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

Post by jasonkester » Thu, Dec 14, 2017, 6:52 AM UTC

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.

Post by vowelless » Thu, Dec 14, 2017, 7:10 AM UTC

Post by peterhunt » Thu, Dec 14, 2017, 7:22 AM UTC

bitL wrote: post #30

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…

Deep Learning frees you from the need to do "feature engineering" and usually works much better than methods which require such process. I'd instead recommend everyone to "dive deep" into deep learning and once they master it, get acquainted with classical methods that still might get used here and there. I understand it's difficult to let go of what you worked very hard to understand when you were studying ML, but s…

Post by fnl » Thu, Dec 14, 2017, 7:29 AM UTC

peterhunt wrote: post #35

bitL wrote: post #30

Earlier quoted context omitted.

Deep Learning frees you from the need to do "feature engineering" and usually works much better than methods which require such process. I'd instead recommend everyone to "dive deep" into deep learning and once they master it, get acquainted with classical methods that still might get used here and there. I understand it's difficult to let go of what you worked very hard to understand when you were studying ML, but s…

This might be true for CV and speech recognition and synthesis, but there are huge categories of problems (dare I say, the majority of industry use of ML) that are either working with time series data (which DL hasn’t had great success with) or must be highly explainable and tunable.

Post by carlmr » Thu, Dec 14, 2017, 7:34 AM UTC

XR0CSWV3h3kZWg wrote: post #3

I really wish that they hadn't decided on having moving images behind the text you are supposed to be reading.