How Google Is Remaking Itself for “Machine Learning First” • Hacker News Board

Re: How Google Is Remaking Itself for “Machine Learning First”

#101

Post by collyw » Thu, Jun 23, 2016, 1:32 PM UTC

bhntr3 wrote: post #45

xenihn wrote: post #9

Anyone happen to have a suggested self-teaching path for Machine Learning? I.e. books and courses. I know that Andrew Ng's course is a great resource, but I know that I'm not ready to start it yet. I'm actually way behind on the mathematical pre-requisites, so recommendations for that would be greatly appreciated as well. I've never taken a statistics course, and never received any formal education for mathematics pa…

I actually think the hardest part about ML is the lingo. It's very alienating that even simple concepts seem to have their own lingo. A lot of the ideas are just what you as a developer might do intuitively if you had to implement it. But the language tends to be a bit mathy and obscure. So, when you try to read something without understanding the lingo, it seems impenetrable. But once you know things like "quantizat…

I agree. I started Andrew NG's coursera course and it seemed pretty maths heavy and dry. I started reading through the tensorflow tutorial and couple other more hands on approaches. I get a better idea of what is actually happening in the second.

Am I likely to need matrix multiplication if I start doing machine learning, or that the equivalent of writing a sort algorithm for a web dev - maybe useful to know the concepts, but in reality you won't actually use it?

Re: How Google Is Remaking Itself for “Machine Learning First”

#102

Post by ocdtrekkie » Thu, Jun 23, 2016, 1:42 PM UTC

peatfreak wrote: post #25

xg15 wrote: post #21

I was kind of surprised this article hooks with that relatively small "Ninja" workshop. My impression so far was that Google more or less created the whole machine Learning movement (out of necessity from their two core field, search and ads/analytics) and is employing several authorities of the field. After Google Now, DeepDream and all the self driving car hype, reading about that workshop being the start of the bi…

> My impression so far was that Google more or less created the whole machine Learning movement How did you get this impression? It has little basis in reality.

Good marketing, I presume.

Re: How Google Is Remaking Itself for “Machine Learning First”

#103

Post by ocdtrekkie » Thu, Jun 23, 2016, 1:45 PM UTC

tjl wrote: post #68

eplanit wrote: post #59

Earlier quoted context omitted.

Agreed, and I've been waiting over a decade now for the demise of tiresome qualifiers like "ninja" and "on steroids" (we could all add a few, I'm sure). I was really, really tired of "uber", too, but now that one seems here to stay for quite a while longer. Oh, well...

I agree that these are all problems, but the one that bothers me most is the use of "x master race" such as PC master race. Considering the association with the Nazis and genocide, I keep hoping that it will finally die.

Why? What could be more insulting to Hitler than associating his 'glorious master race' with a bunch of gamer neckbeards living in their moms' basements? One of the greatest abilities we have to take the power away from something is to redefine it.

Re: How Google Is Remaking Itself for “Machine Learning First”

#104

Post by krosaen » Thu, Jun 23, 2016, 1:55 PM UTC

xenihn wrote: post #9

Anyone happen to have a suggested self-teaching path for Machine Learning? I.e. books and courses. I know that Andrew Ng's course is a great resource, but I know that I'm not ready to start it yet. I'm actually way behind on the mathematical pre-requisites, so recommendations for that would be greatly appreciated as well. I've never taken a statistics course, and never received any formal education for mathematics pa…

I'm taking time off to study ML and keep an ongoing list of curriculum resources, as well as a blog of my day to day, here:

http://karlrosaen.com/ml/

Re: How Google Is Remaking Itself for “Machine Learning First”

#105

Post by TDL » Thu, Jun 23, 2016, 2:23 PM UTC

dzhiurgis wrote: post #31

TDL wrote: post #23

Earlier quoted context omitted.

I'm sure others in this thread will have some good advice on the math front. You will want to be comfortable with statistics (as it seems you already are aware), but you will also want to be comfortable with linear algebra as well. Andrew Ng's course has a quick tutorial on linear algebra, you might also want to check codingthematrix.com. Khand Academy is a decent place for stats, probability, linear algebra, & calcu…

This Coursera specialization is almost polar opposite of Andrew Ng's one. It gives a very rudimentary explanation of a concept and then gets you to do a very basic practical exercise using their framework. The tests are simple enough that you can just replace $variable and pass it, but you'd hardly find it applicable with real world problem. I've started with Andrew Ng course and found it way too dry and too much mat…

Just curious, how far did you get into the UW specialization? The first course is certainly rudimentary. Also important to note, after the intro course you don't have to use Graph Lab Create and you can use pandas, numpy, scikit. I have seen people in the forums use R as well. I thought that the regression course & classification course were very thorough, although it does feel as though some of the programming exercises are "hand holdy". Overall, I think it is a solid specialization to get into ML, it's not meant for those experienced with ML or AI.

Re: How Google Is Remaking Itself for “Machine Learning First”

#106

Post by randcraw » Thu, Jun 23, 2016, 2:33 PM UTC

tdullien wrote: post #99

giardini wrote: post #61

Earlier quoted context omitted.

I concur. ML isn't programming per se; it is experimental problem-solving with a particular dataset and algorithm. Your result may/not work well, may/not generalise, and will almost undoubtedly not contribute anything new to any discipline, even to ML. When all ML work is done we'll have great pattern recognizers but nothing remotely akin to thought. And we won't understand how they work or the best way to build the…

I am a mathematician by trade, and was doing development along with other stuff (reverse engineering and security work, first in my own company, then at Google). So ... 1) I think working knowledge of ML is extremely useful to many developers, and generally under-taught in universities. See the old Joel article which mentions "Google uses Bayesian filtering like MS uses the IF statement" http://www.joelonsoftware.com…

I don't think your points are invalid, but I think you overvalue the data that's available and relevant to most programming tasks. And without novel data, ML can offer little novel value.

Google, Facebook, M$ Research, and perhaps Yahoo are extreme outliers. They have zottabytes of broad unstructured text data, so they mine it. Everybody else has megabytes of narrow structured data, most of it commercial transations of their products. That stuff has already been effectively mined by traditional basic OLAP methods. Most/all of the value has been extracted.

Mainstream software apps have yet to show the value of using ML. Such apps have access to very limited data of very narrow relevance. The utility of ML in such domains isn't new; it's classic optimization. Or it's bayesian anticipation. But it's not a game changer. Frankly, the use of ML in most mainstream apps is more likely to add distraction and annoyance as the computer mispredicts your intent -- like Microsoft Bob did.

Maybe "life in the cloud" will create new opportunities for smarter software. But I definitely don't want free apps making their own decisions when to notify me. I guarantee that will get old immediately. So how will this work? Frankly, I can't guess. Like Apple's iAds, programming ML into the mainstream or cloud sounds like an idea that will serve the software / cloud vendor far better than the user.

Re: How Google Is Remaking Itself for “Machine Learning First”

#107

Post by cconroy » Thu, Jun 23, 2016, 2:51 PM UTC

krosaen wrote: post #104

xenihn wrote: post #9

Anyone happen to have a suggested self-teaching path for Machine Learning? I.e. books and courses. I know that Andrew Ng's course is a great resource, but I know that I'm not ready to start it yet. I'm actually way behind on the mathematical pre-requisites, so recommendations for that would be greatly appreciated as well. I've never taken a statistics course, and never received any formal education for mathematics pa…

I'm taking time off to study ML and keep an ongoing list of curriculum resources, as well as a blog of my day to day, here: http://karlrosaen.com/ml/

This is great, thanks! Have you looked into the udacity ML nanodegree? I gave it a cursory look and it seems pretty decent.

Re: How Google Is Remaking Itself for “Machine Learning First”

#108

Post by krosaen » Thu, Jun 23, 2016, 3:29 PM UTC

cconroy wrote: post #107

krosaen wrote: post #104

Earlier quoted context omitted.

I'm taking time off to study ML and keep an ongoing list of curriculum resources, as well as a blog of my day to day, here: http://karlrosaen.com/ml/

This is great, thanks! Have you looked into the udacity ML nanodegree? I gave it a cursory look and it seems pretty decent.

You're welcome!

I looked a while ago and The Udacity nanodegree looks interesting but kind of a subset of the materials I'd already lined up. I also think part of the challenge is tailoring a curriculum to one's existing strengths, so in my case I'm spending less time on general programming / data munging, more on stats fundamentals and ML algorithms, and find that most all in one MOOCs have some material that is less worthwhile for me. Also: some of the projects they feature, like the kaggle competition https://www.kaggle.com/c/titanic can be undertaken independent of udacity.

I really think Python Machine Learning + https://www.kaggle.com/c/titanic + kaggle.com/c/forest-cover-type-prediction is a great place to start on the practical ML side.

Re: How Google Is Remaking Itself for “Machine Learning First”

#109

Post by giardini » Thu, Jun 23, 2016, 4:15 PM UTC

tdullien wrote: post #99

giardini wrote: post #61

Earlier quoted context omitted.

I concur. ML isn't programming per se; it is experimental problem-solving with a particular dataset and algorithm. Your result may/not work well, may/not generalise, and will almost undoubtedly not contribute anything new to any discipline, even to ML. When all ML work is done we'll have great pattern recognizers but nothing remotely akin to thought. And we won't understand how they work or the best way to build the…

I am a mathematician by trade, and was doing development along with other stuff (reverse engineering and security work, first in my own company, then at Google). So ... 1) I think working knowledge of ML is extremely useful to many developers, and generally under-taught in universities. See the old Joel article which mentions "Google uses Bayesian filtering like MS uses the IF statement" http://www.joelonsoftware.com…

Or perhaps Google's ML career path is largely a ruse, a Golgafrinchan Ark Fleet Ship B, that Google is using to trim a bloated developer pool?!8-))

http://hitchhikers.wikia.com/wiki/Golgafrincham

Re: How Google Is Remaking Itself for “Machine Learning First”

#110

Post by LeanderK » Thu, Jun 23, 2016, 4:38 PM UTC

tdullien wrote: post #99

giardini wrote: post #61

Earlier quoted context omitted.

I concur. ML isn't programming per se; it is experimental problem-solving with a particular dataset and algorithm. Your result may/not work well, may/not generalise, and will almost undoubtedly not contribute anything new to any discipline, even to ML. When all ML work is done we'll have great pattern recognizers but nothing remotely akin to thought. And we won't understand how they work or the best way to build the…

I am a mathematician by trade, and was doing development along with other stuff (reverse engineering and security work, first in my own company, then at Google). So ... 1) I think working knowledge of ML is extremely useful to many developers, and generally under-taught in universities. See the old Joel article which mentions "Google uses Bayesian filtering like MS uses the IF statement" http://www.joelonsoftware.com…

I think it depends on the job. Maybe a web-developer has lesser gain from extensive knowledge in ml. But I agree that every computer scientist (whether he works as an software engineer or not) should have some knowledge of ml, there are many things in the curriculum that are not as important as ml.

As a snarky remark: Maybe i am not yet qualified enough for real criticism as an cs-student, but i don't like it such sharp destinations between engineering and theory. All the "trial an error" in ml can be a useful guide to solving the theory. Also i guess the work of Jeff Dean is quite often more theoretical as the work of an average engineer. While i feel that if we have not developed a theory behind such tools, we have not really understood them, no one knows how komplex these things really are. I think/feel this makes ml-related engineering harder than software projects with a well understood theory

I just hope there are enough computer-scientists/mathmaticians at universities (or google ;) ) sharply looking on all the progess made in ml from the engineering side and asking themselves "what does that really mean?", because thats a hell of an interesting problem.

I may be wrong, my lecture on ml is next semester ;)