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Machine Learning Crash Course

developers.google.com

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191–200 of 233 posts

Re: Machine Learning Crash Course

#191

Post by noobermin »

bluetwo wrote: post #99
achompas wrote: post #59

Earlier 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…

I can't help but think in 3-5 years how quaint our tools of the day will seem.

Not really. I see things like leftpad and npm fails and CEOs mailing private keys they've stored from customers. I see the same lessons we have have to re-learn year after year.

Re: Machine Learning Crash Course

#193

Post by jebediah »

deviationblue wrote: post #176
cowmoo728 wrote: post #51

Earlier quoted context omitted.

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…

>you can reasonably work under PhD level people and write code to test, scale, and deploy their ideas. 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.

They are talking about PhDs in Machine Learning of course

Re: Machine Learning Crash Course

#194

Post by recursivelambda »

achompas wrote: post #137
Retric wrote: post #133

Earlier quoted context omitted.

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 pri…

You can spend many months working through papers and books without a company paying you for that. That's something that I continually do and have always done, in my own time (and many different fields). Sufficient and not necessary indeed.

Re: Machine Learning Crash Course

#195

Post by graycat »

telchar wrote: post #55
graycat wrote: post #24

In the course, in lecture "Reducing Loss: Gradient Descent" is "Convex problems have only one minimum; that is, only one place where the slope is exactly 0. That minimum is where the loss function converges." The first sentence is flatly wrong: E.g., for positive integer n and the set of real numbers R, function f: R^n --> R where for all x in R^n f(x) = 0, f is convex, concave, and linear, and for all x in R^n x is…

You should recheck your definitions on convexity. >function f: R^n --> R where for all x in R^n f(x) = 0 This hyperplane is not convex. A convex curve by definition can not be equal to its tangent at any point. Edit: I should specify, I mean a convex curve cannot be completely equal to any of its tangents, obviously it will equal each tangent at a single point.

You don't want to consider just "tangents" and, instead, consider what I defined as supporting hyperplanes of the epigraph and subgradients of the function. If the gradient exists, that is, if the function is differentiable, then the subgradient really is a tangent. Otherwise can have many different subgradients supporting at one point on the curve and its epigraph.

It's simple: A cube has supporting planes at each point that is an edge or corner, but those points do not have tangents.

Re: Machine Learning Crash Course

#196

Post by gchadwick »

achompas wrote: post #59
minimaxir wrote: post #12

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…

> 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…

> It is also absolutely true, in my experience, that you need a graduate-level education or years of hands-on experience to troubleshoot cases where AI/ML fails on a deceptively-simple problem, or to tweak an AI/ML algorithm (or develop a new one) so it can solve a novel problem.

How much of that is critical domain specific knowledge and how much of that is just general engineering debugging/problem solving experience though? Certainly the person who does have the masters/PhD and a few years of applying that to real-world ML problems will have the edge but an experienced developer who's got a knack for maths (though no direct ML experience) may be able to get up to speed quicker than you think. Part of that will be experience with knowing how and when to ask the right questions when you get stuck.

Re: Machine Learning Crash Course

#197

Post by graycat »

aoki wrote: post #172
graycat wrote: post #154

Earlier quoted context omitted.

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…

my only goal in mentioning the statistics sequence at all was to give a familiar example where the standard sequences vary in depth depending on audience. a trivial point, yes, but i wanted to be concrete because it's the internet. apparently that was a terrible choice, as it was far too close to the topic at hand; my apologies for making you search so hard for a connection. i made my request because it's jarring for…

You do understand that for all the talk and new terminology and claims of "learning" in the "machine learning" (ML) in the Google OP, what is in the OP is a poor introduction to some highly polished material in "regression analysis" in 50 year old books. So, the ML stuff is adulterated old wine in new bottles with new labels. That is essentially intellectual theft and corruption, and without references essentially academic plagiarism. You should be offended.

If they are going to plagiarize, even just teach, regression analysis, then at least they shouldn't make a mess out of it, and a mess is what they made. Google should "get that MESS OFF the Internet".

Students should be told the truth: Regression is powerful stuff. Sometimes the results can be valuable. The Google OP is an introduction to regression and does have some value. But the Google material is a MESS, and students should be informed that they are getting really low quality material and should see some references to some beautifully polished material.

So, I helped any students who would be the target audience for the Google OP.

You should know this; I believe you do.

I'm offended by the mess and passing that out to students trying to learn. You should also be offended.

Re: Machine Learning Crash Course

#198

Post by graycat »

graycat wrote: post #154
aoki wrote: post #110

Earlier quoted context omitted.

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…

Correction of a typo:

> So, the function f: R*n --> R where for each x f(x)

should read

So, the function f: R^n --> R where for each x f(x)

Excuse: Just now I'm using a keyboard on a laptop, and I'm not used to the keyboard yet.

Re: Machine Learning Crash Course

#200

Post by mdda »

acconrad wrote: post #18
minimaxir wrote: post #12

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…

> 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. So what are they hoping to achieve with this course? I'm genuinely asking because part of me wants to take the course, but another part of me feels like what's the point if, even through many additional courses to build up a skill set, Go…

Look at it from an interview perspective. If I ask "are you interested in exploring ML", and you're enthusiastic, my next questions are : What have you done? Have you taken any courses? GitHub? Blog Posts?

If the answer is that you're waiting for a special sign that it's worth doing before making an effort, then that really tells me that your enthusiasm for doing ML is not reality-based. Doing the ML thing is a pretty different mindset from other software jobs.

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