How does this compare to the CS229 lectures by Andrew Ng? (the recorded lectures, not the MOOC)
CS229 @ Stanford is very math/proof intensive. This might be somewhat similar to CS221 or the Coursera course.
Machine Learning Crash Course
131–140 of 233 posts
Re: Machine Learning Crash Course
#132Earlier quoted context omitted.
The other day I met with someone who was visiting my city to attend a big ML conference. In the course of our discussion, it transpired this person did not know the Halting Problem. He'd "heard of" Turing machines, but nothing more than "hearing" of them. Gatekeepers shouldn't keep gates just for gatekeeping sake. But if so-called ML experts don't even know undergraduate computer science, that should really give you…
How does knowing that it's impossible to predict whether an infinite loop exists in a piece of code yield an actionable piece of wisdom that this ML expert should have? I'd suppose that most developers, formal education or not, would have encountered an infinite loop at some point in their initial work with iteration or recursion. How does knowing that Turing proved you can't predict this bug in a piece of code chang…
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Re: Machine Learning Crash Course
#133Earlier quoted context omitted.
What's an example of a problem that needs that troubleshooting? (Curious)
Honestly? The exact problem I'm dealing with at work right now. We're trying to re-write our recommender for artist music stations at iHeartRadio (aka "I'll listen to Drake or Kendrick Lamar's station at the gym today"). Just today, I tried adding negative sampling to the matrix I'm factorizing, hoping it encourages spread in the embeddings learned for artists in certain types of genres. I have a MS, but not a lot of…
That has little to do with a PhD, it's the kind of thing you get with experience leading to a deeper understanding.
3D programming started as a field where only PHD's had any deep understanding of what was going on simply because they had experience when nobody else did. You see this pattern repeated frequently, in any complex domain.
Re: Machine Learning Crash Course
#134Earlier quoted context omitted.
Or you can just say "false negative", and every CS major will understand you. I find people in Math and CS have often very different names for the same type of concepts and they could easy understand each other if they stuck to the more common terms. In this case, saying: TYPE 2 ERROR, makes you look like you are trying too hard.
type ii error is statistics, not mathematics. there is no equivalent concept in CS because type ii error relates specifically to statistical inference and hypothesis testing. that said, if you are just pointing to a box in a confusion matrix and saying "TYPE II ERROR," you are probably trying too hard.
Eh, but if you've taken a machine learning course, you should have seen the notion of false positive/false negative when you cover any kind of classification technique.
Re: Machine Learning Crash Course
#135Looking through the topics covered, the standard AI-course caveats ( https://news.ycombinator.com/item?id=16247629 ) apply. Yes, AI/ML MOOCs teach the corresponding tools well, and the creation of new tools like Keras make the field much more accessible. The obsolete gatekeeping by the AI/ML elites who say "you can't use AI/ML unless you have a PhD/5 years research experience" is one of the things I really hate about…
We have to separate AI researcher and implementation engineer. These types of crash courses help get you to the point where you can reasonably work under PhD level people and write code to test, scale, and deploy their ideas. For many current applications of ML this is acceptable because you're just stealing an idea from a paper or stealing ImageNet to recognize your problem. For anything else you really need to pay…
They really don't. There is a link to the Wikipedia page for matrix multiplication. If these are the people you want to hire, you might as well start outsourcing or generating random numbers
Re: Machine Learning Crash Course
#136For those interested in a deeper dive to just deep learning, "Tensorflow and deep learning - without a PhD" is really good, and covers a lot of material in a single 2hr talk. https://www.youtube.com/watch?v=vq2nnJ4g6N0
+1 for this. Well worth the 2 hours.
Re: Machine Learning Crash Course
#137Earlier quoted context omitted.
Honestly? The exact problem I'm dealing with at work right now. We're trying to re-write our recommender for artist music stations at iHeartRadio (aka "I'll listen to Drake or Kendrick Lamar's station at the gym today"). Just today, I tried adding negative sampling to the matrix I'm factorizing, hoping it encourages spread in the embeddings learned for artists in certain types of genres. I have a MS, but not a lot of…
That has little to do with a PhD, it's the kind of thing you get with experience leading to a deeper understanding. 3D programming started as a field where only PHD's had any deep understanding of what was going on simply because they had experience when nobody else did. You see this pattern repeated frequently, in any complex domain.
Yeah, I expected this reply.
The PhD is sufficient but not necessary here, right? A PhD researcher's job description is basically "learn necessary math, become a domain expert, and publish papers advancing that domain." It's difficult (but possible) to gain the same experience in industry if you don't have a graduate degree. Which company would pay you to work through Bishop or Goodfellow for a few months? Even a principal DS doesn't get that deal, much less a junior/associate.
Also remember: my comment addressed non-vanilla cases. In your example, this is the difference between a researcher advancing 3D programming and someone using Unity or Unreal.
(Also, sorry for all the edits. Done now!)
Re: Machine Learning Crash Course
#138Earlier quoted context omitted.
> The obsolete gatekeeping by the AI/ML elites who say "you can't use AI/ML unless you have a PhD/5 years research experience" is one of the things I really hate about the industry. It is absolutely true that you do not need a graduate degree to apply AI/ML to vanilla problems. It is also absolutely true, in my experience, that you need a graduate-level education or years of hands-on experience to troubleshoot cases…
What's an example of a problem that needs that troubleshooting? (Curious)
ali rahimi alludes to the problem of google engineers simply needing to tweak models that were previously tuned by google researchers who do have well-developed intuition [0]. because the intuitions in explicit form are at best heuristic and not necessarily even consistent, signing up to improve a model without them might result in spending indefinite time and compute resources without guarantee of positive results. which is a terrible perf-theoretic strategy...
Re: Machine Learning Crash Course
#139Earlier quoted context omitted.
The other day I met with someone who was visiting my city to attend a big ML conference. In the course of our discussion, it transpired this person did not know the Halting Problem. He'd "heard of" Turing machines, but nothing more than "hearing" of them. Gatekeepers shouldn't keep gates just for gatekeeping sake. But if so-called ML experts don't even know undergraduate computer science, that should really give you…
Maybe I'm an iconoclast, but I'd respect that person more for not trying to bullshit his way out of it.
That's a great point. It shines a positive light on the gentleman I spoke with, and a negative light on the industry as a whole (if bs is so rampant that merely admitting not knowing something makes someone shine)
Re: Machine Learning Crash Course
#140Earlier quoted context omitted.
"Type I" and "Type II" errors are some of the stupidest and most obfuscatory academic terminology ever invented, and (as an academic) I absolutely refuse to make the effort to learn which way round they go. Just call the bloody things what they are: false positives and false negatives. (Getting seriously OT now, but Kahneman does something annoyingly similar with his talk of "System 1" and "System 2" in Thinking Fast…
What are some synonyms to Kahneman's System 1 and System 2 then? Because Type 1 and 2 errors seem to be completely equivalent to false and positive negatives. I think Kahneman motivates his decision to introduce the terms System 1 and 2 quite well in his book, and I don't know of any direct counterparts.
If the title of his book is justified, maybe the fast and slow systems?