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Machine Learning Crash Course

developers.google.com

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Re: Machine Learning Crash Course

#61

Post by eanzenberg »

ariwilson wrote: post #56
eanzenberg wrote: post #34

Earlier quoted context omitted.

Even people getting a quickie masters is hit/miss in my experience. At the end of the day, successful machine learning engineers require a whole suit of different skills, both technical, communicative, and even life skills that don't really exist for software devs. Not all those can be taught in 3 months, 2 years or even 6 years.

Life skills? Communicative skills? What?

IMO essentially ML experts don't work in a bubble and may interface with potentially anyone at a company; C-level, engineering, product, marketing, ops, etc etc. What other tech-employee needs that flexibility? So, I grouped communication / life skills into being able to understand, read, interpret and ultimately provide value to potentially any team. Just having the technical skills will only get you so far.

Re: Machine Learning Crash Course

#62

Post by achompas »

eanzenberg wrote: post #34
foobiekr wrote: post #25

Earlier quoted context omitted.

Gate keeping is only obsolete when it ceases to have impact. The reality right now is that ML is extremely hard to enter even for a very knowledgeable and deeply experienced but non-credentialed (by degree) person. It will be interesting to see how the situation evolves but my own observations are that people trying to enter the space might be better off getting a quickie masters if they can afford the time or cost t…

Even people getting a quickie masters is hit/miss in my experience. At the end of the day, successful machine learning engineers require a whole suit of different skills, both technical, communicative, and even life skills that don't really exist for software devs. Not all those can be taught in 3 months, 2 years or even 6 years.

> both technical, communicative, and even life skills that don't really exist for software devs

Not a fan of this "data scientist is a unicorn" style of thinking. The best people in any profession (especially software engineering) also use these skills in their day-to-day work.

Re: Machine Learning Crash Course

#63

Post by BadCookie »

3pt14159 wrote: post #23
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…

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…

I took a machine learning graduate-level course from Andrew Ng himself, and I don't recall learning about Jaccard indexes or topic drift. Maybe your sense of what counts as "very, very, very basic" is skewed toward your own experience. There's a phenomenon known to psychologists where people tend to think that the stuff that they know is very easy and basic, so they conclude that anybody who doesn't know what they know must be uneducated. But then it turns out that the person you think is uneducated knows about a bunch of surprising stuff that you don't. I can't remember the term for this phenomenon, but I often remember it whenever I find myself beginning to judge another person's expertise. This phenomenon is also super relevant to the failings of most technical interviews, in my opinion.

Re: Machine Learning Crash Course

#64

Post by xamuel »

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 pause before you open up your wallet for them.

Re: Machine Learning Crash Course

#65

Post by blackrock »

blackrock wrote: post #60
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 a "type two" error? I had to google it. It's a false negative. A "Type 1" error, is a false positive. Is this like how people overuse the term "orthogonal"?

So, to put this in human terms.

A false positive or false negative, can be like a pregnancy test.

A false positive, can be where the pregnancy test shows your wife is pregnant, but she is not. And the baby never arrives. Phew, dodged a bullet!

A false negative, can be where the pregnancy test shows your wife is not pregnant, but she really is. And 9 months later, a baby accidentally pops out. Oh crap!

Re: Machine Learning Crash Course

#66

Post by eanzenberg »

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…

Why? Most of that cruft is abstracted away, computation only gets cheaper over time (a world class AI rig cost ~30k, a decent one for 2k) and most applications of ML run on commodity hardware.

Re: Machine Learning Crash Course

#67

Post by tzs »

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.

It sounds like you are describing curves that are strictly convex. Curves that are convex, but not strictly convex, can intersect their tangents at more than one point, or even at every point.

I'm going by the definition of convex function given in Rudin's "Principles of Mathematical Analysis", Apostol's "Calculus", Wikipedia, and MathWorld.

Re: Machine Learning Crash Course

#68

Post by eanzenberg »

BadCookie wrote: post #63
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…

I took a machine learning graduate-level course from Andrew Ng himself, and I don't recall learning about Jaccard indexes or topic drift. Maybe your sense of what counts as "very, very, very basic" is skewed toward your own experience. There's a phenomenon known to psychologists where people tend to think that the stuff that they know is very easy and basic, so they conclude that anybody who doesn't know what they kn…

There's a bit of snobbiness in different areas of tech, although there also are in different areas of academia and research. At the end of the day, the most successful people are the ones who wouldn't dismiss a DS who didn't know "Jaccard index" or "the Halting Problem".

Re: Machine Learning Crash Course

#69

Post by rtfs »

Thanks Google! Now I know that I am a ML guy, as an economist and econometrician. Yes, we shoot this on all kind of stuff, though with a clear business acumen or economic policy thinking.

Re: Machine Learning Crash Course

#70

Post by eanzenberg »

ardit33 wrote: post #57
eanzenberg wrote: post #33

Earlier quoted context omitted.

You probably can't communicate effectively. If you are describing "Type two error" of course you will get eyes glossing over. A huge problem with research fields is their terse banal labels. Confusion matrix anyone?

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.

It's also extremely confusing because very few people remember type 1 vs 2 but false positive/negative has intuitive understanding.
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