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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

#91

Post by username223 »

npgatech wrote: post #28
rubenfiszel wrote: post #8

If I understand correctly those are slides from a Googler (Not sure if those slides have corporate approval), that probably have as a side goal to showcase that Google is a fun place to do ML. Not that I am judging or anything but, the author's personal website http://www.jasonmayes.com/ whose link is displayed multiple times is a giant ad to get hired elsewhere and show at least some desire for other career opportun…

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.

Wow, that site is extremely weird and off-putting. Scroll down to the section "What are people saying about me?" to read what I can only assume is his friends being asked to write promotional blurbs about him. I can understand putting your best foot forward on your résumé, but this is something else.

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

#92

Post by achompas »

minimaxir wrote: post #6

The presentation goes straight from from linear regression and classification to computer vision and reinforcement learning . The practical value of ML/AI is what’s in between and is something that isn’t often discussed between all the hype. ML/AI can be used to build models which work well with nontabular data (e.g. text and images), and can solve such regression/classification problems more cleanly. (and with tools…

Sorry, where do you see this? I see a lot of slides devoted to the "in-between" of random forests, perceptrons, etc. The jump from supervised to unsupervised to RL also makes sense, since RL is a different learning paradigm from the other two.

I'm as exhausted of the ML hype as anyone else, but I believe this deck tempers expectations.

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

#93

Post by tapatio »

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 want my next side project to use machine learning. Not sure what to do though. Thanks for posting your project. I'll be checking it out. Feature engineering sounds interesting.

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

#95

Post by fnl »

agibsonccc wrote: post #41
fnl wrote: post #37

Earlier quoted context omitted.

Or you don't have millions of annotated examples to learn from, and no similar problem to transfer from...

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/…

> While you are right that some feature engineering is needed, there's no reason DL can't be a part of your workflow.

I (and, I believe, the earlier poster, too) never implied you can't use deep learning on such examples. What we (I think, both) were referring to was the claim that it would absolve you from feature engineering. (Which I understand you also refute.)

> For more of the basics, my book on deep learning might help as well

Congratulations on your book, I know how much hard work that is!

Disclaimer: I make money with deep learning, too... ;-)

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

#97

Post by fnl »

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.

While I agree with others that more is better, if you can take only one course, I strongly recommend taking Andrew Ng's. While it is true that you don't need to be able to design and understand 'nets from scratch to be able to use them, I agree with most of the brightest minds in DL that you won't get too far if you don't at least have an intuition for the math behind it. And Ng's course really only gives you that - an intuition. It does an excellent job at ensuring participants understand the bare minimum to do any kind of serious work. Learning ${your favorite framework}'s API will be a breeze if you understand the "why" already.

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

#99

Post by fnl »

natch wrote: post #43
vowelless wrote: post #33

Slide 64: A whole tonne of stuff going on in robotics right now. Just take a look at Boston Dynamics YT channel for some mind bloding research, most of which is driven by ML. . I highly doubt that BD is doing any ML work right now ... Can the author link to specific research that they are doing using ML?

You mean public work perhaps? I imagine they are doing a lot with vision, gait learning, object manipulation, task planning, autonomy, multi-robot coordination, etc. all of which can be enabled by or at least helped along by machine learning, no? Your request for links is valid, I just am surprised anyone would doubt that they are doing ML research unless you are thinking of a strangely narrow definition of ML.

Most of robotics is about reinforcement learning.

EDIT: Oh, and expert systems/rules. Lots of em.

EDIT2: Well, an engineering, obviously... :-) Heck, just check Wikipedia on the topic...

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

#100

Post by ratsimihah »

qazpot wrote: post #90
quacker wrote: post #44

Earlier quoted context omitted.

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.

This might be intended behavior. If you try to print the deck, it shows the Goole Drive access request page.
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