Wow! I was never good in Maths. I’m a physician, and quite old, 55 years of age. And I could understand everything. Oh, and yes: it’s the COVID-19 lockdown motivation... Thank you so much. You can explain anything, I bet!
24:21. Could you provide some guidance (a link to an example would also be fine) as to how to reach d = 0.179 if we are to calculate the value manually? Thank you very much :)
if you write the fd(d) function you gonna see that this function just depend on the "d" and you can find it's minimum by using any 1 dimensional search algorithm like bisection, golden search, or newton raphson etc..or it can be resolved analyticaly. (maybe you are no longer interested in but some others could be :) )
Very interesting but many things I don't understand. Often when considering the gradient, we consider it at a particular point. When we draw the gradient vector, it's a vector in the same space and coordinate system, but originating from the point where we calculated the gradient, not a vector coming from the origin of the coordinates space ? Then, If I understand gradient like derivative is a slope giving the direction of biggest change, I don't get the intuition on why the gradient lying on this slope is oriented towards the direction of steepest ascent. Does it have anything to do with basis orientation/direction ? I mean when we draw a slope and say it's the slope of the derivative at a particular point, it does not tell us if it is going up or down. I mean rate of change could be toward the decreasing side of the slope, so why do we say gradient always point towards steepest ascent
He said it
This madlad actually did it
Wow!
I was never good in Maths. I’m a physician, and quite old, 55 years of age. And I could understand everything.
Oh, and yes: it’s the COVID-19 lockdown motivation...
Thank you so much. You can explain anything, I bet!
I didn't understand a thing in my textbook, but this is really clear! Thank you sir
guess a starting point, look in thesearch direction(jacobian vector), and search along a 1D variable, repeat. Very clear explaination, thank you.
Brilliant explanation, thank you!
Love your lecturing style! Ty so much
I love you, finially finished my assignment with your help :)
I'm so glad I found this video. Thank you very much.
This is quality Education!
Amazing explanation. Thank you.
Beautiful explanation. Is there any video using conjugate direction as well?
That was beautiful. Thank you very much.
Wow, thanks :-) When I originally made this video, I thought it might be too specialized to get many views. I'm very pleased to have been wrong.
Brilliant professor, thank you!
Thank you so much, your explanation was very clear!!!
This was very helpful. Thank you so much!
Such a great video, congratulations.
Thanks :-)
This was really good. Thank you!
Excellent!
Thank you.
24:21. Could you provide some guidance (a link to an example would also be fine) as to how to reach d = 0.179 if we are to calculate the value manually? Thank you very much :)
if you write the fd(d) function you gonna see that this function just depend on the "d" and you can find it's minimum by using any 1 dimensional search algorithm like bisection, golden search, or newton raphson etc..or it can be resolved analyticaly. (maybe you are no longer interested in but some others could be :) )
Wow!!!! Thank you
Very clear. Thanks
Great! You are a Genius!
Very interesting but many things I don't understand. Often when considering the gradient, we consider it at a particular point.
When we draw the gradient vector, it's a vector in the same space and coordinate system, but originating from the point where we calculated the gradient, not a vector coming from the origin of the coordinates space ?
Then, If I understand gradient like derivative is a slope giving the direction of biggest change, I don't get the intuition on why the gradient lying on this slope is oriented towards the direction of steepest ascent. Does it have anything to do with basis orientation/direction ?
I mean when we draw a slope and say it's the slope of the derivative at a particular point, it does not tell us if it is going up or down. I mean rate of change could be toward the decreasing side of the slope, so why do we say gradient always point towards steepest ascent
What is basic difference between steepest descent method and Marquardt method
thank you very much for the video
Great! Thank you!
That "d" is what brought me here - so what are the methods to find it other than getting another single dim minimization problem?
There is finding that ortho vector along the line..
"Boats Boats Boats" - Laura Bell Bundy
The video was great... But what year is this... Did anyone else get some 90's vibes😀...
Why there is so many circles.
Great!
For these videos, can you please disable the clock in the background?
I didn't notice it untill I read your comment, now I can't even watch this video...
@@WytseZ 😂😂😂😂😂😂😂😂
Thank you, you helped me alot :D
helmed*
What is the unity of d ?
lol sorry to bring this up but it sounded like you said the n word in 10:53
Great tutorial though. much appreciated
lol, nearest N*
I concur with both parts of that statement
knew id found someone else that heard it lol
i suggest to put a mic on ur tshirt =)