Good god, I wish I found this 8 weeks ago. The drop date for classes is the 30th. I ended up with 6% on my first midterm in Mathematical Physics. This class will haunt till the day I die. I'll probably know this material better than any of the classes I've taken, as I'll likely obsess over it for months.
I'm confused on the orthonormal part. There are 2 conditions for orthonormal vectors: (1) orthogonal; and (2) the length is 1. But the example on 2:56, the length is not 1 that negate the conditions of being an orthonormal. Can you please elaborate that part? Thanks
@@wealthy_concept1313 Any set of vectors can be "normalized" (meaning to make the lengths of all of the vectors 1). This does not, at all, change the angles between any of the vectors. The Gram-Schmidt Process (the next video in the playlist) shows us that any _linearly independent_ set of vectors can be made orthogonal without changing the span of the set. Taken together, given any basis, we can always find an orthonormal basis by first using the Gram-Schmidt process to make the basis orthogonal without changing its span, and then we can "normalize" the orthogonal set to make it orthonormal.
Hello Professor and Thanks for your great explanations. I was wondering why do not we have something called orthonormal matrices ?? and think orthogonal matrices are more like orthonormal ones!! :))
Good video. One question: If a square matrix has orthogonal column vectors. its inverse is not equal to its transpose. what should we call this type of matrices?
Elaborate to some extent, I mean your beginning and laying down the foundation of the topic is good but should stretch it till good level. Atleast that's what I feel missing in your videos, do please consider this if you see this comment. By the way I love your videos from quantum numbers to biomolecules all are awesome.
your convention for magnitude of a vector is a bit confusing because the single bar on both sides is usually for absolute value, maybe you should've used double bars for it anyways, i learned a lot, thanks!
Absolute value and magnitude of a vector have so much in common, they might as well use the same notation. I thought the double bars on both sides was completely unnecessary, when I was first introduced to the notation, after having become accustomed to just using the single pair of bars.
"ορθό-ς" is used for other cases too; the one you say is one definition, but the one required for the concept of the video is "vertical"(an example is the mathematical expression "ορθή γωνία"="right angle")
@@georgesimos4914 Even though I know that 3 out of 4 of the letters have completely different pronunciations, I instinctively read "ορθή" as "open". Even though I know it would sound more like "orthi".
I must say your lecture supercedes those in higher institutions.
same
Correct
agreed.
🤓
I agree 💯. At this point you deserve my tuition!
Good god, I wish I found this 8 weeks ago. The drop date for classes is the 30th. I ended up with 6% on my first midterm in Mathematical Physics. This class will haunt till the day I die. I'll probably know this material better than any of the classes I've taken, as I'll likely obsess over it for months.
I'm at same situation as yours, please suggest similar videos/playlist.
same here pal
my final exam is in 15 minutes and i stumbled across this channel. he explains this so clearly!! i wish i found this channel earlier omg
Haha saaaame
15 minutes is crazy dawg
Thank you so much! Professor Dave explains clearly so I can finally understand orthogonal
clean, informative, and concise video, thanks guy
3:05 "four SQUARED plus two SQUARED plus negative one squared" lol im dying and good vid overall
This explained it so well for me, you spoke clearly and didn't do messy sentences, and even paused after every sentence to process it 👏👏👏👏👏👏
can't thank you enough for this clear explaination
Professor Dave explains 😂💕
3:52 I didn't get how the length becomes 1!
A lot of good information in one short video; good overview.
This really helped me understand LLM model quantization just a tiny bit better
you explain so good
Very understood
Thank you
thanks for awesome explanations!!!
Thanks sir.... wonderful lecture
Brilliant explanation!!! ❤️
Amazing explanation
Beautiful Explanation
such good explanations, thanks
I'm confused on the orthonormal part. There are 2 conditions for orthonormal vectors: (1) orthogonal; and (2) the length is 1. But the example on 2:56, the length is not 1 that negate the conditions of being an orthonormal. Can you please elaborate that part? Thanks
Those vectors displayed at 2:56 are not orthonormal, we have converted them both to unit vectors and made them orthonormal thus.
@@criclal1787 but if that's the case it means any vector can be converted to orthonormal at will ?
@@wealthy_concept1313 Any set of vectors can be "normalized" (meaning to make the lengths of all of the vectors 1). This does not, at all, change the angles between any of the vectors.
The Gram-Schmidt Process (the next video in the playlist) shows us that any _linearly independent_ set of vectors can be made orthogonal without changing the span of the set.
Taken together, given any basis, we can always find an orthonormal basis by first using the Gram-Schmidt process to make the basis orthogonal without changing its span, and then we can "normalize" the orthogonal set to make it orthonormal.
Amazing!!.........Explaination is awesome.....
Very nicely taught...
EXCELLENT videos! Thank you so much
Hello Professor and Thanks for your great explanations.
I was wondering why do not we have something called orthonormal matrices ??
and think orthogonal matrices are more like orthonormal ones!! :))
Isn't orthagonality defined by having the dot product equal to null element in Euclidian space?
I think null just means nothing; or in mathematical terms 0 . So yeah you're probably right
Very good explanation, thank you
Thank you for this video
Amazing Professor! One day if God wills I'll come to meet you!
This is perfect!
Inverse of orthogonal matrix = tranpose of matrix
The intro alone earns my like
what happened to the visuals clarifications? its been primarily plug and chug for most of linear algebra..
thanks that was helpful
Could someone elaborate on the weight functions? Is it just a correction factor so that a function can be orthogonal with respect to another?
Please teach at my university.
Good video. One question: If a square matrix has orthogonal column vectors. its inverse is not equal to its transpose. what should we call this type of matrices?
Elaborate to some extent, I mean your beginning and laying down the foundation of the topic is good but should stretch it till good level.
Atleast that's what I feel missing in your videos, do please consider this if you see this comment.
By the way I love your videos from quantum numbers to biomolecules all are awesome.
sir, the inner product notation reminds me of bra-ket notation
video on caley hamilton
I love the professor 😁
your convention for magnitude of a vector is a bit confusing because the single bar on both sides is usually for absolute value, maybe you should've used double bars for it
anyways, i learned a lot, thanks!
Absolute value and magnitude of a vector have so much in common, they might as well use the same notation. I thought the double bars on both sides was completely unnecessary, when I was first introduced to the notation, after having become accustomed to just using the single pair of bars.
3:23 Nice frankenbiting skills xD
eyw reis
Thank you so much Sir.../\
Finally I understood Orthogonality.🤖
Is Ortho Greek word which means , straight up?
"ορθό-ς" is used for other cases too; the one you say is one definition, but the one required for the concept of the video is "vertical"(an example is the mathematical expression "ορθή γωνία"="right angle")
@@georgesimos4914 Even though I know that 3 out of 4 of the letters have completely different pronunciations, I instinctively read "ορθή" as "open". Even though I know it would sound more like "orthi".
I found this channel because of flat earth videos, never did I guess this man would save my math grade
TE DUA
professor dave is an AI
❤️❤️❤️❤️❤️
Yeah but....what does this have to do with birds? (Sorry, couldnt resist!)
CHEMISTRY JESUS CUT HIS HAIR