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Machine Learning 101 slidedeck: 2 years of headbanging, so you don't have to

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Re: Machine Learning 101 slidedeck: 2 years of headbanging, so you don't have to

#71

Post by nerdponx »

xzzherculeszzx wrote: post #20
kmax12 wrote: post #19

As someone who works with a lot of people new to machine learning, I appreciate guides like this. I especially like the early slides that help frame AI vs ML vs DL so that people can have a realistic understanding of what these technologies are for. For my part, one of the biggest realization I had after many years of applying machine learning was that I got too caught up in the machine learning algorithms themselves…

What are best resources for "defining and generating" labels? Any recommendations?

You need a good random sample and lots of manpower to manually label them. Mechanical Turk [0] is one place to go for that man power if you don't have a "grunt work" team and are not willing to spend a few days doing it yourself.

There are also some methodologies out there that can help you label data sets more efficiently. I don't often see them used, but they exist. Look up "active learning" and "semi-supervised learning".

[0]: http://nlp.cs.illinois.edu/HockenmaierGroup/Papers/AMT2010/W...

Re: Machine Learning 101 slidedeck: 2 years of headbanging, so you don't have to

#72

Post by nerdponx »

mrweasel wrote: post #67

I honestly don't really see the value in a slidedeck, without the accompanying talk. It's the same as when someone proclaim: "Slides from this talk is available online", yeah that's not really any good without video or audio.

Disagree. Slides, even out of context slides, can be an excellent source of information for people new to a field: they still give a sense of the structure of the talk (what is related to what), and they give you keywords to start searching for. And for experienced readers, sometimes they just contain nice ideas or tips you had not been aware of.

Re: Machine Learning 101 slidedeck: 2 years of headbanging, so you don't have to

#73

Post by JepZ »

quacker wrote: post #44
kbart wrote: post #34

Information is great, but it would be much more readable in simple text form or pdf. It's strange that senior creative engineer at Google doesn't know presentation making basics.

It's not surprising that a Google engineer would use Google docs. It's at least easily shareable and there are complementary embedded videos that aren't suitable for text/PDF anyway. Though, the options to export as a PDF didn't work for me (either via download or as an export to Google Drive). I'm assuming the presentation is too big.

Same here, just tried to download the PDF multiple times but it always failed (didn't even start; tested Chrome and Firefox)...

Re: Machine Learning 101 slidedeck: 2 years of headbanging, so you don't have to

#74

Post by radarsat1 »

Fiahil wrote: post #55
kmax12 wrote: post #19

As someone who works with a lot of people new to machine learning, I appreciate guides like this. I especially like the early slides that help frame AI vs ML vs DL so that people can have a realistic understanding of what these technologies are for. For my part, one of the biggest realization I had after many years of applying machine learning was that I got too caught up in the machine learning algorithms themselves…

Don't you think people are, sometimes, just applying ML to their problem "because of hype" ? One example I have in mind, was a contest where participants were given a series of satellite pictures and asked to write a classifier to detect icebergs and cargo ships (the two are quite similar). As someone else pointed out, trying to use classical computer vision and machine learning on these images will always have some…

Your comparison seems like a false dichotomy, and I think you are agreeing with OP. OP says, spend less time worrying about the algorithm and more time worrying about what data you are feeding the algorithm. You are saying, what if you had to choose between dataset A with algorithm A and dataset B with algorithm B.

You claim, (probably correctly) that dataset B, which includes velocity and trajectory, is more correct for the problem at hand, and given dataset B, I would suggest that either algorithm A or B would probably do just fine.

You also claim that algorithm A has "some error rate during identification." But so will algorithm B, and so will either algorithm on dataset A and B!

The question you should ask is, how much do I care about "black box" vs. "white box", and is there are trade-off? If the black-box solution (algorithm A, the "ML" solution) gives you 10% higher accuracy, and that accuracy is going to save lives, you bet I'd choose it. Or maybe I decide that interpretability is really important due to external audit reasons, so I need the white-box solution. But maybe I'd choose both, the interpretable one, and use the uninterpretable one as a flag for "a human should look at this." Or maybe I'd combine the results of both algorithms to get even higher accuracy.

There are just so many ways to configure a solution to the problem you propose, and you are only distinguishing between two of them. In the end the appropriate choice depends on context.

Re: Machine Learning 101 slidedeck: 2 years of headbanging, so you don't have to

#75

Post by acdanger »

jmmcd wrote: post #53
kmax12 wrote: post #19

As someone who works with a lot of people new to machine learning, I appreciate guides like this. I especially like the early slides that help frame AI vs ML vs DL so that people can have a realistic understanding of what these technologies are for. For my part, one of the biggest realization I had after many years of applying machine learning was that I got too caught up in the machine learning algorithms themselves…

> I especially like the early slides that help frame AI vs ML vs DL so that people can have a realistic understanding of what these technologies are for. But they're wrong! I read "Deep learning drives machine learning which drives artificial intelligence." This is very wrong. I stopped reading.

How is it wrong? What's the correct hierarchy ?

Re: Machine Learning 101 slidedeck: 2 years of headbanging, so you don't have to

#76

Post by cyberpunk0 »

nerdponx wrote: post #72
mrweasel wrote: post #67

I honestly don't really see the value in a slidedeck, without the accompanying talk. It's the same as when someone proclaim: "Slides from this talk is available online", yeah that's not really any good without video or audio.

Disagree. Slides, even out of context slides, can be an excellent source of information for people new to a field: they still give a sense of the structure of the talk (what is related to what), and they give you keywords to start searching for. And for experienced readers, sometimes they just contain nice ideas or tips you had not been aware of.

No they aren't. Slides are a skeleton. Just bullet points that 99% of the time offer no usable information

Re: Machine Learning 101 slidedeck: 2 years of headbanging, so you don't have to

#77

Post by levesque »

TheNewLab wrote: post #58

Nice introduction, but I really don't see how "2 years of headbanging, so you don't have to" applies.

There is no quick way to get a good grasp on ML. You just need to spent the time needed to get there, reading and working on simple problems, carefully validating that you understand concepts as you go. It's like asking for a way to learn mathematics or computer programming in an hour. Hint: there isn't.

Re: Machine Learning 101 slidedeck: 2 years of headbanging, so you don't have to

#78

Post by kovek »

acdanger wrote: post #75
jmmcd wrote: post #53

Earlier quoted context omitted.

> I especially like the early slides that help frame AI vs ML vs DL so that people can have a realistic understanding of what these technologies are for. But they're wrong! I read "Deep learning drives machine learning which drives artificial intelligence." This is very wrong. I stopped reading.

How is it wrong? What's the correct hierarchy ?

My understanding is that Deep Learning is a type of Machine Learning. Artificial Intelligence is the idea that a machine performs similarly or better than a human for a specific task. Artificial General Intelligence is when a machine performs similarly to a human in many different kinds of tasks.

Re: Machine Learning 101 slidedeck: 2 years of headbanging, so you don't have to

#79

Post by [deleted] »

jmmcd wrote: post #53
kmax12 wrote: post #19

As someone who works with a lot of people new to machine learning, I appreciate guides like this. I especially like the early slides that help frame AI vs ML vs DL so that people can have a realistic understanding of what these technologies are for. For my part, one of the biggest realization I had after many years of applying machine learning was that I got too caught up in the machine learning algorithms themselves…

> I especially like the early slides that help frame AI vs ML vs DL so that people can have a realistic understanding of what these technologies are for. But they're wrong! I read "Deep learning drives machine learning which drives artificial intelligence." This is very wrong. I stopped reading.

[deleted]

Re: Machine Learning 101 slidedeck: 2 years of headbanging, so you don't have to

#80

Post by wenc »

graycat wrote: post #61
QuasiAlon wrote: post #54

Earlier quoted context omitted.

> Fortunately, there are new automated tools today that can do that automatically. can you please elaborate?

One very old tool for such things was called "stepwise regression". IIRC J. Tukey was partially involved in that. It appears that the AI/ML work is close to the regression and curve fitting going back strongly to the early days of computers in the 1960s and a lot in the social sciences back to the 1940s and even about 1900. A lot is known. E.g., there's the now classic Draper and Smith, Applied Regression Analysis .…

Stepwise regression is not to be recommended because it's very easy to fool oneself.

http://www.sascommunity.org/mwiki/images/e/e2/NYASUG-2007-Ju...

http://www.barryquinn.com/the-statistical-dangerous-of-stepw...

Shrinkage methods like lasso/elasticnet are less susceptible to these problems.

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