Corrections: 6:17 I should have said that the blue points have twice the density of the purple points. 7:08 There should be a 0.05 in the denominator, not a 0.5. Support StatQuest by buying my book The StatQuest Illustrated Guide to Machine Learning or a Study Guide or Merch!!! statquest.org/statquest-store/
Thanks very much for the informative lecture and it is really helpful. UMAP is more and more popular now, could you explain it and compare with tSNE as well? Thanks in advance.
@@statquest UMAP is great, I dont know if it is more popular. There are more stringent reductions out there like ICA. I wonder the thoughts of Josh about it?
@@CompBioQuest I guess it largely depends on the field. Right now, genetics and molecular biology are going bonkers over UMAP. However, ICA is very interesting. Thanks to your question, I found this article which is fascinating: gael-varoquaux.info/science/ica_vs_pca.html
Thank you. I am not sure if you remember me from the PCA video. I have a job now. My job do not have high salary, but I could now support you by donating and thank you now. 😊
I am always blown away by how you make statistics & machine learning algorithms so simple to understand and how you graciously share your knowldege. Keep up the great work man, you are awesome!
I regret I can't put 1000 likes! I read about 20 articles about t-SNE, they are similar to one another, almost identical - and they don't get me closer to the point. But your video - I watched it 4 times (because the topic is hard, at least for me) with making some and drawing - but finally I understand how it works, up to the point that I can explain it to someone else. So many thanks to you!
I'm writing this comment while having watched only half way into this video, which is pretty unusual for me! It is so clearly explained! I once glanced at the t-SNE paper and didn't understand it. If this is what it does then this is how things like this should be explained! Really, we need people explaining science like this! It's possible to read scientific papers, but what they fail to do is properly communicate the core idea to the reader so that the reader quickly grasps the big picture and the intent of the mathematical details without getting lost in the details. Frequently, even a missing definition can make reading papers much harder for non experts.
It's impressive how you managed to explain the essential concepts of this chain of algorithms in such a clear way! I'm sharing this video with my beginner fellows, who normally flee as soon as I say words like nearest-neighbor or stochastic. Thank you very much!
This explanation almost makes tSME sound like a clustering technique not a reduction technique..... That said, this was by far the best explanation I've heard to date.
@@statquest Now if you can explain how to interpret a tSME plot. This would help immensely as it's virtually impossible to determine the correct perplexity number without understanding how to interpret the plot. This seems like one of those "blackbox" methods which we just trust. Keep up the great work!
Josh, i literally love your videos, they are really helping me get through my ADV CS degree. I am going to buy one of your shirts, and wear it on campus as a thank you!
Very nice way of teaching ! ML concepts CLEARLY EXPLAINED and BAM adds lot of curiosity in the videos :) Thanks for your videos. And not to forget your songs are really nice :)
Just hear about t-SNE and I did not quite understand how it works so I crossed my fingers hoping that josh did a video of this and of course he did!! haha I have my popcorn ready to enjoy this video :)
thanks for your great explaination. I just wonder from 5:00 - 5:45, Why when you plot the distance on the normal curve the red and the orange is on different sides of normal curve. I thought distance didn't have direction. Can you please explain more detail about this different direction of the red and orange?
@@statquest yeah, i understood. Because we take p as similarities values so right or left is the same. Thanks a lot. Your videos help me a lot in my machine learning studying.
Fantastic video. I really appreciate all the slides that you made to get the animation effect. It really helped. Possibly the best explanation of t-SNE around. Keep up the good work.
this is such an awesome explanation of tsne that i dont need to watch any other video or read any other website/book. I dont think there can be a better explanation. Superlike.
Came here for understanding the t-SNE plots used in single cell transcriptomics - which I finally did, thanks! Overall, you helped me out already plenty of times! To display cells in during cell fate transition/acquisition e.g. different time points during neurodevelopment, often pseudo-temporal ordering is used. Since scRNA seq is becoming more and more popular, this might be a good next topic
Hi Josh, I can't thank you enough for how much I have benefitted from your videos even though I do data science as part of my day job. Thank you so much for sharing your knowledge! One request for a video: could you do a video of when to use which methods / models in a typical data science problem? Much appreciated.
Awesome explanation, thank you so much! I read a few papers/books multiple times and barely have a clue, but with your vid I understand the concept just by watching it once!
Great video - thank you! One small insertion that I think would improve it: at ~2:07, right after showing what projecting on to the X or Y axis would look like, show one more example of projecting onto an arbitrary line to try to retain as much variance as possible (basically PCA). I think this could be done in 15-20 seconds, and would be helpful in comparing t-SNE to one of its most popular alternatives, which is helpful in deciding *when* to use an algorithm - one of the hardest things for beginners like myself.
Hey, love your videos! Just a typo but it should be 0.05 on the values to the right at 07:19. Confused me for a second so might clear things up for others.
Thank you Josh . I love the way you present concepts with simple examples. Could you please explain how you decided the red dot directions to the left, where as the orange on right side @5:30 ?
It doesn't matter what side of the curve the points are on, since the distance from the y-axis values on the curve will be the same (normal curves are symmetrical). However, in order for the points to be easily seen, I spread them out on different sides rather than piling them all up on top of each other.
Thank you a lot for the video Josh. Let me point something out, and by minute 10:40, it looks like that t-sne perform a sort of the matrix, instead of minimizing the loss function by gradient descent.
t-SNE in concept is a little dense to me so I am watching this video multiple times to think about the nitty gritty of it… I have three perhaps very naive questions so far: 1) with really high dimensional feature space for some data, how do t-SNE algorithms decide how many dimensions to use for the simplified data? In PCA it can be specified by inspecting the variance of data in each of the components to decide that new feature’s “contribution” in grouping/separating the datapoints, is there a similar measure that is used to decide how many dimensions are used in t-SNE? 2) Why is it only used as a visualization technique and not a true dimension-reduction method for data pre-processing in machine learning pipelines? 3) is it possible that the data do not converge in low dimensional space (i.e., you just could not move the second matrix so that it is similar enough to the first one)? I dug out the original 2008 paper from SkLearn citation and as usual was amazed by how you explained the fairly abstract idea in section 2 of the paper in a mere 20-minute long unhurried video, down to the analogy of the repelling and attraction of mapped data in the low dimensional space (the original paper interpreted the gradient decent method used to locate the low dimensional mapping of points as “springs between every point and all other points”) - no important detail is lost in your video yet they are organized in such a way that they follow a clear logic and do not overwhelm. That is mastery of the art of elucidation ❤ Thanks as always for digesting these complicated items for the benefit of the students and present them in simplified yet informative ways, as always!
Thank you very much! For t-SNE, I'm pretty sure it's always used to generate a 2 (or at most 3) dimensional graph that can be visualized. This is because, unlike PCA, where the axes (or PCs) actually represent something (the directions of the most variance), the axes in t-SNE are completely arbitrary. So there's no way to quantify or rank the axes in order of importance. And it is probably possible to have the low dimensional graph fail to converge. That said, if you'd like more details on t-SNE, check out my videos on UMAP - a related technique that is a little more popular: th-cam.com/video/eN0wFzBA4Sc/w-d-xo.html and th-cam.com/video/jth4kEvJ3P8/w-d-xo.html
Hi Josh, great videos as always! I'm not sure if there's a video about this already, but could you do one with all the clustering or classification or dimensionality reduction methods compiled together and then compare their differences and similarities and talk about situations when we should use which? For example, after looking at many of the videos, I think I'm already a little lost on if I should use PCA or MDS or t-SNE on my data. Ty.
Thank you so much! Right now everyone in our department (Systems Genetics at NYU Langone) is using UMAP. There aren't many great videos about it - it would be awesome if you could help us understand what all the hype is about!
Hi @statquest - At 6:06 you mention that we scale the similarity scores, and at 8:29 we discuss again that the similarity would be different for those 2 points from different clusters. It should be same right post scaling?
Hi, Josh, if we want to get the "similarity score", why don't we simply use the distance between points, instead of using normal curve to calculate the length between point and curve?
Thank you for this tutorial. I have a question. Can you please help me? Thank you. On 5:37 , it measures the "distance" to the curve to get similarity scores. Since "distance" is always positive. How to make all red point on the left side? (it is negative, but distance is positive). All orange points are the right side.( positive, since distance is positive, I can understand this part)
Because the minimum y-axis value for the normal distribution is > 0. the distance, in terms of the y-axis, is always positive, regardless of whether or not a point is on the left or right side of the peak of the curve.
Hi Josh, quality content! This channel continuously helps me to understand the idea behind so that the dry textbook explanations actually make sense. I still have a question. When you calculate the unscaled similarity score, how do you exactly determine the width of your guassian? I get it in the example that we already know the cluster. If I only want to visualize the data without having pre-defined clusters, what happens then?
I talk more about the details of t-SNE and how it works in my videos on UMAP: th-cam.com/video/eN0wFzBA4Sc/w-d-xo.html and th-cam.com/video/jth4kEvJ3P8/w-d-xo.html
Great explanation! Thank you so much... I think their is a typo @7:08. Oh oh... On upper part, sum of all scores is 0.24+0.5 instead of 0.24+ 0.05. BAM. Same mistake on the other equation with same denominator. Double BAM. Results are correct. Triple BAM :-)
Super clear. Is the small move carresponding to some learning rate multiplying the gradient of some distance between the expected distance matrix and the one we have?
But how do you get the normal curve at 4:34? As far as I can tell, we only have the mean (the point of interest), which (I think) isn't enough to calculate a normal distribution. Are we just picking a random normal distribution that's centered on the point of interest? And how do you put multidimensional data into a normal distribution anyway?
The width of the curve is determined by the perplexity parameter. For details, see my videos on UMAP: th-cam.com/video/eN0wFzBA4Sc/w-d-xo.html and th-cam.com/video/jth4kEvJ3P8/w-d-xo.html
Love it! A few things could still be clarified (please?): At 07:40, which vector of distances must add up to 1 after scaling? The sum of distances from each point to all other points (regardless of cluster)?
Hi Josh, great video, many thanks! Anyway, I still don't get how do you determine the distribution properties (like standard deviation) for calculating unscaled similarity between two points. When you introduced half as dense cluster as the others, you used normal distribution with standard deviation doubled, what is quite intuitve. But you knew that this cluster is just half as dense as the others. The question is, how to know the properties of these distribution curves?
1. In Flow Cytometry we use median for almost all data analysis because it best describes the central tendency of the data. Is geo mean anyway better describe Flow Cytometry data or geomean is better for some types of Flow Cytometry experiments? 2. What are the drawbacks of downsampling? If there are any way to identify when to avoid downsampling? 3. What is the batch effect? How to identify and remove it? What is the basic principle of identification? What are the strategies to avoid begin with?
Just out of curiosity.... ....are there any plans to do a video on trajectory analysis? I'm doing an analysis on whether the floating properties of ducks and wood can be used to predict the outcome of being a witch or not.
Excellent video. But I didn't understand why t-distribution is used to compute similarities in low dimension but Gaussian is used to compute similarities in a higher dimension? Why can't we use t-distribution for both
I believe the original SNE algorithm used normal distributions for both. however, that resulted in the low-dimensional clusters to be too compact. So the t-distribution, which has fatter tails, was added to spread things out a little more in low dimensions.
Also, 10:40: How are the points (initally) ordered in the matrix to the LEFT? Does the Blue/Red/Yellow on the axes of this matrix on the left mean anything or is that a mistake?
Hey, love your videos! We are actually using it to help explain key concepts in our application-focused courses. I'd love to see UMAP (similar to t-SNE), which is a bit more scalable.
@@statquest Awesome! I'm using your content in my courses - Students love it. PCA, K-Means, & t-SNE. Will be using your ML videos as well. Your explanations are the best!
Great, never heard of t-sne. Is it only for visualisation purposes? In PCA points are clustered based on the correlations. Do you have also an analogy with the loadings of the features like in PCA?
Corrections:
6:17 I should have said that the blue points have twice the density of the purple points.
7:08 There should be a 0.05 in the denominator, not a 0.5.
Support StatQuest by buying my book The StatQuest Illustrated Guide to Machine Learning or a Study Guide or Merch!!! statquest.org/statquest-store/
Thanks very much for the informative lecture and it is really helpful. UMAP is more and more popular now, could you explain it and compare with tSNE as well? Thanks in advance.
@@linweitao6470 I should have a UMAP StatQuest ready in a few weeks. I'm working on it right now.
@@statquest Thanks again!
@@statquest UMAP is great, I dont know if it is more popular. There are more stringent reductions out there like ICA. I wonder the thoughts of Josh about it?
@@CompBioQuest I guess it largely depends on the field. Right now, genetics and molecular biology are going bonkers over UMAP. However, ICA is very interesting. Thanks to your question, I found this article which is fascinating: gael-varoquaux.info/science/ica_vs_pca.html
Thank you. I am not sure if you remember me from the PCA video. I have a job now. My job do not have high salary, but I could now support you by donating and thank you now. 😊
WOW! Thank you so much. And congratulations on getting a job!!! HOORAY!!! TRIPLE BAM! :)
@@statquest Keep doing great work sir! Also, it would be great if you could make a video about the comparation between clustering methods. 😁
@@tuongminhquoc Thanks and I'll keep that in mind!
I am always blown away by how you make statistics & machine learning algorithms so simple to understand and how you graciously share your knowldege. Keep up the great work man, you are awesome!
Thank you very much! :)
Whenever I find statistics technique I have never seen in scientific article, I always visit your channel. Thanks a lot!!
Happy to help! :)
I regret I can't put 1000 likes! I read about 20 articles about t-SNE, they are similar to one another, almost identical - and they don't get me closer to the point. But your video - I watched it 4 times (because the topic is hard, at least for me) with making some and drawing - but finally I understand how it works, up to the point that I can explain it to someone else. So many thanks to you!
HOORAY!!! TRIPLE BAM! I'm glad the video was helpful. BAM! :)
I never leave comments, but I really feel the need to thank you for being able to explain this in such a simple way
Thank you! :)
I'm writing this comment while having watched only half way into this video, which is pretty unusual for me!
It is so clearly explained! I once glanced at the t-SNE paper and didn't understand it. If this is what it does then this is how things like this should be explained!
Really, we need people explaining science like this! It's possible to read scientific papers, but what they fail to do is properly communicate the core idea to the reader so that the reader quickly grasps the big picture and the intent of the mathematical details without getting lost in the details.
Frequently, even a missing definition can make reading papers much harder for non experts.
I'm glad you liked this video so much! :)
As entertaining as watching a Walt t-SNE movie!
You made me laugh out loud! BAM! :)
Best stat-word-play of the year! 😂
Josh is so far my favorite TH-camr that is able to explain complex stats concepts so smoothly.
Thank you so much! :)
Josh.. Your explanation is always "simple and easy to understand" even for layman.You are simply "The life Saviour" !!!
Thank you so much :)
Hooray! I'm glad my video was helpful. :)
It's impressive how you managed to explain the essential concepts of this chain of algorithms in such a clear way! I'm sharing this video with my beginner fellows, who normally flee as soon as I say words like nearest-neighbor or stochastic.
Thank you very much!
Thank you very much! :)
🤣🤣🤣🤣it's that terrifying?!? Barbara Oakley in her book, "a mind for numbers" called them zombies🤣🤣🤣
I never knew machine learning could be as simple as... BAM
Thats like the most important lesson.
Double bam 💥
Just a random comment so that someone can say triple bam
Triple bam 💥
hurayyyy we have made it to the END !!!
The only educational channel which brings a smile to my face.
bam!
I am a student in Japan.
I'm not good at English, but it was very easy to understand and I learned a lot:)
Awesome! :)
I was so confusing about t-SNE until I watched this. It's clear and very easy to understand! Thank you! Like your BAM. :D
BAM! :)
I really can't appreciate you enough for your videos.
Books and blogs only make sense after I watch your videos!
Thank you very much! :)
It's rare to come across such a brilliant explanation.
Thank you! :)
Great explanations! Can you please do a video explaining UMAP and potentially how it compares to t-SNE? Thanks!
+1
+1
+1
+1
+1
"This is Josh Starmer, and you're watching Tisney Channel!"
Triple BAM! :)
This explanation almost makes tSME sound like a clustering technique not a reduction technique..... That said, this was by far the best explanation I've heard to date.
That's a good observation. In many ways t-SNE is a hybrid method that reduces dimensions by clustering.
@@statquest Now if you can explain how to interpret a tSME plot. This would help immensely as it's virtually impossible to determine the correct perplexity number without understanding how to interpret the plot. This seems like one of those "blackbox" methods which we just trust. Keep up the great work!
Josh, i literally love your videos, they are really helping me get through my ADV CS degree. I am going to buy one of your shirts, and wear it on campus as a thank you!
That would be awesome!!! Thank you very much! :)
Very nice way of teaching ! ML concepts CLEARLY EXPLAINED and BAM adds lot of curiosity in the videos :) Thanks for your videos. And not to forget your songs are really nice :)
Thank you!
Just hear about t-SNE and I did not quite understand how it works so I crossed my fingers hoping that josh did a video of this and of course he did!! haha
I have my popcorn ready to enjoy this video :)
Worth it!
BAM! :)
Why I couldn't stop bamming the like button??!! You're the best Josh!!
Thanks!
Very clearly explained!
Loved the way you explained such a complicated concept so intuitively.
Thank you.
Glad it was helpful!
thanks for your great explaination. I just wonder from 5:00 - 5:45, Why when you plot the distance on the normal curve the red and the orange is on different sides of normal curve. I thought distance didn't have direction. Can you please explain more detail about this different direction of the red and orange?
The normal curve is symmetrical, so we can puts the dots on either side. In this case, I used both sides so that not all the dots would overlap.
@@statquest yeah, i understood. Because we take p as similarities values so right or left is the same. Thanks a lot. Your videos help me a lot in my machine learning studying.
You are incredible, Josh Starmer!! I loved this
Thank you! :)
Fantastic video. I really appreciate all the slides that you made to get the animation effect. It really helped. Possibly the best explanation of t-SNE around. Keep up the good work.
this is such an awesome explanation of tsne that i dont need to watch any other video or read any other website/book. I dont think there can be a better explanation. Superlike.
Very well explained ! Your video was recommended to us by our professors at ETH-Zürich.:)
Hello Josh, thank you for coming with such incredible videos. Data scientist’s life becomes easy.😬
Thank you! :)
StatQuest with Josh Starmer Hi a request to do a tutorial of UMAP.
Came here for understanding the t-SNE plots used in single cell transcriptomics - which I finally did, thanks! Overall, you helped me out already plenty of times!
To display cells in during cell fate transition/acquisition e.g. different time points during neurodevelopment, often pseudo-temporal ordering is used.
Since scRNA seq is becoming more and more popular, this might be a good next topic
Same here, and I did not expect to understand so fast and clearly!
Excellently explained! I really like your simple, clear, concise explanation - those 3 factors make a world of difference. And, great animations.
Awesome, thank you!
Brilliant explanation, this has been bugging me all day, thank you!!
Glad it helped!
The Best tutorial and explanation for TSNE so far! It's of great help! Thanks a lot!
Thanks! :)
Thanks a lot!! These videos are much more clear than any article!
A video explaining UMAP (related to t-SNE) would be awesome !
I'm working on UMAP. For now, however, know that it is almost 100% the same as t-SNE. The differences are very subtle.
you are the hero, keep explaining complex thing into simple. thankss
Thank you! :)
Great as always. I've heard of t-SNE before, but this was my first real introduction to it. Definitely want to go look at some more resources now.
I just love the way you start all your videos! Stat-Questtttttt :)
BAM! :)
Thanks a lot. I really struggled to understand the concept first time I came across it in a book. Your video helped a lot. Great job!
Hi Josh, I can't thank you enough for how much I have benefitted from your videos even though I do data science as part of my day job. Thank you so much for sharing your knowledge!
One request for a video: could you do a video of when to use which methods / models in a typical data science problem? Much appreciated.
That's a great idea.
Awesome explanation, thank you so much! I read a few papers/books multiple times and barely have a clue, but with your vid I understand the concept just by watching it once!
This is the best video for t-SNE that I have ever seen. Thanks a lot, man
Wish I could *Triple Bam* like this video! Such a simple explanation. Thanks a lot Josh :-)
Glad you liked it!
Super Mega BAM !! So great at what you do as always ... Tons of love sent your way ! Keep up the amazing work :D
Thanks so much!!
excellent explanation , this intuition helps to follow maths behind t-SNE
You make a complex idea becomes so simple and understanding ! Great video. Thanks a lot
Difficult concept made so simple. Just brilliant!!!!
Thanks a lot 😊!
Thank you so much for this great resource and how much investment you have made into it. I have understood this well.
Glad it was helpful!
Thanks for such a clear explanation. You know, your channel already in the top list for me and very soon I'll watch all your videos..
Great video - thank you! One small insertion that I think would improve it: at ~2:07, right after showing what projecting on to the X or Y axis would look like, show one more example of projecting onto an arbitrary line to try to retain as much variance as possible (basically PCA). I think this could be done in 15-20 seconds, and would be helpful in comparing t-SNE to one of its most popular alternatives, which is helpful in deciding *when* to use an algorithm - one of the hardest things for beginners like myself.
Thanks for the tip!
OH God, this is a great explanation, as Radel mention below, it would be nice to have an extended video of the algorithm as the one from PCA!!
Thank you! Yes, one day I'll break the actual equations down and do "step-by-step" explanation of t-SNE.
Looking forward to this.
Kudos, I understood so effortlessly....tripple BAM!!!
Thanks! :)
i am a huge fan of this channel! greetings from brazil ^^
Muito obrigado! :)
One word reaction after watching this video --> AWESOME!!
Thank you so much 😀!
Hey, love your videos!
Just a typo but it should be 0.05 on the values to the right at 07:19. Confused me for a second so might clear things up for others.
"Bam, I made that terminology up" :D :D , great vid, keep up the good work.
Thanks! 😁
I never thought I'd not understand a statquest video! :(
Bummer. What time point was confusing?
Dude this is super clear. Love the content! BAM
Thank you very much! :)
thank you so much for this nice explanation. will help me a lot in my exams
Glad to hear that!
Love the vid. I was wondering how tsne works and you broke it down great and the explanation for the t distribution was short and to the point.
Thank you! :)
Thank you Josh . I love the way you present concepts with simple examples.
Could you please explain how you decided the red dot directions to the left, where as the orange on right side @5:30 ?
It doesn't matter what side of the curve the points are on, since the distance from the y-axis values on the curve will be the same (normal curves are symmetrical). However, in order for the points to be easily seen, I spread them out on different sides rather than piling them all up on top of each other.
@@statquest Thank you again
Amazing work! perfectly explained!!!
Thanks a lot!
"Clearly Expalined" indeed!
hi,i have a question at 8:44,why symmetric probability is Pij = (Pi|j + Pj|i)/2N and not Pij = (Pi|j + Pj|i)/2 ? why there is a N? THANK YOU!
I have no idea.
Thank you a lot for the video Josh.
Let me point something out, and by minute 10:40, it looks like that t-sne perform a sort of the matrix, instead of minimizing the loss function by gradient descent.
Good point. I represented it as a matrix because, internally, all of the similarity scores are maintained that way.
Subscribed because that intro gave me life!
Ha!!! Thanks! :)
t-SNE in concept is a little dense to me so I am watching this video multiple times to think about the nitty gritty of it… I have three perhaps very naive questions so far: 1) with really high dimensional feature space for some data, how do t-SNE algorithms decide how many dimensions to use for the simplified data? In PCA it can be specified by inspecting the variance of data in each of the components to decide that new feature’s “contribution” in grouping/separating the datapoints, is there a similar measure that is used to decide how many dimensions are used in t-SNE? 2) Why is it only used as a visualization technique and not a true dimension-reduction method for data pre-processing in machine learning pipelines? 3) is it possible that the data do not converge in low dimensional space (i.e., you just could not move the second matrix so that it is similar enough to the first one)?
I dug out the original 2008 paper from SkLearn citation and as usual was amazed by how you explained the fairly abstract idea in section 2 of the paper in a mere 20-minute long unhurried video, down to the analogy of the repelling and attraction of mapped data in the low dimensional space (the original paper interpreted the gradient decent method used to locate the low dimensional mapping of points as “springs between every point and all other points”) - no important detail is lost in your video yet they are organized in such a way that they follow a clear logic and do not overwhelm. That is mastery of the art of elucidation ❤
Thanks as always for digesting these complicated items for the benefit of the students and present them in simplified yet informative ways, as always!
Thank you very much! For t-SNE, I'm pretty sure it's always used to generate a 2 (or at most 3) dimensional graph that can be visualized. This is because, unlike PCA, where the axes (or PCs) actually represent something (the directions of the most variance), the axes in t-SNE are completely arbitrary. So there's no way to quantify or rank the axes in order of importance. And it is probably possible to have the low dimensional graph fail to converge. That said, if you'd like more details on t-SNE, check out my videos on UMAP - a related technique that is a little more popular: th-cam.com/video/eN0wFzBA4Sc/w-d-xo.html and th-cam.com/video/jth4kEvJ3P8/w-d-xo.html
Great videos! Great channel! Big thumbs UP!
Big thanks!
Hi Josh, great videos as always! I'm not sure if there's a video about this already, but could you do one with all the clustering or classification or dimensionality reduction methods compiled together and then compare their differences and similarities and talk about situations when we should use which? For example, after looking at many of the videos, I think I'm already a little lost on if I should use PCA or MDS or t-SNE on my data. Ty.
Thanks! I'll keep that in mind.
I need to watch 3 more times to fully understand. TRIPLE BAM!!!
:)
Thank you so much! Right now everyone in our department (Systems Genetics at NYU Langone) is using UMAP. There aren't many great videos about it - it would be awesome if you could help us understand what all the hype is about!
UMAP is on the to-do list. I hope to get to it in the spring.
your explanation is very very good! thanks!!!
Thank you! :)
Thank you very much Josh . You made it easier to understand.
Hooray! I'm glad the video was helpful! :)
Your speak like Kevin from The Office. Great explanation, thanks a lot:)
Hi @statquest - At 6:06 you mention that we scale the similarity scores, and at 8:29 we discuss again that the similarity would be different for those 2 points from different clusters. It should be same right post scaling?
Maybe. In this example, yes. But if each cluster had different numbers of points in them, I'm not sure they would.
@@statquest got it, will check this and post my findings
Excellent video! Perhaps you could add another video where you go through the actual algorithm and how the moves is actually computed.
yes!!! pleasee!!
great explanation especially for beginners.Thanks
Thank you! :)
Thanks really great videos understood concepts so well
Glad it was helpful!
I am at the intro and love it already!
BAM! :)
Hi, Josh, if we want to get the "similarity score", why don't we simply use the distance between points, instead of using normal curve to calculate the length between point and curve?
The normal curve forces the distances to focus on other points that are relatively close.
VERY CLEAR EXPLANATIONS :) THANK YOU FOR ALL YOUR VIDEOS
Thank you for this tutorial. I have a question. Can you please help me? Thank you. On 5:37 , it measures the "distance" to the curve to get similarity scores. Since "distance" is always positive. How to make all red point on the left side? (it is negative, but distance is positive). All orange points are the right side.( positive, since distance is positive, I can understand this part)
Because the minimum y-axis value for the normal distribution is > 0. the distance, in terms of the y-axis, is always positive, regardless of whether or not a point is on the left or right side of the peak of the curve.
Hi Josh, quality content! This channel continuously helps me to understand the idea behind so that the dry textbook explanations actually make sense. I still have a question. When you calculate the unscaled similarity score, how do you exactly determine the width of your guassian? I get it in the example that we already know the cluster. If I only want to visualize the data without having pre-defined clusters, what happens then?
I talk more about the details of t-SNE and how it works in my videos on UMAP: th-cam.com/video/eN0wFzBA4Sc/w-d-xo.html and th-cam.com/video/jth4kEvJ3P8/w-d-xo.html
Thanks for this wonderful video❤️
Glad you enjoyed it!
Great explanation! Thank you so much... I think their is a typo @7:08. Oh oh... On upper part, sum of all scores is 0.24+0.5 instead of 0.24+ 0.05. BAM. Same mistake on the other equation with same denominator. Double BAM. Results are correct. Triple BAM :-)
Thanks! I added that note to the pinned comment.
Super clear. Is the small move carresponding to some learning rate multiplying the gradient of some distance between the expected distance matrix and the one we have?
I believe so.
But how do you get the normal curve at 4:34? As far as I can tell, we only have the mean (the point of interest), which (I think) isn't enough to calculate a normal distribution. Are we just picking a random normal distribution that's centered on the point of interest? And how do you put multidimensional data into a normal distribution anyway?
The width of the curve is determined by the perplexity parameter. For details, see my videos on UMAP: th-cam.com/video/eN0wFzBA4Sc/w-d-xo.html and th-cam.com/video/jth4kEvJ3P8/w-d-xo.html
Love it! A few things could still be clarified (please?):
At 07:40, which vector of distances must add up to 1 after scaling? The sum of distances from each point to all other points (regardless of cluster)?
Yes.
@josh: Amazing videos..BTW @5:12 how do we decide that the orange dots goto the right of the bell curve? Why not to the left side of the bell curve?
It doesn't matter. The curve is symmetrical.
@@statquest Thank you Josh..please continue with the statquests..you are doing mankind a huge help!!!
Incredibly helpful and well presented. Thank you.
Hi Josh, great video, many thanks! Anyway, I still don't get how do you determine the distribution properties (like standard deviation) for calculating unscaled similarity between two points. When you introduced half as dense cluster as the others, you used normal distribution with standard deviation doubled, what is quite intuitve. But you knew that this cluster is just half as dense as the others. The question is, how to know the properties of these distribution curves?
You estimate it from the data.
1. In Flow Cytometry we use median for almost all data analysis because it best describes the central tendency of the data. Is geo mean anyway better describe Flow Cytometry data or geomean is better for some types of Flow Cytometry experiments?
2. What are the drawbacks of downsampling? If there are any way to identify when to avoid downsampling?
3. What is the batch effect? How to identify and remove it? What is the basic principle of identification? What are the strategies to avoid begin with?
Great questions!
Just out of curiosity.... ....are there any plans to do a video on trajectory analysis? I'm doing an analysis on whether the floating properties of ducks and wood can be used to predict the outcome of being a witch or not.
Whatever your model, it will probably improve if you incorporate the average airspeed of swallow.
Excellent work, thank you !!
Thanks!
Excellent video.
But I didn't understand why t-distribution is used to compute similarities in low dimension but Gaussian is used to compute similarities in a higher dimension? Why can't we use t-distribution for both
I believe the original SNE algorithm used normal distributions for both. however, that resulted in the low-dimensional clusters to be too compact. So the t-distribution, which has fatter tails, was added to spread things out a little more in low dimensions.
@@statquest Thanks, I love how you reply to all the queries on your videos.
Also, 10:40: How are the points (initally) ordered in the matrix to the LEFT? Does the Blue/Red/Yellow on the axes of this matrix on the left mean anything or is that a mistake?
I just ordered things by cluster because that was easiest for me, but the "true" ordering is done without knowing the clustering.
Hey, love your videos! We are actually using it to help explain key concepts in our application-focused courses. I'd love to see UMAP (similar to t-SNE), which is a bit more scalable.
Thank you so much! It's on the to-do list. :)
@@statquest Awesome! I'm using your content in my courses - Students love it. PCA, K-Means, & t-SNE. Will be using your ML videos as well. Your explanations are the best!
Great, never heard of t-sne. Is it only for visualisation purposes? In PCA points are clustered based on the correlations. Do you have also an analogy with the loadings of the features like in PCA?
It's only for visualization. It does not have the equivalent of loading scores in PCA.
This is a great explanation thank you!
Glad you enjoyed it!