The graphic with 4 plots displaying the form forms of the Dirac delta function is very helpful. A brief explanation in the video also compares the Green's function to the Eigenfunction/value method. This comparison is a helpful presentation as the different differential equation solution methods often seem to yield such similar results that a student wonders why there are these different methods.
For the Average Signal Power in the discrete case, I think we should be dividing by n_2 - n_1 + 1, since that is how many data points you will have in the interval from n_1 to n_2.
Thank you for the lecture Can anyone explain the reason behind the other factors apart from the loss function in the optimization equation? why do we need to reduce the distance of the line or the hyperplane center from the axis central point? -> ||w||^2 and what is the 'subject to' condition? how did it come by or what is its purpose? why should the points be parallel to the SVM line ( assuming dot product) correct me on this if it is wrong
Extremely bad English Pronounciation. No one misses the arrogance and bad deliverance of scientific Content. It is not even close to that of Brunton's Excellent Lectures. The Greatest Dislike 👎👎👎
Could anyone elaborate on the last comment regarding Monte Carlo simulation around 22:50? If the reduced model does not yield sufficiently accurate qualitative/quantitative predictions, then how would the Monte Carlo simulation give good results?
Bit weird, you replace some variables and now it looks like a linear equation, except all the nonlinear stuff has been wiped under the carpet, but is stil there 🤔. I'm in the middle of your lecture, curious how this will work out, of course it will.
What is a lot of weights? The problem I have had is that it is hard to find the gradient where the derivative of all the parameters are minimized at once. Usually, there is a parameter or two that will significantly increase the sum of squared errors or mean squared error so the step can't be big and one of the many parameters will keep the parameter set from moving towards a minimum.. In other words, the valley of the data set of parameter is trying to walk down is very narrow. I have lots of real data. GD always has problems with optimizing his data. There are many algorithms that a much better if the number of parameters is less than 25. The professor's visual example is OK for teaching but is too simple.easy,
The linear algebra / functional analysis perspective of representing a function with a new coordinate system of orthogonal sinusoidal basis is where it makes Fourier transform clicks.
Hi Nathan. Thanks for your great lesson. It's just what I need in my subject at university. However, I have a problem. I can't find the python codes for your video on your website. Sincerely, Jim.
I have dug around in your content as well as your website. I am looking for the nuts and bolts of how to go from A --> Z with Chebyshev. Your content is as close is as I have gotten. Can you direct me if you have the content somewhere? You have it in your video here with asubn and bsubn but it isn't very clear what you are trying to communicate. Please. Thank you!
