I created nearly the same presentation this week. It's good to see that I didn't miss much, though this one goes deeper, which I don't do on purpose. I'll probably send the attendee this presentation afterwards for those who want to go deeper, very cool!
Machine Learning 101 slidedeck: 2 years of headbanging, so you don't have to
51–60 of 123 posts
Re: Machine Learning 101 slidedeck: 2 years of headbanging, so you don't have to
#52Earlier 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 sorta draw the line at autoplaying music. Apart from that, he's done a good job. How many of us are bold enough to put long list of glowing reviews on our resume?
Re: Machine Learning 101 slidedeck: 2 years of headbanging, so you don't have to
#53As 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.
Re: Machine Learning 101 slidedeck: 2 years of headbanging, so you don't have to
#54As 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…
> Fortunately, there are new automated tools today that can do that automatically.
can you please elaborate?
Re: Machine Learning 101 slidedeck: 2 years of headbanging, so you don't have to
#55As 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 error rate during identification. However, if we were able to extract speed and trajectory of all objects in the picture and mixing them with AIS data, finding which ones are ships, which ones are giant pieces of ice, and which one are non-moving structures to be avoided, becomes easy.
So, you have to choose between a black box that will give you potential results with a given error-rate, and a predictable algorithm that anyone can audit. Seems like a no-brainer situation to me. For what other reason would you choose the first solution, except hype-related decisions ?
Re: Machine Learning 101 slidedeck: 2 years of headbanging, so you don't have to
#56Thank you for this. This is an excellent slide deck
Re: Machine Learning 101 slidedeck: 2 years of headbanging, so you don't have to
#57How can I download this slide?
Going to the /export/pdf link shows access denied. My quick 3min workaround was to use FF dev tools, settings enable screenshot, click through and save each page.
Re: Machine Learning 101 slidedeck: 2 years of headbanging, so you don't have to
#58Nice introduction, but I really don't see how "2 years of headbanging, so you don't have to" applies.
Re: Machine Learning 101 slidedeck: 2 years of headbanging, so you don't have to
#59Earlier 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/…
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.
Re: Machine Learning 101 slidedeck: 2 years of headbanging, so you don't have to
#60As 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…
The thing is if you know exactly what you are looking for, like in your example, or a QR code, or a barcode, it makes sense to tailor an algorithm. But you may not want to have to maintain a complex algorithm every time a small change happens (say new kind of ships appear). Or you might want a generic approach (recognise any objects, including objects that did not even exist at the time the code is written, but will appear in the data). In such case I can see ML being a good choice.