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Machine Learning Crash Course

developers.google.com

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Re: Machine Learning Crash Course

#91

Post by sannee »

xamuel wrote: post #64
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 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…

> But if so-called ML experts don't even know undergraduate computer science

To be fair, Machine learning seems more closely related to applied mathematics - statistics/optimization than to computer science.

Re: Machine Learning Crash Course

#92

Post by dragandj »

bpesquet wrote: post #87

The choice of TensorFlow is a bit disappointing for a beginner-focused course which looks really solid otherwise. Business seems to have gotten priority over pedagogy in that case. I see TensorFlow as the Angular of machine learning: first on the market, powerful but unwiedly. Like Angular, it will ultimately get superseded by tools with a nicer API (scikit-learn, Keras) or more versatility (PyTorch). Like Angular, i…

Add to that that TensorFlow was practically a latecomer, not the first to market.

Re: Machine Learning Crash Course

#93

Post by xamuel »

joshuamorton wrote: post #89
xamuel wrote: post #64

Earlier 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…

Why does an ML-expert need to know the halting problem? Considering that ML is really a CS-oriented form of statistics, why would you expect a statistician to know CS theory?

Thinking more, it's the misleading names ("machine learning", "AI") that rustle my jimmies so much.

Sure, you don't need to know the halting problem to approximately solve MNIST by fitting a million-parameter curve to a dataset.

But you're misleading people if you're claiming to have any kind of insight into how computers can be made intelligent, or how computers can "learn", when you don't even know the halting problem.

Re: Machine Learning Crash Course

#94

Post by ju-st »

fwdpropaganda wrote: post #28
3pt14159 wrote: post #23

Earlier quoted context omitted.

Unless you work for a company obviously known for their ML the "expertise" out there right now is brutal. People are building recommendation engines without knowing the very, very, very basics like Jaccard indexes, ROC Curves, or topic drift. I've even had to explain type two error to someone working on one of these before. I agree with your general thrust, and you're right, messy data is often 95% of the problem, bu…

What's topic drift?

maybe he meant concept drift? https://en.wikipedia.org/wiki/Concept_drift

Re: Machine Learning Crash Course

#95

Post by HumanDrivenDev »

xamuel wrote: post #64
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 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.

Re: Machine Learning Crash Course

#96

Post by ivansavz »

I love the prework section: https://developers.google.com/machine-learning/crash-course/... It's a very good mix of topic and skills that I think everyone should learn, even if not directly planning to do ML or DL. If y'all are looking for a compact (and inexpensive) textbook on linear algebra that comes with all prerequisites you can check out: https://gum.co/noBSLA (disclaimer: I wrotes it)

Re: Machine Learning Crash Course

#97

Post by penetrarthur »

bjourne wrote: post #53

I want to ask people who know ML well if the hype is warranted? Billions of courses, web sites, job applications and HN posts. The subject seem to have taken off massively in the last two years. I mean image and speech recognition is pretty cool (when it works!), but hardly that earth shattering, is it?

I've been reading about it "getting popular during the last two years" for at least 6 years.

Re: Machine Learning Crash Course

#98

Post by imh »

Abundnce10 wrote: post #32

I have a new project at work: I need to take in a free form text of recipe ingredients (e.g. "1/2 cup diced onions", "two potatoes, cut into 1-inch cubes", etc.) and build a program that identifies the ingredient (e.g. onion, potato), as well as the quantity (e.g. 0.5 cup, 2.0 units). Would machine learning be an applicable approach to solving this? Right now I'm just planning on using an NLP library to parse out the…

I did the same a while back, and i suggest using an NLP library to extract parts of speech and parse trees and building a quick dirty solution. I did the same a while back and the strong solution isn't much better (took a week+) than the hacky manual one based on specific keywords like "teaspoon" and parts of speech/parse trees (took a few hours).

Re: Machine Learning Crash Course

#99

Post by bluetwo »

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…

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

Re: Machine Learning Crash Course

#100

Post by lugg »

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…

What's an example of a problem that needs that troubleshooting? (Curious)
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