why would every ML guide use cats as example
I know right! It should be about hotdogs instead.
Machine Learning Guides
31–40 of 56 posts
Re: Machine Learning Guides
#32One 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?
s/Google/someone at Google/
20% time leaves discretionary time for people who're motivated to get something like this started. Official approval may come along the way.
Re: Machine Learning Guides
#33Earlier quoted context omitted.
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 Ex…
This is how people did things back in the time. "Expert systems" with hand-crafted rules, built by "experts".
From the past, we learn that these systems are brittle and break continuously. For example, what happens when spammers start using different words, or send legitimate looking emails that are actually spam? Do you think you can build rules to catch 70%, 80%, 90% or 99.99% of spam?
If your goal is simply showing the rules being applied, you can still learn the rules with ML but display them in this way (for example GP suggested looking at Naive Bayes which was the most common method used to fight spam; I'd also point you to decision trees which are easy to visualize).
Re: Machine Learning Guides
#34One 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…
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?
They want to sell TPUs, this is part of generating the demand.
Re: Machine Learning Guides
#35One 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? s/Google/someone at Google/ 20% time leaves discretionary time for people who're motivated to get something like this started. Official approval may come along the way.
Everyone I know at Google says 20% time comes on top of 100% time these days
Re: Machine Learning Guides
#36One 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…
An ML/data engineer tasked with productizing a data pipeline still does all of those - building layers, optimizing code, learning new tools, algorithms data structure, creating unit tests, following programming paradigms.
Re: Machine Learning Guides
#37One 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 wor…
"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?"
Well, over the last 8 years or so I started out in a similar kind of place, and have gotten quite good at building CRUD and business logic and glue, and fixing crap on the front end, and configuring servers.
Maybe I can stand in for the OP a few years down the line?
Over the last quarter, I've been splitting my time between things like linux admin automation and a set of pre-calculus core classes.
To answer your question on my personal scale, my whole ability to do this kind of work with my mediocre CS education (my BA is in Philosophy, and my PhD work is in Lit) is premised on leveraging the points in the systems where "all the little gears and things that go on in backend have [become] a commodity"... hence I just integrate ERP systems with WordPress or try and clean up some business's AWS drupal hosting setup some crap like that. That's been a fun and rewarding conjunction of my love for systems and the commodification of parts of IT/ programming work.
My hope is that by the time all the little bits of these data science topics become "abstracted away" over the next couple of years, I will understand the general underlying things well enough to use them. But who knows if that is a good bet or not... certainly not me.
However, it feels perfectly fine to learn things like math... I'm way, way better at it than I was as an undergrad 20 years ago and so it's quite a lot more fun for me. It's not like knowing some math has no application outside of this narrow field.
I dunno if my personal answer (keep learning, and enjoy fixing crap) matches the OP or helps extend your points/ question, but I've been getting a lot of fun (and some money) out of following my answer.
Re: Machine Learning Guides
#38One 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…
Regarding your latter part, do read the “define: CTO OpenAI” (don’t have link I’m on mobile) - author has fascinating insights on just how important engineering of the specifics you describe is, for ML work to progress and show results.
Re: Machine Learning Guides
#39Back in 2006, in highschool, I was investigating multilayer feed-forward NNs. I found them magical. I wrote the XOR problem etc. etc.
What always confounded me was the choice of the number and width of hidden layers. This is even now more confusing with the advent of deep and recursive networks. We need empirical work on this, that can be taught in much the same way that gravity is taught as an apple falling from a tree.
We need a determination of the entropy of a network, how to route that entropy and expolit it. Specific scenarios are not adequate.
Re: Machine Learning Guides
#40Rule #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.
Yes and no. When I first started getting into this stuff I was amazed at the difference good feature selection and feature engineering made and I was meticulous about it. But in many real world cases now, an expert human can’t do it as well as quickly as a DNN running on multiple K80s. It all boils down to economics in the end.