Lecture 47 - Singular Value Decomposition | Stanford University

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[@awesome3dan]

It's amazing how genuinely interesting topics like these become when you understand what it could represent in the real world rather than treating it all abstractly.

[@MsAlarman]

Definitely

[@paolacalle5737]

AGREEED

[@jessewolf7649]

It’s interesting to mathematicians independent of any real world applications.

[@pradyumnakaushik5836]

This is a really amazing video. It's no joke explaining a concept like SVD in such simple terms and you have nailed it. Concepts become so much more clearer now.

[@LRCatFan]

Those who think quality of teaching doesn't matter need to watch these videos, this guy explained SVD better than anyone I've ever encountered

[@mattbritzius570]

This vampire is good at teaching.

[@MultiAtif]

😂😂🧛🧛

[@mark_grey]

hahaha it's funny~

[@arindammitra2293]

🧛🏻‍♂️🧛🏻‍♂️🧛🏻‍♂️🧛🏻‍♂️🧛🏻‍♂️😂😂😂

[@lighted321]

By far the best explanation on SVD I've ever seen! Now I understand why it is called Singular Value Decomposition

[@nournote]

The origin or the term is just historic and has nothing to do with what is explained here and with data science in general.

[@clapdrix72]

This is by far the best SVD explanation I've come across and I've watched a half dozen and read the same.

[@pravii444]

This guy saved me almost 2-3 hours of time and 2 gigs of data that I was about to spend on UA-cam if I haven’t found this video. Perfect explanation.

[@diekluge]

Excellent video! My brain still hurts from this, but I agree with other posters that this is about the best, easiest to understand explanation of SVD I've come across so far. Thank you.

[@chrisjerome3707]

This has to be the best explanation I have come across for SVD!! Much appreciated!!

[@harineemosur6530]

Brilliant! Went through so many videos and sites but this was the most lucid explanation done.

[@ryanshafiei2374]

This has to be the best explanation of SVD I have encountered on UA-cam. Bravo!

[@amitsinghrathore9012]

Wow...I spent lot of months just to get a clear understanding of this and you did this in just few mins.

[@silviagutierrez7296]

Thank you so much whoever you are! As a LitMajor it's been hard to understand some key concepts for Latent Semantic Analysis, but you explained it beautifully!

[@singhpratyush_]

The example was one of the best ones for SVD that I've seen.

[@kenbobcorn]

This is one of the most well explained concept videos I have ever watched. This video, and other like it, will go well alongside my Data Science course I am going through.

[@impzhu3088]

Your explanation with visualization makes this concept so clearly explained! Thank you so much for clearing my confusion with this concept which had me struggle for weeks!
Great work!

[@Girishlimaye]

Awesome awesome.. in 13 mins i got more intuition on SVDthan i got reading lots of papers on SVD over last few days

[@leif1075]

The first entry firdt column 0.13 is a ery small decimal so how can he say thst entry heavily corresponds or relatrs to rhe first concept ..thst low number would suggest low relation..

[@xiyuanliu2712]

Thank you, this really helps me to understand the concept of SVD!

[@raamav.4837]

This is hands down one of the best explanations of SVD and its practical applications

[@sagnikroy6405]

Thanks for teaching us to the point. Reading this topic for 3 years, no one could have explained it better.

[@abhisheksharma6617]

wow! just wow! one of the best SVD breakdown videos around

[@vaigokhale222]

Amazing explanation in such a short and simple way.
Thanks!

[@evayang1398]

This is the first time I learn it clearly! Thanks for the amazing video

[@xingfang8507]

Is this Dr. Leskovec? Very nice video, very nice professor as well. Thanks for your SNAP project as well.

[@inesarous9117]

The best explanation I have ever found! thank you so much! :)

[@Cappy1102]

The best explanation of SVD I've seen. Thanks for the video!

[@jerrycote1661]

Simple and precise example with meaning - couldn't be better, extremely well prepared, thanks so much for this.

[@jalalsadeghi66]

The most esplaining video that I have seen about SVD it helped me understanding latent semantic indexing

[@unpatel1]

Superb explanation of SVD!! The best I have come across. I was struggling for the last few months, but this video gave me a clear idea about it. Thank you.

[@aprilsun2572]

Best explanation I've seen!!! Love

[@felixtsao]

Great explanation and cool example. Thanks!

[@jegankarunakaran5798]

This is AWESOME! "If you can't explain it simply, you don't understand it well". Can't say more than that...

[@TheStevenSinger]

What an excellent explanation.. Stanford professors are really smart

[@biswasshubendu4]

wow this is the teaching level at standford !!!! hatsoff

[@sanjaykrish8719]

Awesome.. Now I truly understand the interpretation of SVD.

[@mikezhang2266]

the best resource for SVD on youtube

[@chittasandeep]

loved the explanation.....very clearly explained! Thanks :)

[@yongxingsong4782]

Thank you, this really help me on understanding SVD.

[@rrooho]

it was indeed the most intuitive explanation of SVD. it's been about a week that I was trying to understand this concept for use in a deeper way n I couldn't find anything like this

[@HaouasLeDocteur]

GODLIKE EXPLANATION

[@ProfessionalTycoons]

this video actually does a great job explaining this hard concept

[@patrickmullan8356]

Best Explanation - especially the example with movies! - I heard for SVD!
By far!!!

[@jdm89s13]

Excellent overview of SVD and one of its widely-used applications!

[@dusicamilosavljevic6718]

I am studying analytics with very limited prior background in linear algebra - you could not have made it easier for me. Thank you!

[@yasinilulea]

Best video ever in human history

[@BadriNathJK]

This is a very good channel.

[@ArtificialIntelligenceAllinOne]

Thanks!! Keep Learning and Sharing :)

[@nikhilbalwani5556]

Penaldo

[@ronaldmarks5772]

Awesome. I can actually understand this. Thx!

[@satyamgupta4808]

really really amazing. Now it is cleared. Before, by reading many blogs and watching videos i made my own different concept like SVD is A*A(transpose) or A(transpose)*A and U = eigen vectors of first matrix and V = eigen vector of second matrix and singular values are eigen vector of these matrix.

[@adi_1o1]

Thanks, so brilliantly explained.

[@marc2564]

That is a clear example! Good job

[@daesoolee1083]

Crystal clear. Appreciate it so much :)

[@daesoolee1083]

simply, admirable.

[@danielsarabi]

Thank you for a great video, saved me a bunch of time. Cheers!

[@karthik-ex4dm]

Best SVD explanation ever!!!

[@deepm5047]

This was an amazing explaination.

[@VictorBanerjeeF]

Best way one can describe...

[@ntcool123]

This is absolutely brilliant.

[@rudypieplenbosch6752]

Thanks for providing this insight, because its all good and well how to calculate the SVD, but its equally important to know what insights it provides.

[@milenacariello8330]

This is so great, thank you so much.

[@rishikesanravichandran9914]

A good pro gives a good example

[@marcopierrefernandezburgos3116]

Amazing video. All clear now.

[@dimitriskass1208]

Excellent teacher !! Keep up with the good work !!

[@jonathanuis]

I love this guy!!! super good explained

[@paultoronto42]

This is a really good video. I've been struggling with SVD in a course I am taking and for the first time I "almost" understand it. I am still confused about the signs of the elements in matrices U and V. Some numbers are positive and some are negative. Is it just the magnitude that matters? When I entered this in MATLAB I got different results with the signs. At first I thought the signs were all just the opposite, but upon closer inspection I can see that sometimes that signs are opposite and sometimes not. I can't detect a pattern. I also don't completely understand how we can tell which column in the U matrix corresponds to which concept. The sizes of U and V are different in the MATLAB output as well, but I notice that the truncated columns are those where the strength value in the Sigma matrix are zero, so I think that makes sense.

[@louisebuijs3221]

im not an expert but I heard sometime that weird sign things with low values might have to do with round off errors during the calculations

[@elket100]

What a wonderful explanation!! Thank you!

[@rbnn]

This was a remarkable presentation.

[@ArMeD217]

Wow! Thank you for this awesome clarifying explanation.

[@h.moussa2190]

Thank you very much. it's very clear and very well explained !

[@glarange72]

Good intuition. On the formal definition side, this is a bit at odds with the Strang and wikipedia. Where U and V are defined as square matrices, But perhaps, that's not so important in this context since those extra rows and columns are mostly zeros I think

[@Magnetron692]

Thank you for this brief explanation of the SVD

[@muhammadmubashirullah7152]

This is so much better than those MIT lectures. Good Lord, they did not make sense to me.

[@soumyaiter1]

could not stop myself to like this video

[@ruohongzhang7245]

Great lecture with good intuition about SVD

[@divyaalr2076]

Brilliantly explained!!

[@gamerfela8317]

This is what I understood.
In general left singular matrix shows how the rows are related to each other. The right singular matrix shows how columns are related to each other and the diagonal matrix shows that the strength of the relation.
But have one question though in this case it was movies and audience so we could find the correlation and attribute to it. But if we don't know the correlation before hand can we know we determine it?

[@nairairinachovelidze2785]

Thank you! Very helpful!

[@gayatrivenugopal3]

Brilliant explanation, Thank You!

[@seemuNeu]

Great video and very clear explanations. Thank you

[@arnobarefin3946]

So far the best SVD explanation (still in 2020)

[@adityabhide6300]

Best explanation till now👍

[@yoyomemory6825]

a fantastic explanation of SVD, thank you very much!

[@rakilachraf391]

thank you sir your video was rich of infos...with all gratitude & respect

[@arminm5718]

It `s just perfect!

[@astroknight5]

I never really understood the concept behind SVD until now. The example in the first minute made everything click!

[@Ahmedkedir]

Excellent explanation. Thanks,

[@user-iy7rm7dt8x]

Thank you very much!!!

[@robinranabhat3125]

there you go,, you guys got my subscription

[@orhanraufakdemir900]

"It just models in some sense our noise" ... wow

[@TheOYENDRILA]

I was wondering what that was for. And hence this line makes sense.

[@Loudjazz]

thanks for this amazing video !! 👏👏

[@kemalbkts]

Very nice explanations, thank you!

[@yangningxin1832]

this video is the best to SVD, the best, the best!!!

[@user-cn9id3hv1q]

amazing explanation!

[@jatayubaxi4553]

As far as I know, this is the best explanation of SVD on youtube.

[@helenacosta1028]

I love you man!!!! thank youuuu!!

[@dvirginz4001]

Great video, and great lecturer, thanks a lot.

[@jinjupark5728]

VERY HELPFUL THANK YOU SO MUCH!!

[@user-xh4lp5ts8g]

This video explains complex SVD with minimum concepts! (i.e. singular values) :D

[@priyankrajsharma]

OMG.. You made it super easy

[@ma888u]

Greatly explained!!! Thank you so much! :-)

[@haripriyas5250]

Thank you!! Wonderful explanation!