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How Google Is Remaking Itself for “Machine Learning First”

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

#81

Post by p1esk »

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…

Well, if they gave you the formula for softmax, it shouldn't take more than a minute to implement it:

import numpy as np

def softmax(x): return np.exp(x)/np.sum(np.exp(x))

where x is an array of numbers.

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

#82

Post by paganel »

srtjstjsj wrote: post #76
hoodoof wrote: post #50

I seem to recall Google focusing the entire company on social/GooglePlus. Is this now saying the company is now being focused on machine learning in the same way? Reminds me of the Ballmer/Gates strategy of everything must be Windows, which seemed flawed to me.

In between, they focused the entire company on switching from Desktop to Mobile.

And at some point the back key on their search page stopped working and they branded this as a feature.

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

#83

Post by paganel »

lugg wrote: post #12
StevePerkins wrote: post #4

Great article, but I can't help but CRINGE at the "ninja" references. I think that's already played out within the industry... and although pop-tech writers tend to lag a few years behind, it will sound extremely dated in the mainstream within a few years.

> “The tagline is, Do you want to be a machine learning ninja?” I don't really like the word, but I don't really give a flop either. I'm not sure how its better or worse than guru, rockstar, or any other lame word recruiters like to use to make us feel like the special snowflakes we are. Which word would you like to see in place of 'ninja'?

Because not all of us are ninja fans. I personally like the Shaolin masters more. And while we're at glorifying paid killers, why not go all the way and strive to be like the original Assasins (https://en.wikipedia.org/wiki/Assassins)? Granted, they were Muslim Shia killers that had a bad habit of also killing Crusaders (i.e. Christians), so that might not play out so well on a professional programmer's CV.

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

#84

Post by kriro »

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…

"Python Machine Learning" is a pretty good book. I also like "Natural Language Annotation" which is a bit specialized but there aren't all that many books on the annotation process.

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

#85

Post by raverbashing »

yomly wrote: post #37

Articles like this for me tend to vindicate Google's notorious hiring processes. While it is true that for most people will not need to be able to whiteboard a binary tree inversion in their day to day, it seems like they expect their engineers to be able to throw themselves at any problem they're given and require them to be able to pivot in skillset quickly, and have an appreciation of all the developments going on…

> Articles like this for me tend to vindicate Google's notorious hiring processes.

No, because they have rejected ML experts if they can't do their stupid dog & pony show.

> hiring based on sound knowledge of CS fundamentals seems like a good bet...

Too bad many of them can't get their heads around the ML math.

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

#86

Post by raverbashing »

matt_wulfeck wrote: post #5

And my anecdotal experience is that it's working extremely well. Take the Google Photos app that does automatic image recognition and tagging. The other day I was looking for a picture we took of our cat the first night we brought him home. I remembered we left him with a blanket in the bathroom but couldn't remember much else. "kitten bathroom 2013" And there was a picture of the cat sitting in the tub on a blanket.…

Google thinks half my pictures of cats are dogs

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

#87

Post by uola »

arbre wrote: post #33

I don't believe in "everyone should work on machine learning". I worked on several deep learning models but I don't really like it. It is a very different job than software engineering in my opinion. ML is more about gathering data and tuning the models as opposed to building stuff. I have spent months working on models and barely wrote any code. It is more efficient to have ML experts focus on the modeling and softw…

"Moving data around" is what a lot of software engineering is these days. Facebook, Google etc. are more data companies than software companies (and probably close to media than communcations companies).

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

#88

Post by zby »

giardini wrote: post #61
arbre wrote: post #33

I don't believe in "everyone should work on machine learning". I worked on several deep learning models but I don't really like it. It is a very different job than software engineering in my opinion. ML is more about gathering data and tuning the models as opposed to building stuff. I have spent months working on models and barely wrote any code. It is more efficient to have ML experts focus on the modeling and softw…

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…

What we need is models that are more retrospectable - so that you can find the rules that it learned. Most of the time they will be too complex for any human understanding - but from time to time we'll find something interesting, something that we can build other things upon.

I have never used neural nets etc - but with simplified bayes spam filters this was possible and quite useful. I used to check which words were pushing a text into one or other category and which did not (when they should).

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

#89

Post by argonaut »

visarga wrote: post #79
argonaut wrote: post #55

Earlier quoted context omitted.

ML involves math. That does not mean it's "applied research," though. The math is mostly at the undergraduate-college-level, and is mostly applied math - except for very theoretical ML/statistics which a practitioner can easily avoid. The math involved straddles an awkward boundary where most undergrad math majors find the math quite simple, but most CS majors would think it's too much math.

There are two major cases: academic, related to algorithm design and industry - related to deployment of already existing algorithms on various data sets. For a CS engineer who wants to be able to use the latest Inception neural net from Google in his pipeline, there is actually almost zero math need. It's like any other API. In goes the image, out comes the label. What she would need to know, as a good utilizer of M…

That isn't "doing machine learning," for the same reason that web developers aren't "operating systems programmers" (even though they use operating systems and need to know some OS concepts).

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

#90

Post by a_imho »

Houshalter wrote: post #58
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…

In 2008 Peter Norvig was quoted saying there was very little or any machine learning in Search. They found it unreliable.

I thought 8 years is a lot and felt ML is just becoming mainstream.

Interestingly trends shows me a steady incline for 'machine learning', while searches for 'neural networks' are dropping since 2004

https://www.google.com/trends/explore#q="machine%20learning"...

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