Finally here I found a super video that explains briefly and clearly what Kernel Density Estimation is. Thank you so much.
@webelod49995 жыл бұрын
Thanks man. Glad the video was of help :)
@jasonhe69474 жыл бұрын
I love this tutorial, the pace, example, and visualization are just so great
@XY-yg1ci3 ай бұрын
so straightforward explanation. understand kernel in the first 2 mins
@peterstanbridge38712 жыл бұрын
Thank you so much for this presentation - first time I've been able to even begin to understand this at an overview level.
@webelod49992 жыл бұрын
Awesome! Thanks for leaving the nice comment :)
@zyflying2 жыл бұрын
Really great intro, briefly and straight to the point
@matematikce94905 ай бұрын
Clean, on the the point, good theory/practice ratio. Very much appreciated, thanks.
@yunfenghu37865 жыл бұрын
Thanks Tommy for this amazing video. I am a visual person and this video gives me a clear view of how density kernel works in 1D and 2D using graphs. Your visualization for norms in higher dimension was fantastic. I will use recommend it to my students in the future!
@webelod49995 жыл бұрын
Thanks! I appreciate it!
@aaronlin87852 жыл бұрын
Amazing video Tommy. I couldn't understand KD in a week of Uchicago lectures and you did it in about 45 seconds.
@carl4163 жыл бұрын
Relatively clear exp, good. Visuals really make the difference.
@PianoMan3332 жыл бұрын
Great video. I found this topic rather abstract but this makes it a lot clearer. Thank you!
@michaeljagdharry4 жыл бұрын
you are amazing, that was one the clearest explanations of a nonstandard statistical concept I have ever seen
@webelod49993 жыл бұрын
Thanks!
@nengjingding59424 жыл бұрын
Finally found a video to get a rough but clear idea what KDE is. Highly recommend!
@ali-kadar4 жыл бұрын
Thank you a ton for the very clear and concise explanation. I like that you go into some algorithmic details nearing the end of the video.
@okokpk1231232 жыл бұрын
Thank you for your presentation.It is really briefly and clearly.It really helps a lots.Hopes you can share more presentation!
@webelod4999 Жыл бұрын
Thanks! The success (in terms of views) on this video inspires me to create more.
@ukvaishnav3 жыл бұрын
Thanks for making this video. Its concise and quick guide to KDEs.
@IroXtreme5 ай бұрын
Great video, clear and concise - thanks!
@luismisanmartin983 жыл бұрын
This video is absolutely precious! Thank you Tom for taking the time to create this
@webelod49992 жыл бұрын
Glad you liked it. So happy to get positive feedback, since it took some time to create.
@timuryalta5 жыл бұрын
This deserves much more views!
@ummesalmamofficial76373 жыл бұрын
Thank You Sir for explaining KDE in a simple way.
@Scoutik9972 жыл бұрын
This is a very clear explanation of KDE, good job
@pcenxyz18384 жыл бұрын
Sir thanks for the explaination.Very well explained actually I came here with zero knowledge. Thanks for the explanation and I will definitely use KDEpy in my projects...thanks for saving the day
@tgwashdc3 жыл бұрын
Short, sweet and perfect!
@nakko30172 жыл бұрын
Thanks for the very clear explanation. ありがとうございます
@webelod49992 жыл бұрын
どういたしまして ! (I used Google Translate)
@RajeshSharma-bd5zo3 жыл бұрын
Beautifully explained!!
@rajanalexander49492 жыл бұрын
Clear visualisations, succinct and lucid explanations -- fantastic video. Thanks!
@barnabyinteractive Жыл бұрын
super well made couldnt ask for anything better lol
@Brumor7 ай бұрын
Great video, thanks!
@Ariel-px7hz Жыл бұрын
Excellent video. Thank you!
@user-pq6ed3zs5k Жыл бұрын
Great visualizations
@jeffreychong34677 ай бұрын
Watched about 10 videos, only this one clicked for KDE.
@ZinzinsIA Жыл бұрын
Very nice, even if i did not get the part about the linear binning and what it is exactly
@ZinzinsIA Жыл бұрын
And very nice for the library btw !
@tymothylim65503 жыл бұрын
Thank you very much for this video! It was very easy to understand (although this topic is still quite new to me). The use of graphs helps a lot with the explanations!
@aman.bansal Жыл бұрын
Thank you for making this helpful video.
@JayPatel-et4vi5 жыл бұрын
Best video for KDE
@samuelfischer51313 жыл бұрын
This is awesome. Thank you for this overview!
@marcelsa51912 жыл бұрын
Extremely good video! Well explained and nice graphics. Thank you and greetings from Oxford :)
@webelod4999 Жыл бұрын
Many thanks!
@qwqsimonade35802 жыл бұрын
thanks for the dedicated video
@jaantollander2 жыл бұрын
Great tutorial. Thank you!
@stephengargan3907 Жыл бұрын
super informative, nice job!
@webelod49998 ай бұрын
Thank you!
@NadavBenedek Жыл бұрын
Great audio quality
@webelod49998 ай бұрын
Thanks. For anyone curious, the microphone I use is Audio Technica AT2020 USB+
@snehagaikwad26555 жыл бұрын
Thank you so much for the video! It was easy to understand conceptually!
@gekkejunior32622 жыл бұрын
Clear. Thank you a lot!
@ebrahimfeghhi17773 жыл бұрын
Thank you, great explanations!
@laxmanbisht26383 жыл бұрын
precisely explained
@singlebinary5 жыл бұрын
Excellent video and clear explanation. Please keep making more!
@svendavidsson2 жыл бұрын
Great explanation!
@chenghungchou95212 жыл бұрын
Amazing easy to understand!!!!!!!!
@alejozen34574 жыл бұрын
Great explanation. Thank you for the effort.
@powerchucho0073 жыл бұрын
Thanks a lot. Great explanation!
@richardtarbell9463 жыл бұрын
This is king shit right here.
@delinyahkoning6882 Жыл бұрын
What a nice video this is! Super clear.
@rhodesengr9 ай бұрын
Thanks for this video. It makes the concept very clear. Other videos, not so much. I have an application where I would like to use 2D KDE on data sets that are set of point on an xy plane. My goal is to fit a 2D Gaussian to the data and then compare goodness of fit for different data sets. I believe I first need to generate a density function for the data and then fit the Gaussian to the density function. KDE looks like a good way to generate the density function. I would prefer to do this in Excel so an Excel plugin would be ideal. I am not really setup (or proficient) to do regular programming in Python, C, or whatever.
@martinwutke33863 жыл бұрын
Thanks for this very good explanation. Will definitely look into your library. Best Wishes
@webelod49993 жыл бұрын
Glad it was helpful!
@lifestoriesfromearth62714 жыл бұрын
Thank You Tommy for this wonderful explanation. :-)
@bernardoamorim91824 жыл бұрын
amazing tutorial, thank you very much for the video and the library :)
@giuliofederico76383 жыл бұрын
Perfect explanation
@h-hugo4 жыл бұрын
Very nice lecture!
@himanshudalai10285 жыл бұрын
Thank you so much for the video. Loved it.
@felipefavadelima3 жыл бұрын
Thanks for your video! Very well explained.
@webelod49992 жыл бұрын
Glad it was helpful!
@juandavidcaicedoms76866 жыл бұрын
Thnks for this video! It’s a really good explanation, super helpful!
@webelod49995 жыл бұрын
Thanks man, I appreciate it!
@khubaibraza84464 жыл бұрын
Thank you so much, Super clear explanation.
@makimakiwii5 жыл бұрын
Very helpful. Thank you so much!
@raduiulia40344 жыл бұрын
Amazing video!
@capricacity5 жыл бұрын
I wish I saw this before completing my PhD. This would have made the process "smoother" get what i mean? HAHA!!!
@zenchiassassin2833 жыл бұрын
lol, congrats for your PhD too
@Colegial245 жыл бұрын
Excellent video! Extremely helpful!
@Abafoteq-Ltd3 жыл бұрын
Wow..... wonderful. thank you so much. this was indeed very helpful.
@webelod49993 жыл бұрын
Glad it was helpful!
@rajm34965 жыл бұрын
genius...happy that I found this :-)
@nassehk5 жыл бұрын
What a great video. Thank you.
@mahadeibnsalam67354 жыл бұрын
Great content!
@songmichaelbai5 жыл бұрын
Nice tutorial! Thanks!
@canmetan6704 жыл бұрын
Thanks man. Great video.
@pranavkumar97823 жыл бұрын
Is it possible to sample from the KDE after fitting, either in sklearn or KDEpy, apart from the usual method of going to a point x_i and sampling from N(x_i, h) if the kernel is Gaussian in the KDE ?
@webelod49992 жыл бұрын
Not that I know of. You could use the Inversion method and the CDF of the returned PDF, but "the usual method" that you mention is equivalent to sampling from the PDF.
@LeeLeeCode5 жыл бұрын
Thank you!
@nallakrishna8796 Жыл бұрын
finally, i found an amazing lecture on kernel density estimation thanks a lot . but i have one query how it can be used to find the anomaly detection. sir can u please make one lecture about this topic otherwise can u please recommand me some good references for KERENEL DENSITY ESTIMATION FOR ANOMALY DETECTION
@aparnamuralidhar5413 Жыл бұрын
Hello there. I tried using your KDE package for my work. Used FFT KDE. When i was trying to evaluate the model with some data-i got an error-'Every data point must be inside the grid" . could you elaborate on this,please?
@webelod4999 Жыл бұрын
If you have a data point at 0, say, and you grid ranges from 1 to 5, then you will get this error. The data point is outside of the grid. Best to let KDEpy create the grid for you. It automatically sets up a reasonable grid.
@lilaberkani43763 жыл бұрын
Thank you so much for your video, it helps me a looot
@realreactteseract62615 жыл бұрын
Amazing, really!!!!
@TheOfficialJeppezon5 жыл бұрын
Please make more videos!
@diwakarns16004 жыл бұрын
Thank you..I did not understand what a norm is, can you explain a bit more on that? Thank you!
@webelod49993 жыл бұрын
It's basically a measure of distance. A generalization of abs(x) in one dimension. See Wikipedia :)
3 жыл бұрын
Sorry for the dumb question but why in the first formula X is subtracting Xi? What it does mean?
@webelod49992 жыл бұрын
If I have a function f(x), then subtracting 2 will shift the function. So f(x-2) shifts the function to the right by 2. When we subtract the data point x_i, we shift the kernel function so it lies "on top" of that data point.
2 жыл бұрын
@@webelod4999 Thank you very much 🙌🙌🙌
@thomasalderson3684 жыл бұрын
Liked!
@juheesingh11575 жыл бұрын
Very hepful video 😊
@dayy145 жыл бұрын
Thanks a lottttt!!!
@bean2175 ай бұрын
9:50 why is the sum only normalized by 1/(h^d) and not 1/(N * h^d) ?
@MiroLogieАй бұрын
Thanks for the video, what you used to do the plots BTW
@webelod499920 күн бұрын
This is a nice extension/improvement! I considered looking at moves too, but determined that (1) getting and preprocessing the data and (2) potentially optimizing over both pokemon and moves would be too much work for a weekend project. If anyone wants to take this even further, I think your ideas are good. At the end of the day the most interesting thing might be to train a reinforcement learning algorithm (like alphago / alphazero et al), but that would be a lot of work!
@teresaebernardo3 жыл бұрын
Does the size of the grid make a difference?
@webelod49992 жыл бұрын
Yes. The finer the grid, the better the results. In KDEpy the default is 1024 grid points.
@justforsynchtc3 жыл бұрын
Having to implement this and don't understand the "discrete convolution (possibly by fourier transform)". Any pointers?
@webelod49993 жыл бұрын
Look to wikipedia for information about discrete convolution.
@canernm4 жыл бұрын
Thanks for the video ! Quick question, are the kernel functions probability density functions? I know the fulfull their properties, but is that enough to make them PDFs? Thanks in advance.
@webelod49993 жыл бұрын
They are, yes. If they fulfill the properties, they are PDFs by definition.
@abdizinab79344 жыл бұрын
Thanks you some much, please Can you sent me the programs of all those representations
@shivshankarkeshari66044 жыл бұрын
4.07- 4.14 how can I do similar in my py project?
@zahrahsharif84314 жыл бұрын
Hi, how would you interpret a kde if the x axis is probability and the y axis is density?
@webelod49993 жыл бұрын
As a prior distribution in Bayesian statistics.
@SLee-xj4jn5 жыл бұрын
Best
@Borzacchinni4 жыл бұрын
Do you happen to be from Norway?
@jeanny28524 жыл бұрын
what is the difference between x and xi?
@webelod49993 жыл бұрын
x is a continuous variable (the domain), while the x_i's are the observations in the sample.
@cendradevayanaputra71502 жыл бұрын
do you have review of Density Estimation?
@webelod4999 Жыл бұрын
kdepy.readthedocs.io/en/latest/literature.html
@cendradevayanaputra7150 Жыл бұрын
@@webelod4999 thank you
@zilezile49424 жыл бұрын
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