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Google's Free Deep Learning Course

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Re: Google's Free Deep Learning Course

#51

Post by j2kun »

stared wrote: post #48
j2kun wrote: post #37

I'm going through the course right now, and the instructor is saying some strange things, clearly (to me) ignoring that what he's saying is only true in very specific contexts. For example, in the video I just watched he said "the natural way to compute the distance between two vectors is using cross entropy." And then he goes on to describe some unnatural features of cross entropy. The truly "natural" way to compute…

It's a course assuming some background in ML. For _probability_ vectors, there is a distance measure called cross-entropy. It's a standard error measure in classification problems. It has some important properties different from the Euclidean distance (especially for low probabilities) and there is information-theoretic interpretation. More on it: - http://stats.stackexchange.com/questions/80967/qualitively-w...

I understand it, I just disagree with the presentation. If you're sweeping complexity under the rug, say so and provide a link to further reading. I don't think cross-entropy is that common that someone in the target audience for this course would quickly and easily see the nuance.

Re: Google's Free Deep Learning Course

#52

Post by sytelus »

j2kun wrote: post #37

I'm going through the course right now, and the instructor is saying some strange things, clearly (to me) ignoring that what he's saying is only true in very specific contexts. For example, in the video I just watched he said "the natural way to compute the distance between two vectors is using cross entropy." And then he goes on to describe some unnatural features of cross entropy. The truly "natural" way to compute…

Euclidean distance is not a good measure in higher dimension: http://stats.stackexchange.com/questions/29627/euclidean-dis...

Re: Google's Free Deep Learning Course

#53

Post by pconner »

wrsh07 wrote: post #29
jokoon wrote: post #26

Earlier quoted context omitted.

I was just beginning to give it a try, it just requires you to type the code that is shown on video. Poor way of teaching something, it seems at first. I sense this course is just to teach me the tools of the trade, not really enabling students to fully understand what they're doing. On the other hand, some months ago I watched the ML course by Andrew Ng, and I still did not understand how to test a simple linear reg…

From the yc reading list 2015 [ http://themacro.com/articles/2015/12/yc-2015-reading-list/ ], they recommend [for Neural Networks] this book: http://neuralnetworksanddeeplearning.com/ It's more about understanding than "learning tools."

NN&DL is great for neural nets specifically, it doesn't really cover other branches of ML. That being said, I found it very easy to understand with no prior ML knowledge, all you need is some calculus and linear algebra experience.

Re: Google's Free Deep Learning Course

#54

Post by [deleted] »

j2kun wrote: post #37

I'm going through the course right now, and the instructor is saying some strange things, clearly (to me) ignoring that what he's saying is only true in very specific contexts. For example, in the video I just watched he said "the natural way to compute the distance between two vectors is using cross entropy." And then he goes on to describe some unnatural features of cross entropy. The truly "natural" way to compute…

[deleted]

Re: Google's Free Deep Learning Course

#56

Post by argonaut »

it_learnses wrote: post #12

Would it be beneficial for me as a developer to take these machine learning courses? I took a course in the uni a while back and know the general techniques, but I'm not sure how it would help me in my career unless I'm doing some cutting edge work in the field or focusing on a machine learning career, in which case wouldn't I need to be pursuing a postdoc or something in it?

No. Honestly, no. Do it because you think it's interesting. Very few companies do deep learning (Google, Facebook, and Microsoft come to mind - it might be useful if you work at these companies already). That number will undoubtedly grow, but the vast majority of ML/data science positions deal with stuff like linear regression, PCA, logistic regression, decision trees, random forests, maybe svms/boosting if you want to get fancy. Take an ML survey course like Andrew Ng's course to learn about these. Also basic statistics/probability.

Re: Google's Free Deep Learning Course

#57

Post by [deleted] »

j2kun wrote: post #51
stared wrote: post #48

Earlier quoted context omitted.

It's a course assuming some background in ML. For _probability_ vectors, there is a distance measure called cross-entropy. It's a standard error measure in classification problems. It has some important properties different from the Euclidean distance (especially for low probabilities) and there is information-theoretic interpretation. More on it: - http://stats.stackexchange.com/questions/80967/qualitively-w...

I understand it, I just disagree with the presentation. If you're sweeping complexity under the rug, say so and provide a link to further reading. I don't think cross-entropy is that common that someone in the target audience for this course would quickly and easily see the nuance.

[deleted]

Re: Google's Free Deep Learning Course

#58

Post by aswanson »

mrborgen wrote: post #19
magicmu wrote: post #17

How accessible is a course like this with no prior knowledge of linear algebra? I know it's listed in the pre-reqs, but with a good head for math and lots of calc, is it something that could be picked up along the way? I'm normally pretty bold about stuff like that, but I know it's a core part of deep learning / ML. If it's really necessary, if anyone has any resources for linear algebra run-throughs it would be grea…

Haven taken this course (though I will), but check out Andrew Ng's Coursera's ML course. He gives a crash course in linear algebra. It helped me a lot in my endeavours in ML & neural nets. I also like to learn as I go along, and this intro was just about enough to get started.

Ng is an awesome lecturer, very easy to follow.

Re: Google's Free Deep Learning Course

#59

Post by Estragon »

pramodliv1 wrote: post #35
ganeshkrishnan wrote: post #5

Earlier quoted context omitted.

Yes it does. I wonder if it's overkill but Google has to push tensorflow to the market some how

Why do they have to push the library. Are there any competitive advantages?

So they can spend less time onboarding new engineers.

Re: Google's Free Deep Learning Course

#60

Post by datawrangler »

jskonhovd wrote: post #20
magicmu wrote: post #17

How accessible is a course like this with no prior knowledge of linear algebra? I know it's listed in the pre-reqs, but with a good head for math and lots of calc, is it something that could be picked up along the way? I'm normally pretty bold about stuff like that, but I know it's a core part of deep learning / ML. If it's really necessary, if anyone has any resources for linear algebra run-throughs it would be grea…

I would start with the nano degree program if you don't have any prior knowledge of ML. Udacity has a Linear Algebra review course, but I don't believe it is public for now. I had taken a linear algebra course before I took the GT ML class, but I wasn't a expert by any means. I don't believe you will need a deep understanding of linear algebra before taking this class. Singular value decomposition might come up. I th…

Udacity has a couple free linear algebra courses.

One here: https://www.udacity.com/course/linear-algebra-refresher-cour...

And another here: https://www.udacity.com/course/intro-algebra-review--ma004

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