Gaussian Mixture Model

[@pakoraize]

The best explanation of GMMs on KZbin

[@amartyahatua]

agreed

[@ljb6596]

I really love that you talked about the big picture, what the variables mean, and how they relate to each other. The last 2 minutes of the video really explains everything to me. My professor in school spends most of the time showing us the technicalities in the math part, like how to take the derivatives, which seems to totally miss the important points but is just a big showoff.

[@anirudhrvs4849]

The best way to explain a concept is by keeping it simple. Hands down the best explanation ever !

[@KasperKubica]

WOW you are honestly an incredible teacher. My normal experience learning some ~"deep" concept like this is comprehending 30% of the material and needing to search at length to find answers to the questions I have about the remaining 70%. Watching this video, despite the fact that I had almost no background, you paused to answer 95% of the questions I would've otherwise had (e.g. "what is this big pi notation?"). I wish I had teachers like you in college - you truly are making me believe in the value of a competent professor over merely reading textbooks (which is the only way I got any learning done in college).

[@hahahaYL-h3x]

Whenever I need to understand a new concept, I search your channel. Thank God, you have this!

[@Raven-bi3xn]

I love that you talk about its assumptions, the reason for using it vs K means, etc. All the important questions answered.

[@ritvikmath]

Thanks!

[@HiraLal-qb8iz]

Way better than my professor who couldn't explain it in one semester. You are a great teacher. thanks very much.

[@ritvikmath]

Happy to help!

[@ricardogomes9528]

You are the most underrated machine learning conceptual lecturer I can find on youtube...

[@mosama22]

Man I SWEAR, you are a legend, I watched videos for faculty staff from very famous universities, they didn't explain it half clear and simple as you did, THANK YOU, THANK YOU, THANK YOU :-)

[@CMDPromptify]

I came here looking for clarity on an adjacent topic that is quite different from what you were teaching, but I have a good idea of how my own troublesome matter is supposed to work. Thanks for this awesome explanation!

[@thedailyepochs338]

bro i just put on notifications on this channel, you are blessing sent from the heavens

[@ritvikmath]

Wow, thanks!

[@ligezhang4735]

Really gives a simple but clear explanation of the model! Better than the book's explanation!

[@keshavsharma267]

the way explained it is so practical as well as theoretical, on top of that easy to understand. Respect to you sir. Thanks a lot

[@ritvikmath]

You are most welcome

[@roshanantony7467]

couldnt understand GMM in my expensive six month long semester classes but understood it now in 7.5 mins by watching this free video in 2x! Thanks Ritwik! Weird world we live in lol

[@quahntasy]

*Probably the best explanation out there. Taught better than my professor.*

[@gravitycuda]

I really really appreciate the effort in making this video. Truly helped in understanding the GM model

[@ritvikmath]

thank you for the kind words :)

[@maharshiroy8471]

I came from the Deep Learning book (Goodfellow, Bengio et al.), and I must say you have done a phenomenal job explaining GMM as compared to that literature.

[@miguellorenzo3726]

The best resource for understanding this topic. I've been searching for two days and this is the most useful explanation I've encountered so far. Thank you.

[@yasirkhatri79]

By far the best explanation I found! Thanks!!!!!!

[@talibdaryabi9434]

Thank you so much, watching your Em model video before watching this one helped me to understand things even better.

[@ritvikmath]

Glad it helped!

[@veloenoir1507]

Dude, you broke it down so clearly even I could understand it! Well done!

[@veloenoir1507]

Keep it going! Your hand-written Notes with that color scheme could be even more impactful if you used Onenote. Get a writing tab for small money it really is a game-changer. Try it if you fancy! Anyhow, pls keep doing these videos.

[@abdularisumar5741]

Wow you help me in my journey on understanding spectral mixture kernel which requires the knowledge in mixture of Gaussian. Thank you

[@ritvikmath]

No problem !

[@kartikkatara5999]

one of the best explanation on this topic

[@ustcdj]

Purely logical and very smooth explanation!

[@kmf9709]

The most easy-to-understand video about GMM on KZbin

[@anushajayasimhan1699]

Wow! God bless you for explaining it so beautifully!

[@karan_vyas]

I wish I had you as one of my prof, who could make the concept easy and teach, rather than complicating it with all maths

[@sairamreddypulikanti1153]

Thanks a ton ! I'm obsessed with this channel. Would love to watch you explaining - 'Deep Learning' topics.

[@lancelofjohn6995]

very clear introduction ,I have understood the Gaussian mixed models.

[@satheeshsekar3512]

Thanks a lot for explainig this concept this much easier. Hats off to you..

[@ritvikmath]

You are most welcome

[@AbHiShEkdAS1505]

You should be our professor at our university.

[@tanyaneeraj8798]

Thanks a lot, I have my exam tomorrow, and I am saved.

[@jaliu]

My new favorite stats channel! Can you make a video on multivariate normal distribution? You referenced it after all 😀

[@santeau]

I wish I found your channel earlier. Great content !

[@michaeldecarvalho8022]

This was a fantastic explanation!

[@seyedalirezaabbasi]

Thank you for another clear explanation

[@ralfsbelohvosciks7466]

Incredible explanation, thank you.

[@ritvikmath]

You're very welcome!

[@alif_ni]

The punch line is, "That's Gaussian Mixture Model in a Nutshell". Anyway.. great video.. thank you...

[@federicobarra3655]

Thanks sir, outstanding explanation!

[@grace292]

Best simple explanation👏

[@avltrees1558]

best explanation ever

[@dr.eduardocalixto5148]

Very well done ! Thanks for your explanation !

[@ohyweqyaey]

Great video! Can you please consider making one on Gaussian Processes?

[@TheParkitny]

Great explanation thanks

[@FabianLandwehr]

Brilliant explanation!

[@frankhu4066]

Clearly explained, ever.

[@mahboobulrasool8333]

amazing one to learn about GMM. do you have any video related to the use of GMM in sensor fusion?

[@calm2584]

What a best way to use those crayolas

[@OyaUna]

Wow .... keep it up

[@jasperobico1459]

Thank you very much! That's was a very clear explanation!

[@prabirdas8430]

Great Explanation

[@sergioreyes5431]

Great video, outstanding job. Got me 99% of the way to understanding the concept. I have 1 question: During Expectation Maximization, you are recomputing Mu of k, Sigma of k, Pi of k. For Sigma of k, what does the variable T denote? Transpose?

[@aryashahdi2790]

Incredible !! Thanks gigantically

[@hadjerbounazou5248]

thanks a lot i really understand it!!!

[@hdrevolution123]

This was amazing

[@ilhsillhs2239]

Ritvik, thanks for explaining this. using Mclust package in R, what are the different parameters EEV etc.? what is BIC in the context of MM. This is awesome explanation. Using MM in mclust to classify small group of clusters that are distinct from population that carry significant meaning. Could you please introduce Dirichlet concept also as an extension to this class. Thank you.

[@EjazAhmed-pf5tz]

wow thank you so much sir

[@arunrajbhandari1656]

Thank you!!!!!!!!!!!!

[@mage2754]

Thank you

[@ritvikmath]

Welcome!

[@nutzeeer]

Does capital sigma have to be a 2x2 matrix? Width, length and angle can also be represented by 3 values.

[@АндрійПолішко-ш7е]

When you describe the value of P(x) as a "probability that we see x" did you mean "likelihood" instead of "probability"? Because the result of pdf function doesn't give the the probability but rather the likelihood that point x belongs to the distribution. Thanks for the video though. Good stuff!

[@oldfairy]

terrific

[@gabeguo6222]

So helpful!

[@ritvikmath]

Thanks!

[@timchang5676]

the fucking 🐐

[@nothj]

Woow

[@thejll]

Does this work for other distributions, like t-distros etc?

[@lancelofjohn6995]

Whether dos GMM with EM means, that at last we classify the point by the highest value from P(xi)?

[@parkerqi5543]

brilliant

[@nightskywatcher]

Could you please introduce the source for the math, especially for the derivatives part

[@alessandrorosati969]

In the multivariate gaussian mixture, how can insert the proportion in R?

[@justsomegirlwithoutamustac5837]

ur awesome!

[@ritvikmath]

You rock!

[@dhairya931]

Please provide R code for modelling the dependence between trial and suceess ( herons case by J Zhu) which include em algorithm of beta binomial poisson mixture model. Please help me

[@saraaltamirano]

I came here after breaking my brain reading books for 2 days... seems like I only needed 15 minutes.

[@rahulrawat5271]

Hi Ritvik, Great video but why gamma has a summation from 1 to k. I think it should be without summation as you said probability of being in class k given an observation. Please correct me if understood that correctly

[@codingblaze4611]

It's just a hyperparameter just like in case of K means

[@gulshanjangid3470]

Thank you!

[@hemantmahajan80]

Simplified explanation.

[@17dhawal]

What a boss

[@danieloh4511]

Is salmon lengthy than tuna?

[@tobias2688]

So is EM like maximum likelihood?

[@harshilchaudhary4916]

Please make a post prune video. Thanks!

[@notgoodatmathmmm6185]

nice :))

[@user-or7ji5hv8y]

What is capital N sub k?

[@ikhtiyor]

pls look at 10:16