Applied Linear Algebra: Tensor Decompositions

[@ronniec8805]

Professor Kutz, thank you for your whole series of lectures. I learnt quite a lot from the whole series. I am so jealous of your students who attend your courses with privilege.

[@speedbird7587]

Very interesting , delighting and wonderful lecture. I enjoyed it very much. Thanks

[@hiranabe]

Great lecture, thank you! I came here after Gilbert Strang's MIT, was struggling with tensor in an engineering context. and found this lecture is suitable for the audience, too !!

[@jimlbeaver]

Great stuff! Thanks for the clear explanation. Your students are lucky to have you

[@zzzzzzzmr9759]

Thank you! Very intuitive and easy understanding lecture.

[@supersweetshera]

That one direction pun :))

[@OMGshinyobject]

Thanks so much professor kutz this has helped me understand some of the post processing funtions that can be applied to EEM spectra (im a photochemist)

[@alejandrolozada6530]

Great lecture!

[@franciscojavierramirezaren4722]

Thanx for sharing, waiting for next video!👍👍👍

[@zoltangal3793]

Great extension of SVD in multidimensional space.

[@KALLAN8]

Thank god for this video.

[@NeoZondix]

So clear. Thanks

[@weipingshi77845]

Excellent lecture! By the way, you look like actor Brennan Brown.

[@phmfthacim]

This is so helpful!

[@calcifer464]

tell us about tensor networks and blow our minds

[@payamkhorramshahi5726]

Thank you so much for great explanation ! I am just a little confused on how you would calculate the singular values in case of 3d tensors ? arent there supposed to be a different singular values along each dimension ?

[@RuqiyaGhafoor]

how to calculate singular values of 3rd or higher order tensor????

[@aidanokeeffe7928]

This is pretty cool stuff

[@TerryBowl]

What if your points of data is a prime number? That can only be a vector then?

[@jairjuliocc]

Thanks you , good vídeo

[@肚子疼校长]

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