Literally within the first 10 seconds, you covered what my lecturer tried to say in 2 hours, and I am paying 45k in tuition!!! (I'm not rich, I'm just heavily indebted, but I am too cowardly to overcome societal pressures of attending a "prestigious" college. Absolutely crushing my mental health)
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!
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
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!
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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.
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 ?
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.
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?
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.
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.
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
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!
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.
Finally here I found a super video that explains briefly and clearly what Kernel Density Estimation is. Thank you so much.
Thanks man. Glad the video was of help :)
I love this tutorial, the pace, example, and visualization are just so great
Clean, on the the point, good theory/practice ratio.
Very much appreciated, thanks.
Thank you so much for this presentation - first time I've been able to even begin to understand this at an overview level.
Awesome! Thanks for leaving the nice comment :)
Literally within the first 10 seconds, you covered what my lecturer tried to say in 2 hours, and I am paying 45k in tuition!!! (I'm not rich, I'm just heavily indebted, but I am too cowardly to overcome societal pressures of attending a "prestigious" college. Absolutely crushing my mental health)
Really great intro, briefly and straight to the point
so straightforward explanation. understand kernel in the first 2 mins
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!
Thanks! I appreciate it!
Thank you for your presentation.It is really briefly and clearly.It really helps a lots.Hopes you can share more presentation!
Thanks! The success (in terms of views) on this video inspires me to create more.
Amazing video Tommy. I couldn't understand KD in a week of Uchicago lectures and you did it in about 45 seconds.
Great video. I found this topic rather abstract but this makes it a lot clearer. Thank you!
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.
Relatively clear exp, good.
Visuals really make the difference.
Great video, clear and concise - thanks!
This video is absolutely precious! Thank you Tom for taking the time to create this
Glad you liked it. So happy to get positive feedback, since it took some time to create.
Thanks for making this video. Its concise and quick guide to KDEs.
you are amazing, that was one the clearest explanations of a nonstandard statistical concept I have ever seen
Thanks!
This deserves much more views!
This is a very clear explanation of KDE, good job
Finally found a video to get a rough but clear idea what KDE is. Highly recommend!
super well made couldnt ask for anything better lol
Beautifully explained!!
Extremely good video! Well explained and nice graphics. Thank you and greetings from Oxford :)
Many thanks!
Clear visualisations, succinct and lucid explanations -- fantastic video. Thanks!
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
Short, sweet and perfect!
Thank You Sir for explaining KDE in a simple way.
Thanks for the very clear explanation.
ありがとうございます
どういたしまして ! (I used Google Translate)
This is awesome. Thank you for this overview!
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!
Very nice, even if i did not get the part about the linear binning and what it is exactly
And very nice for the library btw !
Good evening everyone,
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loved the value you provided! subscribed :D
Excellent video. Thank you!
What a nice video this is! Super clear.
Excellent video and clear explanation. Please keep making more!
Thank you so much for the video! It was easy to understand conceptually!
Great explanation. Thank you for the effort.
Thank you for making this helpful video.
Thanks for this very good explanation. Will definitely look into your library. Best Wishes
Glad it was helpful!
Thanks a lot. Great explanation!
Best video for KDE
Great video, thanks!
super informative, nice job!
Thank you!
Watched about 10 videos, only this one clicked for KDE.
Clear. Thank you a lot!
Amazing easy to understand!!!!!!!!
thanks for the dedicated video
amazing tutorial, thank you very much for the video and the library :)
Great visualizations
Great audio quality
Thanks. For anyone curious, the microphone I use is Audio Technica AT2020 USB+
Very nice lecture!
Thank You Tommy for this wonderful explanation. :-)
Thank you so much for the video. Loved it.
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.
Thank you so much, Super clear explanation.
Excellent video! Extremely helpful!
Thnks for this video! It’s a really good explanation, super helpful!
Thanks man, I appreciate it!
Perfect explanation
Wow..... wonderful. thank you so much. this was indeed very helpful.
Glad it was helpful!
Very helpful. Thank you so much!
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 ?
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.
9:50 why is the sum only normalized by 1/(h^d) and not 1/(N * h^d) ?
Amazing video!
precisely explained
What a great video. Thank you.
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?
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.
I wish I saw this before completing my PhD. This would have made the process "smoother" get what i mean? HAHA!!!
lol, congrats for your PhD too
Thanks man. Great video.
genius...happy that I found this :-)
Sorry for the dumb question but why in the first formula X is subtracting Xi? What it does mean?
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.
@@webelod4999 Thank you very much 🙌🙌🙌
This is king shit right here.
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
Thanks for the video, what you used to do the plots BTW
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!
Thank you..I did not understand what a norm is, can you explain a bit more on that? Thank you!
It's basically a measure of distance. A generalization of abs(x) in one dimension. See Wikipedia :)
Thank you so much for your video, it helps me a looot
Please make more videos!
Thanks you some much, please Can you sent me the programs of all those representations
Does the size of the grid make a difference?
Yes. The finer the grid, the better the results. In KDEpy the default is 1024 grid points.
Very hepful video 😊
Thank you!
Having to implement this and don't understand the "discrete convolution (possibly by fourier transform)". Any pointers?
Look to wikipedia for information about discrete convolution.
do you have review of Density Estimation?
kdepy.readthedocs.io/en/latest/literature.html
@@webelod4999 thank you
Amazing, really!!!!
Hi, how would you interpret a kde if the x axis is probability and the y axis is density?
As a prior distribution in Bayesian statistics.
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.
They are, yes. If they fulfill the properties, they are PDFs by definition.
What you used to plot the data ?
matplotlib!
@@webelod4999 thank you
4.07- 4.14 how can I do similar in my py project?
Do you happen to be from Norway?
Thanks a lottttt!!!
what is the difference between x and xi?
x is a continuous variable (the domain), while the x_i's are the observations in the sample.
Liked!
Best
Wow...
pika pika
Great explanation!
Thanks for your video! Very well explained.
Glad it was helpful!
Nice tutorial! Thanks!
Great content!
Thank you, great explanations!