Hacker News Board Hacker News, browsed like a classic message board
Live data from Hacker News

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

docs.google.com

Post reply on HN
Print view View original

81–90 of 123 posts

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

#81

Post by 60654 »

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 ?

AI is the overall field and the superset of all of the various approaches.

ML is one family of approaches for knowledge acquisition in AI, but far from the only one (eg. logic based inference is another big one).

DL is a family of approaches in supervised ML. As the author points out, it's a subset of a subset.

But saying that this sub-subset "drives" AI is like saying endocrinology "drives" medicine: not the right mental model at all.

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

#83

Post by tudelo »

Pretty similar information to my semester-long Machine Learning class in undergrad (with less detail). Good information to get the basics down, but can't say I've gone to apply any of the information I have learned yet... Still a useful set of slides.

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

#84

Post by dkersten »

nblavoie wrote: post #13

The document is awesome, but the animated backgrounds are distracting.

Completely agree. The movement in the animations keeps grabbing my attention. The first slide took multiple attempts to read without getting distracted. Maybe my attention span is just bad, but I really want to understand the slides! :(

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

#85

Post by npgatech »

jasonkester wrote: post #31
npgatech wrote: post #28

Earlier quoted context omitted.

Checking his website, it reeks of narcissism. There are better ways to assert yourself than to do all the corny things he has done on his self promotion website.

Are you honestly slagging a guy off for talking about himself on his resume ??? I mean yeah, we computer folk are supposed to be all self deprecating and all. But if there is one place we should stop mumbling and talking ourselves down for a second, that is it. At some point if you want people to know what you do, you're going to have to tell them.

I found his approach tacky, loud and insincere.

Of course you should be talking about yourself on your resume but a couple of this that are different here:

- Wtf is up with music - 51%/49% thing. - Publicly asking to be hired that reflects poorly on his current job at Google. - Excessively loud self marketing

why not have a simple site with your accomplishments? Why all the excess bullshit?

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

#86

Post by npgatech »

thenomad wrote: post #66
npgatech wrote: post #28

Earlier quoted context omitted.

Checking his website, it reeks of narcissism. There are better ways to assert yourself than to do all the corny things he has done on his self promotion website.

This is a deeply unfair, unreasonable and arguably abusive comment. It's entirely reasonable to talk about yourself and your achievements on your resume, and Mr Mayes' site is rather a good example of doing so.

No one is talking about resume. I’m talking about his approach.

If I see this kind of resume land or my desk, it gets thrown out.

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

#88

Post by nlowell »

60654 wrote: post #81
acdanger wrote: post #75

Earlier quoted context omitted.

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

AI is the overall field and the superset of all of the various approaches. ML is one family of approaches for knowledge acquisition in AI, but far from the only one (eg. logic based inference is another big one). DL is a family of approaches in supervised ML. As the author points out, it's a subset of a subset. But saying that this sub-subset "drives" AI is like saying endocrinology "drives" medicine: not the right m…

If endocrinology was the most talked-about, hyped, and invested-in form of medicine I think it would be fair. DL is where an enormous amount of AI growth and progress is.

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

#89

Post by bitL »

384028345 wrote: post #59
agibsonccc wrote: post #41

Earlier quoted context omitted.

I both make most of my money from time series data and use deep learning and work with data with no labels. Here's a recent presentation I did on some of this work and a companion presentation I encourage people to read on how to use this effectively in production. While you are right that some feature engineering is needed, there's no reason DL can't be a part of your workflow. https://www.slideshare.net/agibsonccc/…

Thanks for the info! The book looks interesting. Do you have an opinion on the fast.ai and deeplearning.ai courses? I finally have some time to work through these and since the deeplearning.ai series starts on December 18th, I'm wondering which one to dive into since I can't tell from the outside how they compare.

Add Udacity's DLF ND to the mix and do all 3 of them, they are all a bit different. Udacity's one has the inventor of GANs doing lectures there, so it's pretty top notch as well.

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

#90

Post by qazpot »

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.

Can't download the PDF as well.
Post reply on HN