Eigenvectors and eigenvalues - simply explained

[@arvindmathur6574]

Absolutely marvellous lecture! Never heard a lecture of this quality and clarity with absolutely the right speed. Taken me five decades to understand these terms! Thank you!

[@albertcowley7679]

Old video but luckily youtube suggested this one to me. Watched other videos first and none succeeded at explaining this concept as effectively.

[@sowmyas4213]

I am enjoying your lecture, starting from the basics. Many things I failed to understand as a student finally makes sense. You are a marvelous teacher and Thank you so much!

[@mrbilalkhan]

Best tutorial ever on Eigenvectors and Eigenvalues. Thanks

[@VKjkd]

Truly feel this is the best explanations I’ve read or seen. I knew the details were lurking in plain sight thank you. This channel deserves millions of views.

[@Nikita-uv7zk]

Thank you very much for making this video.I don't have a statistics background, yet it was easy to comprehend. The pace of teaching was perfect and there is a lot of clarity in explaining the concept.

[@tilestats]

Glad you enjoyed it!

[@RS-hs6ni]

Some people are born to teach. Thanks a lot for this amazing series of videos about multivariate analysis.

[@burburchacha]

straight forward explanation. Thank you! I don't know why all the other videos on this topic have to make things so overcomplicated.

[@hnull99]

hands down the best video on eigenvalues & -vectors on youtube

[@shark-p4o]

this channel should be over 1 million subscribers. Its contents are far better than 99% of the youtube maths and ML contents

[@Vickdeem_007]

I wish all KZbin channels would teach so concisely and easy to understand as you.

[@iamgrinhausgases]

I just wanted to say THANK YOU for making such a wonderful tutorial! I finally have a proper understanding of this topic.

[@hermosafieldsforever4782]

I Sub'd right after 30 seconds. Congratulations you broke through my barrier.❤

[@baduraldeenali2411]

This the most clear and simple demo I have seen.. thank you very much.

[@v.vidhya6752]

The best explanation I got from this channel regarding Eigen vectors and Eigen values and orthogonal eigen vectors. Thanks a ton

[@thenumbersguy8288]

Excellent, the approach, the pace, the details!

[@sakkariyaibrahim2650]

Great. After wandering through hundreds of video now I know what an Eigen vector and Eigen value is👍

[@sefatergbashi]

I absolutely love this channel. The explanations are so clear that I understand every bit of this complex topic

[@Lucyferandtheson003]

This is the best lecture on eigenvectors and eigenvalues. I now have a good basic understanding of what these are. Thank you so much sir

[@kuleeeet1]

thank you very much for this channel it is like a treasure

[@travelingmenagerie]

You are amazing. All my life I've been fearful of this topic, but I totally get it now. What I really like is the fact that you use a very simple data table to show things in concrete terms, and you work through the example using that table. Thank you so very much!!

[@DhanasekaranT-de4wz]

This important topic has been eluding me for several years. But this video made it clear about the concept with appropriate examples. ❤❤❤❤

[@elenapopova4569]

Great explanation about eigenvectors and eigenvalues! My favorite one! Thank you very much! :))))

[@hadidavardoust7893]

The best training of Eigenvectors

[@Frdy12345]

You are an amazing teacher, thanks.

[@jillurrahman6913]

awesome teaching skill..thank you so much boss

[@MrDavidaslv]

Awesome explanation 👍 Eigenvector vs eigenvalue.

[@debasiskar4662]

What an excellent description of things. Hats off to you.

[@tilestats]

Thank you!

[@mrbilalkhan]

At 08:20 I wonder how will you normalize the eigenvector if there were three rows in the vector instead of two?

[@tilestats]

Just extend the equation by the third value. You sum the three squared values and then take the square root of the sum.

[@mahdi1594]

bro, great job, love the way you explain things. You might see this comment copied and pasted across few of your other videos, I am just doing this for the algorithm.

[@tilestats]

Thank you!

[@aindrilasaha1592]

Great explanation, really helpful for beginners..thank you so much!!

[@tilestats]

Thank you!

[@manjusha7526]

Simple and easy video on eigen values and eigen vectors

[@thegtdoc]

excellent series.

[@HaithamAhmed-kr8yl]

Good explanation

[@vgreddysaragada]

very well explained

[@jaypople8885]

Thanks Man ❤❤❤

[@romanemul1]

This explanation is super sweet and i have seen it couple of times on other webs and channels. But when i see applications of SVD , PCA or face recognition that intuition about "stretched and not rotated" or "its just a multiple of vector" is lost somewhere. Or how that is has to do with determinant = 0 ?

[@tilestats]

Yes, the math behind PCA is not easy. However, the purpose of this video is to introduce the students about eigenvectors before they watch my videos about PCA. I would recommend that you watch my videos about multivariate statistics in order at my homepage: www.tilestats.com/

[@Mathclub63]

Absolutely ❤❤❤❤

[@HeavenlyGodlyAngelic]

Thank you

[@dailyenglishforyou9119]

Thanks

[@A.Safwat8]

Thanks!!

[@danielchimezie8680]

Splendid. Thank you.

[@tilestats]

Thank you!

[@shanzou2548]

Hi， I was just confused about why n*n matrix have n eigenvector and eigenvalues

[@tilestats]

It has to do with the degree of the polynomial in the calculations. For example, a two-by-two matrix results in a degree of 2, which can result in a maximum of two roots. In the next video, I show how the polynomial is generated in the calculations: kzbin.info/www/bejne/gKXGf5hjYsumr6M

[@datascience-vf9kx]

How can i download the slides

[@tilestats]

www.tilestats.com/shop/

[@ilhomsadriddinov3627]

great

[@vinguyenkhanh199]

100 likes for you

[@younique9710]

Thank you for that great video! I wonder if we can calculate eigenvalues and eigenvectors with a 3x2 matrix.

[@tilestats]

No, it has to be a square matrix.

[@younique9710]

@@tilestats Thank you for your response. So, can we not use canonical correlation analysis for a n-by-5 matrix?

[@tilestats]

Sure, because you compute the eigenvectors on the covariance matrix, which is a square matrix: kzbin.info/www/bejne/aKW4pqyNidmDp68

[@younique9710]

@@tilestatsThank you!