linfinity works, so everything inbetween must too"

Watch the whole lecture, he explains it really well. You don't need high level math to understand it.

...

we should build a decision tree to help us decide if its a decision problem or an optimization one.

Machine Learning it's just non-parametric statistics.
Now you know

Its math for cute anime face
uwu

I'm pretty sure p just has to be real and greater than or equal to 1 (including infinity) for the Lp distance to be a metric. I don't think p needs to be a prime.

Overrated post.

No it is not.

That's grade school math in Europe.

Stupid Ameridumb.

More like
>le ML is le future of le singularity because le /r/futurism said so!!!!
>learn ML
>it's fucking multivariable optimization by randomized brute force
Fucking brace for the singularity man, gone hill climbing with the Kurzweil holy shit I can see forever

Basically this.

so how do i read those chinese symbols and do math fags use those just to troll normies?

made-up faggot

>gradient is bruteforce
...........................

That's literally how it works in practice. Basically, ML engineers (not scientists) do exactly that all day. Scientists do stuff like
arxiv.org/abs/1612.05159
arxiv.org/abs/1701.07875
arxiv.org/abs/1412.6980
for example.

Cry all you want. The rest of us will laugh all the way to the bank.

>math is a buzzword because even 1 + 1 = 2 is called math!!11

Nobody uses non-parametric models in ML because they suck balls. The more you know (tm).

the point is, it's not a bruteforce

>can't tell the difference between computer science and quantum mechanics

If you cannot understand that, you're under 14.

sir delete that right now sir

what video?

I bet you feel proud posting that.

youtube.com/watch?v=_PwhiWxHK8o

ML is the only hope I have left for an interesting CS job, as otherwise most of jobs are just dumb CRUD codemonkey stuff in language X

LITERALLY based on a true story faggot

ty

As based on a true story as fargo season 2's last episode.

I'm a mathfag and machine learning triggers me hard.
It's literally just CSfags discovering statistics and applying some algorithms and believing they invented a new field.

CS is just a subset of mathematics, I don't know why you're surprised.

ITT CS undergrads pretending to understand courses they haven't taken yet

we /sci/ now

OwO

I have a master's degree in machine learning and I work for microsoft in the business AI group (although I do research, not business research nor engineering).

/sci/ never was that smart.
Its just fedora tipping atheist edgelord bronies over there.

>mfw /mlg/ always dies with like 5 posts
>this shitty start to a ML thread is actually working

but what worth is reading this drivel anyways?

if you wanted a ML general, then people would actually have to be doing anything with ML and not just post their hot opinions.

one of the hardests part is understanding the Symbols and each variable, once you get that done, it's so easy anyone can do it, but it's esoteric to anyone that doesn't sit around doing this specific breed of math.
Even basic Electrical engineering and physics looks hard because of the esoteric naming and variables with subsets.

Let me tell you that Machine Learning is going to be the buzzword of the next 5 years. Any dickhead that writes any statistical determination functionality in their site with even baby tier analytics data will say how their site uses "Advanced Machine Learning Algorithms" you can bet your ass on it... and this will range from literally basic tier stats to DNN and you'll never know which (until DNN becomes buzzword tier)

because anyone who knows anything about ml or ai in general avoids these cancerous /x/ + anti-sci threads like the plague.

what is OP's formula describing? i'm assuming many of the letters are placeholders for different values

it's a soft-margin svm. x is the input vector, y is the output target, w is the weight matrix, b is the bias vector, alpha is the margin hyperparameter. Top row is the loss to be minimized, middle row is the gradient with regard to it in the weights direction, bottom row is likewise but for the bias vector.

what?

ITT: Sup Forums pretends it knows stuff