Gaussian Mixture Models

Covariance matrix video: • The covariance matrix
Clustering video: • Clustering: K-means an...
A friendly description of Gaussian mixture models, a very useful soft clustering method.
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0:00 Introduction
0:13 Clustering applications
1:56 Hard clustering - soft clustering
3:36 Step 1: Colouring points
6:10 Step 2: Fitting a Gaussian
10:33 Gaussian Mixture Models (GMM)

[@trantrungnghia9642]

after 2 years, finally I found someone that explains covariance the good way

[@ppddeka5511]

Probably the most intuitive explanation of expectation maximization within gaussian mixture models . Cannot be more simple than this, just loved it

[@rebeccacarroll8385]

agreed!

[@tashfiashamim]

What an intuitive explanation! Kudos to you!

[@sharathnatraj]

I wish every complicated model is explained with this kind of simplicity.. amazing skill. Thanks a lot

[@maselyna313]

Being a visual learner, I'd say you are the best teacher ever! Thank you so much for this lesson it really helps a lot. Keep up the good work!

[@dragon5038]

Machine Learning Mastery

[@AnirbanDasgupta]

Never saw a better explanation of GMMs

[@saprativa]

best explanation on GMMs. Thank you.

[@sanjeevjangra84]

Can't Thank you enough for this great explanation. You made it look so simple :)

[@justin.c249]

Best video in explaining GMM in a not-so techical way that I come across so far!

[@donaastor]

DAMN this is exactly what I needed for my project. One of the best UA-cam's recommendations so far. Thank you.

[@Ninjasharkcat]

Wow excellent video, without any background on GMM I was able to understand the concept and logic behind it. Gracias!!

[@dhinas9444]

I love the clear visualizations! Thank you for your great work. :)

[@mr_possible6197]

This is the best channel for such ML stuff that I have come across by far! Thanks!!!

[@asdasvcxvwe5114]

Thank you VERY MUCH. i have been struggling with understanding the GMM for two whole days and no book or video could explain it very intuitively like you have done, i truly appreciate it

[@lancezhang892]

Thank you,professor,you have saved my life.

[@eliebordron5857]

This is so clear, thank you. perfect for learning quickly and in detail how Gaussian mixture models work.

[@nishitskunder]

After wasting time on all other videos I had lost hope to find a good video on this topic. Amazing use of visuals to support the explanation. You nailed it. Subscribed your channel too :)

[@land_sea]

Best video I found on understanding the top level concept of Gaussian Mixture Models, thanks!

[@dhruvrnaik]

Please keep making videos. I've never understood concepts better than when watching your videos

[@amrapalisamanta5085]

I just needed a beginner level understanding and this video was amazing.

[@subhrajitbhowmik7540]

Your videos are a great refresher brother. Really Appreciate your work.

[@minruihu]

tbh I look up tons of videos.this is the only one I can understand. it is so simple, with no terminology and clear explanation with visualization.

[@yingqu6932]

Great job on introducing a concept! Thank you 😊

[@reverse_engineered]

Great video Luis. I think this is a very clear and concise explanation of Gaussian Mixture Models.
One thing you could do to improve your videos is to focus on the audio quality. The volume levels varied considerably between parts, as did the sound of the vocals (echo, room tone), and the instrumental covered your voice for the first minute or so. Using a consistent recording setup with some curtains or sound absorbing foam will help to keep the reflections down and give a consistent sound. Using a compressor on your audio track and adjusting your levels both during recording and during mixing to get consistent audio levels will help to keep the volume consistent. A quick A/B listen of each part compared to each other will also help to tell if things are inconsistent and need to be adjusted.
I think your illustrations are excellent and the explanations are very clear. You seem to achieve a great balance between giving simplified explanations while still providing correct and accurate explanations.
Great job! I look forward to the next video. And I still plan to read over your grokking machine learning book too!

[@duoyizhang4665]

Thanks! Nice figures you made in the slide!

[@8eck]

Very informative and very helpful. Best explanation so far. Thank you!

[@yopedroaaron]

Luis, como siempre, ¡gracias!

[@pupsiklat1308]

The best video about GMM by far! Thank you!

[@sevsesi7866]

Thanks for your great explanation. This helps me understand GMM a lot!

[@ZinzinsIA]

Very clear and well illustrated, many thanks !

[@postnetworkacademy]

As always, clear and concise explanation.

[@abhishekarora4007]

Thank you for such an intuitive explanation! One of the best out there :)

[@thomasrimer1611]

Best video on GMMs. Thank you!

[@konconsu4013]

thanks, this is the first time I understant how it works!

[@dipjoy5178]

Thank you sir. Learning GMM from you helped me a lot

[@vaishnavejp9247]

the best video on this topic

[@grosmanph]

Thanks for sharing with us. Nicely done!

[@yulinsun8873]

Clearly explained thank you so much.

[@mariellakhoury3127]

Thank you for the video. It is extremely helpful for me as a visual learner.

[@hadjerbounazou5248]

thank you for the best explanation of GMMs

[@haroldsu]

Thank you, Sir. A great video on GMM.

[@anacarolinabarreto3712]

Very clear explanation!
Great video

[@aashwinsharma1859]

Great explanation. Wonderful video

[@ushasr2821]

Really awesome work sir.
Thank youuu sooo much sir. 🙂

[@Mahmoud_Gabr]

Thank you. Very helpful video!

[@blesucation4417]

Just want to leave a comment so that more people could learn from your amazing videos! Many thanks for the wonderful and fun creation!!!

[@think9824]

very good explanation. Thank You !

[@its_me7363]

Again a very informative video...Can You please make playlist explaining all machine learning algorithms and then deep learning? I know you are busy person...it's just that I and many people like myself really learn from your videos and if they are in order it's really easy to implement and become knowledgeable. Thanks for all your time and great videos.

[@SerranoAcademy]

Thank you! :)
I have everything organized by topic here: serrano.academy , otherwise, you can also look at the channel page: ua-cam.com/users/LuisSerrano, where a bunch of playlists appear more organized. I hope that helps. Happy learning!

[@its_me7363]

@@SerranoAcademy yes but is not complete.

[@neox357]

Superb! Am glad I held this long to read abt GMMs, till your explanatory video came 😄
Please do an Attention/Transformer video

[@SerranoAcademy]

Thank you, and thanks for the suggestion! Definitely been looking at attention/transformers. In the meantime, check out this material by a friend of mine jalammar.github.io/illustrated-transformer/

[@cheerioz83]

Fantastic explanation.

[@nikihooshmand2987]

Thank you for your great work! I really enjoy watching your videos:)

[@siyuancheng9575]

Jumped from the Standford cs229 class to this one, love the visualization, totally beats the Standford class

[@ayushagarwal69]

Loved the visuals , the maths part is so confusing to visualize !! Thanks

[@MH_HD]

Bravo, hope to see more videos like that. That was very nice explaining. Wish to see more especially Reinforcement Learning!

[@SerranoAcademy]

Thanks, glad you liked it!
Check this video out, it’s one I made on reinforcement learning! ua-cam.com/video/SgC6AZss478/v-deo.html

[@roshcoben8820]

Thank you so much!! I'd like to see how HMM-GMM are combined for applications such as acoustic modeling in speech recognition :) Muchas gracias!!

[@bryanbischof4351]

This was very good.

[@homayoungerami4176]

thanks, it was very well explained

[@vgreddysaragada]

very well explained

[@yashkapoor9511]

Excellent!

[@dtr_cpg]

Cool video!

[@lekanadenusi462]

Awesome content

[@yzaazou97]

Thank you!

[@reee896]

Awesome 🎉🎉

[@tomfreeman6741]

you are doing great

[@shravan6457]

Thanks Luis 🙏

[@SerranoAcademy]

@shravan6457, thank you so much for your really kind contribution, I really appreciate it! :)

[@MARTIN-101]

thank you.

[@oxfordsculler8013]

Your description is well explained, with clear visuals and with a good intuitive explanation of the subject. I encourage you to spend a little money on production values, better consistent sound quality, a better less intrusive intro music and these will move you into be there with Statquest and even ThreeBlueOneBrown. Good luck.

[@arshad1781]

Thanks 👍

[@youngzproduction7498]

That’s a good video.

[@thepythonprogrammer4338]

bruh thank you so much

[@lukeshchettri9945]

Thanks Luis! very good explanation. Here you assumed that you have two clusters to being with. In many real-life cases (for example: biological datasets), we do not know how many clusters are there. In those cases, I guess we have the number of clusters itself as a parameter, and we have to play with it till we get the right number of clusters, right? If yes then how can we be sure that we got the right number of clusters if we do not know the ground truth? Do we have to employ some kind of nonparametric model for such a case? What's the justification for assuming that each cluster can be modeled by Gaussian distribution?

[@SerranoAcademy]

Thanks Lukesh, great question! There are several methods that can be used to figure out the ideal number of clusters, although most are heuristical methods. A very common one is the elbow method. It is explained here: ua-cam.com/video/QXOkPvFM6NU/v-deo.html

[@haneulkim4902]

Thanks Luis, when is SVD not a good choice for reducing dimensionality?

[@tanphan3970]

Thank you so much!! But how to know the new Gaussian is not converging?

[@mauriciosalazar2733]

Luis, thanks for the video it is the same a expectation maximisation?

[@MrMannyCalavera]

I would say yes: it is the application of the general EM algorithm to this concrete problem, is it right?

[@mauriciosalazar2733]

@@MrMannyCalavera Gracias por la aclaración profe

[@MrMannyCalavera]

@@mauriciosalazar2733 It is just my guess, I'm waiting for the explanation of the boss :-) amazing channel!

[@nazrulhassan6310]

fabulous explanation. Most of the authors try explaining the subject in machine learning mathematically using jargon and symbols which become too hard to understand.

[@siyuancheng9575]

Oh, btw, there is a typo for the normal distribution pdf, the denominator should be sqrt(2*pi) * sigma

[@AbdulRahman-zp5bp]

I want to see the code implementation of GMM model

[@danielchacreton2401]

Your videos are great, but the music is a bit loud and can be distracting.

[@ridhamshah290]

How do you decide how many gaussian needed ?

[@spark2584]

can u turn the music up even higher? We can almost hear u

[@amritakaul87]

great video sir but it would be better without the background instrumental music

[@cernejr]

FYI: I had to skip past the portion with music - I am not able to follow math while listening to music.

[@ThinAirElon]

fyi... i liked music which motivates me

[@drdr3496]

Kill the music, my man :) Otherwise great video.

[@NoOneIsHereRightNow]

turn the volume of the music down pleaseeeeeee

[@jacobbarats1493]

69

[@oxfordsculler8013]

Please remove the music

[@AbdulrahmanAlNimari]

I like watching your videos but music is very noisy and distracting.

[@DerKurier6]

Please in the next Time, make the muisc quiter. The first chapter of the Video was very difficult to understand you

[@user-xw7ec3sf4w]

Horrible background music… please upload without music

[@javlontursunov6527]

Please remove the background music

[@miroslavdyer-wd1ei]

lose the piano music. then you're good to go

[@jinorohit2921]

amazingggggg video!