171–180 of 233 posts
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I can't help but think in 3-5 years how quaint our tools of the day will seem.
I think about this constantly. Not to sound like I walked uphill in both directions back in my day or something, but I remember building models in numpy without pandas. It was tedious -- and that's just a nice API wrapping ndarrays!
Local minima?
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i would request that you stop critiquing "machine learning" based on the presentation in introductory online materials like this and the ng coursera course. you provide a lot of signal in general but i think these critiques do decrease your SNR. i am certain that you are familiar with the "usual" statistics sequence. (for others: there are lower-division courses that use calculus in a few places but otherwise avoid i…
If you find something wrong mathematically or otherwise with something I write, then by all means let me know. So far you have found nothing. Details: The Google statement I quoted was flatly wrong. It is really important for students to be told that. I gave some references to more in statistics. > the measure-theoretic issues I didn't mention measure theory, and the statistics references I gave don't mention measure…
i made my request because it's jarring for me as a reader when you punctuate your (often delightful) expository writing with conclusions about entire fields and large organizations that seem (on the face of it) to be justified by old and/or very limited data.
but that's a selfish request, and you are of course free to tell me to get lost and post whatever you want (and i'll still read it); i'm certainly not going to pursue this further, aside from the apology and clarifying comment above.
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> 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)
If you take some sophisticated deep neural net and try to train it on a binary classification where tails occurs 99% of the time - unless you specifically take measures to correct for this bias - the net will just learn to predict tails.
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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…
Aren't you sort of glossing over the fact that he is in high up machine learning position at a company that specializes in recommender systems? Doesn't that by itself increase the likelihood that he deeply understands implicit and explicit matrix factorization? I am a good ways through my masters (second CS degree, first specializing in ML), and the more I learn, the more I realize that on any given topic, there is n…
Sure thing, but someone in that position needs years of experience in recommender systems, as well as working with researchers.
Folks are hanging on to the PhD part of my claim, instead of the "PhD or experience" part. The fact is, a PhD + prior industry work means the person is close to a decade of relevant background, grad degree or not. They will unstick a co-worker far faster than an experienced backend developer with, say, a year of Keras experience.
> Side note: sounds like we do similar work. I work at Vevo, also do a lot of matrix factorization to learn latent factors of items such as artists, videos, etc.
Seems like it! Email me if you'd like to chat some more offline (it's in my profile).
Any book suggestions to getting up to speed in this area?
Looking 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…
Which phd, though? All PhDs are not equal (see politics vs computer vision). Also, PhDs are hardly the holy grail of demonstrating capability, accuracy or intellect, especially given the reproducibility crisis, phds as a measure of any of those things should be used carefully.
we recommend that students meet the following prerequisites:Mastery of intro-level algebra. You should be comfortable with variables and coefficients, linear equations, graphs of functions, and histograms Any book suggestions to getting up to speed in this area?
we recommend that students meet the following prerequisites:Mastery of intro-level algebra. You should be comfortable with variables and coefficients, linear equations, graphs of functions, and histograms Any book suggestions to getting up to speed in this area?
Khan Academy probably has this covered.
we recommend that students meet the following prerequisites:Mastery of intro-level algebra. You should be comfortable with variables and coefficients, linear equations, graphs of functions, and histograms Any book suggestions to getting up to speed in this area?