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Machine Learning Guides

developers.google.com

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Re: Machine Learning Guides

#21

Post by abdujava »

One of my random question is that what does Google gain by spending resources on developing course like this? Do they want more people to do machine learning as there is a short age of developer with this skill in the market or is there something else involved in the mix?

Secondly, for some reason data science just doesn't excite me as much as typical software development goes. Like, why am I not excited enough to go down the path of specializing in data science in field of machine learning? Even if there is more money in it, I'm still not extremely motivated to learn it.

What i do particularly enjoy is good ol' back end web development. I don't have a degree in computer science but working on a information system degree with focus on "programming", I dream/working my ass to become cult of "software engineer" type II, a sophisticated software developer/programmer. I love building layers, optimizing code, learning new tools, algorithms data structure (without knowing math), creating unit tests, following programming paradigm. It excites me so much. And my core skills to dive into is block chain.. I love studying that topic too and all the algorithms it comes with it.

But when I see data science, no excitement. All I imagine is image manipulation and fancy charts. I know I sound a bit ignorant but, that's how it is.

Re: Machine Learning Guides

#22

Post by webmaven »

tabtab wrote: post #19

Perhaps it's somewhat off-topic, but I've built a spam detector similar to the article's withOUT using "direct" AI, but rather via a key-word or key-phrase "ranker". A simplified example is given below. The advantage over other techniques is that one can easily trace the exact math of a conclusion, and tune it as needed. The disadvantage is that one probably has to manually tune it all rather than let the machine "le…

You're essentially doing a rough manual version of Bayesian classification on n-grams (which is still very explicable): http://www.paulgraham.com/spam.html

Re: Machine Learning Guides

#24

Post by boxspam »

abdujava wrote: post #21

One of my random question is that what does Google gain by spending resources on developing course like this? Do they want more people to do machine learning as there is a short age of developer with this skill in the market or is there something else involved in the mix? Secondly, for some reason data science just doesn't excite me as much as typical software development goes. Like, why am I not excited enough to go…

> One of my random question is that what does Google gain by spending resources on developing course like this?

Mindshare or more generally PR. Also to "collect" the talent on their platforms (Tensorflow, Google Cloud, ...). Also these guides were repurposed from existing (internal) guides and are a few years old by now, so the cost is low.

You further describe the role of a data engineer or ML engineer. If you'd approach data science with a focus on engineering and tool use, you could be one of the few dangerous data scientists that is able to go end-to-end (should be safe for at least 5 years when such pipelines are evolved without much human intervention).

> But when I see data science, no excitement. All I imagine is image manipulation and fancy charts.

This is because, while there is legit substance to the hype, the hype is real and it is focused on deep learning ImageNet (and later GAN's, Atari games, Go). Being able to show deepdreamed images and cat neurons is like catnip to journalists. Computer vision is but a very small part of ML and lots of data-driven companies have no need for such skills. Charts are made by analysts.

Everything (including block chain) will move closer to ML paradigm of learning software. Data infra engineers will see their infra increasingly used for ML. It remains all software (very advanced, but accessible to anyone) and hardware (still a asymmetry here between industry lab and practitioner). Don't get left out: Do machine learning like the great engineer you are, not like the great machine learning expert you aren’t.

Re: Machine Learning Guides

#25

Post by tabtab »

webmaven wrote: post #22
tabtab wrote: post #19

Perhaps it's somewhat off-topic, but I've built a spam detector similar to the article's withOUT using "direct" AI, but rather via a key-word or key-phrase "ranker". A simplified example is given below. The advantage over other techniques is that one can easily trace the exact math of a conclusion, and tune it as needed. The disadvantage is that one probably has to manually tune it all rather than let the machine "le…

You're essentially doing a rough manual version of Bayesian classification on n-grams (which is still very explicable): http://www.paulgraham.com/spam.html

The idea of my approach was that a "power user" could add the rules and scores without having to understand something that may take a while to explain. A scoring sheet can be displayed for a given message that would make sense to just about anybody with an associate degree. Example scoring sheet for a given message:

     Category: Spam
       Rule-ID    Score
       ----------------
       NgrPrnc1       7
       bPills         5
       knownPeople   -3      
         Total:       9 Threshold Exceeded!

     Category: Tech Support
       knownWidgets   3
       offer1        -2
         Total:       1 Insufficient total

     Category: Etc...
One could click on the rule-ID as a hyperlink to see specifics of a given rule (if details don't fit on screen).

Re: Machine Learning Guides

#26

Post by rs86 »

Puer wrote: post #10
tzahola wrote: post #2

Daily reminder for data scientist and machine learning types: fill your pockets while you can, because machine learning bootcamps are on the horizon!

Honestly, unless these ML bootcamps are extensive courses on calculus, linear algebra, and statistics and not just "Here's k-means. Memorize it" I doubt they'll harm the market for grad school educated data scientists.

Agreed

Re: Machine Learning Guides

#27

Post by jl2718 »

Rule #0: choose first principles over machine learning.

ML is a last-resort for problems you don’t understand. There are lots of these, but understanding the problem is better.

Re: Machine Learning Guides

#28

Post by hhs19832 »

abdujava wrote: post #21

One of my random question is that what does Google gain by spending resources on developing course like this? Do they want more people to do machine learning as there is a short age of developer with this skill in the market or is there something else involved in the mix? Secondly, for some reason data science just doesn't excite me as much as typical software development goes. Like, why am I not excited enough to go…

Great and honest points.

>Secondly, for some reason data science just doesn't excite me as much as typical software development goes

Fair enough. Part of the reason is "data science" has been so jammed pack of nonsense and people who don't do the actual work of building things, as you describe below.

> What i do particularly enjoy is good ol' back end web development. I don't have a degree in computer science but working on a information system degree with focus on "programming", I dream/working my ass to become cult of "software engineer" type II, a sophisticated software developer/programmer. I love building layers, optimizing code, learning new tools, algorithms data structure (without knowing math), creating unit tests, following programming paradigm. It excites me so much. And my core skills to dive into is block chain..

Ok this makes sense. But I'd be worried about 5 years from now. When all the little gears and things that go on in backend becomes a commodity (or abstracted away in the "cloud"), what are you going to do?

> I love studying that topic too and all the algorithms it comes with it.

That spark of interest in the algorithms, (which is just about logic, which is what math is basically about in the end), is basically the essence of what makes "Data science" so attractive.

Re: Machine Learning Guides

#29

Post by nothis »

These guides pop up left and right, lately. I can't comment on their quality (I assume it's somewhat decent) but it's kinda ridiculous to try compressing a college degree's worth of knowledge into a bunch of sleek online tutorials.

Re: Machine Learning Guides

#30

Post by tomrod »

nothis wrote: post #29

These guides pop up left and right, lately. I can't comment on their quality (I assume it's somewhat decent) but it's kinda ridiculous to try compressing a college degree's worth of knowledge into a bunch of sleek online tutorials.

> it's kinda ridiculous to try compressing a college degree's worth of knowledge into a bunch of sleek online tutorials.

Honest question, why?

We used to give degrees (albeit hundreds of years ago) for material that now is covered, at a high level, in a single course (e.g. physical sciences). The amount of material to cover, and to master, increases dramatically over time. It makes sense to compress the knowledge to be delivered to a compendium so as to simply keep up with progress.

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