Intro to Kernel Density Estimation

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Intro to Kernel Density Estimation

Рет қаралды 153,620

webel od

Күн бұрын

Пікірлер: 130

@n.sabriozturk6520 5 жыл бұрын

Finally here I found a super video that explains briefly and clearly what Kernel Density Estimation is. Thank you so much.

@webelod4999 5 жыл бұрын

Thanks man. Glad the video was of help :)

@jasonhe6947 4 жыл бұрын

I love this tutorial, the pace, example, and visualization are just so great

@XY-yg1ci 3 ай бұрын

so straightforward explanation. understand kernel in the first 2 mins

@peterstanbridge3871 2 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.

@webelod4999 2 жыл бұрын

Awesome! Thanks for leaving the nice comment :)

@zyflying 2 жыл бұрын

Really great intro, briefly and straight to the point

@matematikce9490 5 ай бұрын

Clean, on the the point, good theory/practice ratio. Very much appreciated, thanks.

@yunfenghu3786 5 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!

@webelod4999 5 жыл бұрын

Thanks! I appreciate it!

@aaronlin8785 2 жыл бұрын

Amazing video Tommy. I couldn't understand KD in a week of Uchicago lectures and you did it in about 45 seconds.

@carl416 3 жыл бұрын

Relatively clear exp, good. Visuals really make the difference.

@PianoMan333 2 жыл бұрын

Great video. I found this topic rather abstract but this makes it a lot clearer. Thank you!

@michaeljagdharry 4 жыл бұрын

you are amazing, that was one the clearest explanations of a nonstandard statistical concept I have ever seen

@webelod4999 3 жыл бұрын

Thanks!

@nengjingding5942 4 жыл бұрын

Finally found a video to get a rough but clear idea what KDE is. Highly recommend!

@ali-kadar 4 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.

@okokpk123123 2 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.

@ukvaishnav 3 жыл бұрын

Thanks for making this video. Its concise and quick guide to KDEs.

@IroXtreme 5 ай бұрын

Great video, clear and concise - thanks!

@luismisanmartin98 3 жыл бұрын

This video is absolutely precious! Thank you Tom for taking the time to create this

@webelod4999 2 жыл бұрын

Glad you liked it. So happy to get positive feedback, since it took some time to create.

@timuryalta 5 жыл бұрын

This deserves much more views!

@ummesalmamofficial7637 3 жыл бұрын

Thank You Sir for explaining KDE in a simple way.

@Scoutik997 2 жыл бұрын

This is a very clear explanation of KDE, good job

@pcenxyz1838 4 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

@tgwashdc 3 жыл бұрын

Short, sweet and perfect!

@nakko3017 2 жыл бұрын

Thanks for the very clear explanation. ありがとうございます

@webelod4999 2 жыл бұрын

どういたしまして ! (I used Google Translate)

@RajeshSharma-bd5zo 3 жыл бұрын

Beautifully explained!!

@rajanalexander4949 2 жыл бұрын

Clear visualisations, succinct and lucid explanations -- fantastic video. Thanks!

@barnabyinteractive Жыл бұрын

super well made couldnt ask for anything better lol

@Brumor 7 ай бұрын

Great video, thanks!

@Ariel-px7hz Жыл бұрын

Excellent video. Thank you!

@user-pq6ed3zs5k Жыл бұрын

Great visualizations

@jeffreychong3467 7 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 !

@tymothylim6550 3 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-et4vi 5 жыл бұрын

Best video for KDE

@samuelfischer5131 3 жыл бұрын

This is awesome. Thank you for this overview!

@marcelsa5191 2 жыл бұрын

Extremely good video! Well explained and nice graphics. Thank you and greetings from Oxford :)

@webelod4999 Жыл бұрын

Many thanks!

@qwqsimonade3580 2 жыл бұрын

thanks for the dedicated video

@jaantollander 2 жыл бұрын

Great tutorial. Thank you!

@stephengargan3907 Жыл бұрын

super informative, nice job!

@webelod4999 8 ай бұрын

Thank you!

@NadavBenedek Жыл бұрын

Great audio quality

@webelod4999 8 ай бұрын

Thanks. For anyone curious, the microphone I use is Audio Technica AT2020 USB+

@snehagaikwad2655 5 жыл бұрын

Thank you so much for the video! It was easy to understand conceptually!

@gekkejunior3262 2 жыл бұрын

Clear. Thank you a lot!

@ebrahimfeghhi1777 3 жыл бұрын

Thank you, great explanations!

@laxmanbisht2638 3 жыл бұрын

precisely explained

@singlebinary 5 жыл бұрын

Excellent video and clear explanation. Please keep making more!

@svendavidsson 2 жыл бұрын

Great explanation!

@chenghungchou9521 2 жыл бұрын

Amazing easy to understand!!!!!!!!

@alejozen3457 4 жыл бұрын

Great explanation. Thank you for the effort.

@powerchucho007 3 жыл бұрын

Thanks a lot. Great explanation!

@richardtarbell946 3 жыл бұрын

This is king shit right here.

@delinyahkoning6882 Жыл бұрын

What a nice video this is! Super clear.

@rhodesengr 9 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.

@martinwutke3386 3 жыл бұрын

Thanks for this very good explanation. Will definitely look into your library. Best Wishes

@webelod4999 3 жыл бұрын

Glad it was helpful!

@lifestoriesfromearth6271 4 жыл бұрын

Thank You Tommy for this wonderful explanation. :-)

@bernardoamorim9182 4 жыл бұрын

amazing tutorial, thank you very much for the video and the library :)

@giuliofederico7638 3 жыл бұрын

Perfect explanation

@h-hugo 4 жыл бұрын

Very nice lecture!

@himanshudalai1028 5 жыл бұрын

Thank you so much for the video. Loved it.

@felipefavadelima 3 жыл бұрын

Thanks for your video! Very well explained.

@webelod4999 2 жыл бұрын

Glad it was helpful!

@juandavidcaicedoms7686 6 жыл бұрын

Thnks for this video! It’s a really good explanation, super helpful!

@webelod4999 5 жыл бұрын

Thanks man, I appreciate it!

@khubaibraza8446 4 жыл бұрын

Thank you so much, Super clear explanation.

@makimakiwii 5 жыл бұрын

Very helpful. Thank you so much!

@raduiulia4034 4 жыл бұрын

Amazing video!

@capricacity 5 жыл бұрын

I wish I saw this before completing my PhD. This would have made the process "smoother" get what i mean? HAHA!!!

@zenchiassassin283 3 жыл бұрын

lol, congrats for your PhD too

@Colegial24 5 жыл бұрын

Excellent video! Extremely helpful!

@Abafoteq-Ltd 3 жыл бұрын

Wow..... wonderful. thank you so much. this was indeed very helpful.

@webelod4999 3 жыл бұрын

Glad it was helpful!

@rajm3496 5 жыл бұрын

genius...happy that I found this :-)

@nassehk 5 жыл бұрын

What a great video. Thank you.

@mahadeibnsalam6735 4 жыл бұрын

Great content!

@songmichaelbai 5 жыл бұрын

Nice tutorial! Thanks!

@canmetan670 4 жыл бұрын

Thanks man. Great video.

@pranavkumar9782 3 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 ?

@webelod4999 2 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.

@LeeLeeCode 5 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.

@lilaberkani4376 3 жыл бұрын

Thank you so much for your video, it helps me a looot

@realreactteseract6261 5 жыл бұрын

Amazing, really!!!!

@TheOfficialJeppezon 5 жыл бұрын

Please make more videos!

@diwakarns1600 4 жыл бұрын

Thank you..I did not understand what a norm is, can you explain a bit more on that? Thank you!

@webelod4999 3 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?

@webelod4999 2 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 🙌🙌🙌

@thomasalderson368 4 жыл бұрын

Liked!

@juheesingh1157 5 жыл бұрын

Very hepful video 😊

@dayy14 5 жыл бұрын

Thanks a lottttt!!!

@bean217 5 ай бұрын

9:50 why is the sum only normalized by 1/(h^d) and not 1/(N * h^d) ?

@MiroLogie Ай бұрын

Thanks for the video, what you used to do the plots BTW

@webelod4999 20 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!

@teresaebernardo 3 жыл бұрын

Does the size of the grid make a difference?

@webelod4999 2 жыл бұрын

Yes. The finer the grid, the better the results. In KDEpy the default is 1024 grid points.

@justforsynchtc 3 жыл бұрын

Having to implement this and don't understand the "discrete convolution (possibly by fourier transform)". Any pointers?

@webelod4999 3 жыл бұрын

Look to wikipedia for information about discrete convolution.

@canernm 4 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.

@webelod4999 3 жыл бұрын

They are, yes. If they fulfill the properties, they are PDFs by definition.

@abdizinab7934 4 жыл бұрын

Thanks you some much, please Can you sent me the programs of all those representations

@shivshankarkeshari6604 4 жыл бұрын

4.07- 4.14 how can I do similar in my py project?

@zahrahsharif8431 4 жыл бұрын

Hi, how would you interpret a kde if the x axis is probability and the y axis is density?

@webelod4999 3 жыл бұрын

As a prior distribution in Bayesian statistics.

@SLee-xj4jn 5 жыл бұрын

Best

@Borzacchinni 4 жыл бұрын

Do you happen to be from Norway?

@jeanny2852 4 жыл бұрын

what is the difference between x and xi?

@webelod4999 3 жыл бұрын

x is a continuous variable (the domain), while the x_i's are the observations in the sample.

@cendradevayanaputra7150 2 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

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@MiroLogie Ай бұрын

What you used to plot the data ?

@webelod4999 20 күн бұрын

matplotlib!

@MiroLogie 20 күн бұрын

@@webelod4999 thank you