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JewKiller 3000 posted:why? what is your motivation for learning this thing? what if i told you that behind all the fancy math, it is mostly horseshit, and people will move on to the next thing within 5 years or so once all the promises don't pan out? Who cares. qhat fucked around with this message at 15:05 on Aug 22, 2017 |
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Aug 22, 2017 07:27
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like jerking off isn't like a long term vision thing but still worthwhile
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Aug 22, 2017 09:25
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read perceptrons by marvin minsky op i am persuaded that machine learning fell out of favor just long enough to forget about the lessons of that book, and the rather fundamental lessons it offers are not getting the respect they deserve with the claims made about ai today
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Aug 22, 2017 13:17
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qhat posted:Who cares. obviously not you!
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Aug 22, 2017 21:30
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I'm going to create an AI that auto blocks everyone with names like "JewKiller 3000"
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Aug 22, 2017 21:43
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i look this course a couple years ago and aced it. i still don't feel like i know anything. at the time it was cool, but i guess i instantly forgot everything
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Aug 22, 2017 21:47
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Deep Dish Fuckfest posted:it's vector calculus and linear algebra all the way down
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Aug 22, 2017 21:48
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go buy a book on linear algebra and study it, then go through whatever /r/machinelearning are wanking to at the moment machine learning is so hot right now everyone and their mum have books out on the topic
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Aug 22, 2017 21:54
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Deep Dish Fuckfest posted:it's vector calculus and linear algebra all the way down one of the hardest parts after part two is convincing yourself this model you've built with garbage data is somehow good because it was a lot of garbage
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Aug 22, 2017 21:55
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echinopsis posted:like jerking off isn't like a long term vision thing but still worthwhile 
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Aug 22, 2017 22:08
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i just want to know how to make a program that makes stuff like this https://www.youtube.com/watch?v=pgaEE27nsQw
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Aug 22, 2017 22:44
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OP just watch this video that doesn't explain how to do anything: https://www.youtube.com/watch?v=wbRx18VZlYA
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Aug 22, 2017 23:08
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HoboMan posted:i just want to know how to make a program that makes stuff like this desperately the same
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Aug 22, 2017 23:16
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HoboMan posted:i just want to know how to make a program that makes stuff like this https://www.youtube.com/watch?v=itACOKJHYmw https://www.youtube.com/watch?v=ngZ0K3lWKRc AlphaKeny1 fucked around with this message at 23:20 on Aug 22, 2017 |
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Aug 22, 2017 23:18
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JewKiller 3000 posted:why? what is your motivation for learning this thing? what if i told you that behind all the fancy math, it is mostly horseshit, and people will move on to the next thing within 5 years or so once all the promises don't pan out? deep learning is getting p. close to the real deal
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Aug 23, 2017 04:34
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in the same sense that me taking a steaming poo poo is p close to launching a rocket
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Aug 23, 2017 05:40
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yeah um ask any phd who focused on "deep learning" and they'll tell you that noooooooooooope
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Aug 23, 2017 05:42
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HoboMan posted:i just want to know how to make a program that makes stuff like this get an army of grad students to grind to death
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Aug 23, 2017 06:28
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Cybernetic Vermin posted:read perceptrons by marvin minsky op oh another good suggestion like the machine learning world is just now rediscovering the utility of backprop
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Aug 23, 2017 06:56
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Deep Dish Fuckfest posted:it's vector calculus and linear algebra all the way down except when it's symbol and list manipulation all the way down
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Aug 23, 2017 06:59
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what do you mean just now? 
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Aug 23, 2017 07:05
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is this the poo poo that puts hosed up dogs in poo poo or makes porn look like a van gogh
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Aug 23, 2017 07:07
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I'm not asking to simulate a human brain. I just want to do interesting data analysis.
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Aug 23, 2017 15:02
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you don't even need to "know" machine learning to do that; just get some libraries and learn the basic strengths and weaknesses of the methods contained within and how to tweak hyperparameters all day i mean that's half of what kaggle participants do and people think they're geniuses
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Aug 23, 2017 15:10
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JewKiller 3000 posted:yeah um ask any phd who focused on "deep learning" and they'll tell you that noooooooooooope Bloody posted:in the same sense that me taking a steaming poo poo is p close to launching a rocket i dont mean 'it's gonna give us general ai' which is of course absurd but for places where it's applicable (tons and tons of structured data + right kind of problem) it's incredible (i've been doing ml-adjacent stuff for 15-ish years now and was p. much ignoring dnn until about a year ago, but i'm super bullish on it now) also one huge diff. between dnn and older techniques is that even as horrible as they are the frameworks out there (and pre-trained models) make it possible for even non-technical javascript developers to do useful things with them
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Aug 23, 2017 15:48
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lancemantis posted:you don't even need to "know" machine learning to do that; just get some libraries and learn the basic strengths and weaknesses of the methods contained within and how to tweak hyperparameters all day a lot of kaggle people do post-facto writeups: https://www.kaggle.com/kernels as well as general here's-how-i-did-it, including this recent winner : https://www.kaggle.com/jamesrequa/keras-k-fold-inception-v3-1st-place-lb-0-99770
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Aug 23, 2017 15:55
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fritz posted:non-technical javascript developers please don't remind me about our industry's #1 problem
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Aug 23, 2017 17:04
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really i am very interested in the math but everything glosses over it as hard as it can so as soon as i start following a tutorial i don't understand what i am doing anymore like cool, magic, i made a thing that very narrowly accomplishes whatever the tutorial set as the task, but i want to know the theoretical stuff and not cast computer spells
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Aug 23, 2017 17:13
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fritz posted:a lot of kaggle people do post-facto writeups: https://www.kaggle.com/kernels as well as general here's-how-i-did-it, including this recent winner : https://www.kaggle.com/jamesrequa/keras-k-fold-inception-v3-1st-place-lb-0-99770 that write-up sucks as far as explaining why he did anything. also all his example outputs are just a bunch or warning and error messages?
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Aug 23, 2017 17:15
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HoboMan posted:all his example outputs are just a bunch or warning and error messages? that's like the default jupyter output for some horrible reason, run that stuff locally and it "should" work he went into a little more detail on the kaggle forums : https://www.kaggle.com/c/invasive-species-monitoring/discussion/38165 but i think for the 'why to do it' the best way might be just to read a whole bunch of kaggle forum posts and blogs and stuff
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Aug 23, 2017 17:28
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HoboMan posted:really i am very interested in the math but everything glosses over it as hard as it can so as soon as i start following a tutorial i don't understand what i am doing anymore all 3 semesters of calculus; make sure you can do vector/gradient stuff calculus-level linear algebra *variate statistics, calculus level a bunch of other esoteric poo poo the thing is the field is multi-discipline; some methods were created by statistics researchers, others by computer scientists, social and physical scientists, and various other branches of mathematics i mean algebraic topology researchers are contributing to this stuff
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Aug 23, 2017 17:34
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read and do most of exercises in The Elements of Statistical Learning, free at https://web.stanford.edu/~hastie/Papers/ESLII.pdf hope you remember a lot of the aforementioned linear algebra
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Aug 23, 2017 17:34
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Lysidas posted:read and do most of exercises in The Elements of Statistical Learning, free at https://web.stanford.edu/~hastie/Papers/ESLII.pdf that is such a painful book written for and by statisticians
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Aug 23, 2017 17:37
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though I did enjoy the use of the screaming man head for the "difficult passages" and their accommodation of the colorblind 
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Aug 23, 2017 17:47
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eschaton posted:except when it's symbol and list manipulation all the way down maybe the truth is in the middle
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Aug 23, 2017 18:07
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lancemantis posted:all 3 semesters of calculus; make sure you can do vector/gradient stuff check, check, never did any stats stuff, check lomarf
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Aug 23, 2017 18:33
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HoboMan posted:really i am very interested in the math but everything glosses over it as hard as it can so as soon as i start following a tutorial i don't understand what i am doing anymore mike nielson's "book" (can be read through in probably a day or two) is pretty good: http://neuralnetworksanddeeplearning.com/ that said, most novel things with neural nets these days rely on torch/tf/theano to do the derivatives. i rarely deal with theoretical issues outside of "make the hidden activations and gradients closer to 0-mean 1-var" when doing something new
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Aug 23, 2017 19:41
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lancemantis posted:that is such a painful book written for and by statisticians yeah it definitely is a slog in a lot of places but i got a good amount out of it should reread some parts of it; its been a while
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Aug 23, 2017 19:43
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HoboMan posted:really i am very interested in the math but everything glosses over it as hard as it can so as soon as i start following a tutorial i don't understand what i am doing anymore Those Coursera videos are good and they seem to go over the essentials. Understanding what's actually going on underneath the surface of svd() or whatever is what I want.
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Aug 24, 2017 02:04
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normally wikipedia math is incomprehensible, but the article for singular value decomposition actually seems pretty good. read that, and by "read" i don't mean read the words and skip over the equations like i always do. you have to go through the equations symbol by symbol and make sure you understand what they're saying. when you see a math term whose definition you don't know, click on the term and read that article too, in the same fashion. if the article is too obtuse, look up the term in your linear algebra textbook instead. note that this process will probably have you (re)learn most of linear algebra, but it sounds like that's exactly what you're asking for, and there isn't really any shortcut to it
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Aug 24, 2017 03:08
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