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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”

#31

Post by dzhiurgis »

TDL wrote: post #23
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 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 mathematical where Dato one seem too simple.

Tensor Flow course seems humorously hard as 15 minutes in you get "Please implement Softmax using Python". Ok, maybe later.

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

#32

Post by dzhiurgis »

cxseven wrote: post #8
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.…

Strangely, half the time I try to use Google Now on my phone, it doesn't seem to understand basic queries that worked two years ago. And in the meanwhile, features and APIs that used to allow more reliable and explicit control (e.g. like in Picasa) are being shut down. I guess someone at Google figured that imitating Apple is worth sacrificing what remained of their power user appeal.

Just yesterday I was amazed I was unable to Google 'what is the smallest website possible' or 'what can you fit on 32kb website' or whether html demoscene exists at all. Or sometimes the results seem like complete spam, instead of showing me answers on xcode, it was showing some heavily seo-ized apple blogs.

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

#33

Post by arbre »

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 software engineers use the model.

I do believe however that some experience is needed to understand what is possible and best benefit from existing tools or to be able to communicate with machine learning engineers about your needs.

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

#35

Post by chc »

swiley wrote: post #29
sputknick wrote: post #17

Earlier quoted context omitted.

Six months ago, I would have said Kaggle, Juptyer, Python, figure things out. I've since discovered Microsoft's ML Studio. It allows you to start out with drag and drop (no code to learn) and, most importantly, you can visually see the output of your experiments. For example if you run a binary decision tree algorithm you can actually look at images of the 1000 trees it created and what the nodes from them is. Not im…

Typing out text isn't hard (and honestly, if you're working with software it's preferable). GUIs give you two things to learn: the fancy editor, and the language.

Was the GP edited after you replied? Because the comment as it exists now is about how ML Studio makes it easy to learn through visualizations. The difficulty of typing has nothing to do with it.

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

#36

Post by thedlade »

dzhiurgis wrote: post #32
cxseven wrote: post #8

Earlier quoted context omitted.

Strangely, half the time I try to use Google Now on my phone, it doesn't seem to understand basic queries that worked two years ago. And in the meanwhile, features and APIs that used to allow more reliable and explicit control (e.g. like in Picasa) are being shut down. I guess someone at Google figured that imitating Apple is worth sacrificing what remained of their power user appeal.

Just yesterday I was amazed I was unable to Google 'what is the smallest website possible' or 'what can you fit on 32kb website' or whether html demoscene exists at all. Or sometimes the results seem like complete spam, instead of showing me answers on xcode, it was showing some heavily seo-ized apple blogs.

I have also noticed that the results are becoming more spammy, I thought I was losing it.

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

#37

Post by yomly »

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 around them so they can apply anything novel ideas developed internally to what they are currently working on.

In those cases, hiring based on sound knowledge of CS fundamentals seems like a good bet...

60k engineers is a pretty terrifying number though.

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

#38

Post by arcanus »

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…

I'm skeptical nevertheless. In my experience, most programming is very different than r+d, which often does require significant concentrated training or even the smartest will spin their wheels.

It's hard to describe, but research (which the vast majority of ML remains) is something that even a sound knowledge of fundamentals might not remotely be enough.

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

#39

Post by [deleted] »

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…

[deleted]

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

#40

Post by arcanus »

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…

The sad truth of the matter is that ML is more applied research at this point than a sensible set of programming problems.

From that standpoint, graduate mathematics is more useful for a practitioner than any robust programming experience.

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