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!
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 :)
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
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.
Thank you! :) I have everything organized by topic here: serrano.academy , otherwise, you can also look at the channel page: th-cam.com/users/LuisSerrano, where a bunch of playlists appear more organized. I hope that helps. Happy learning!
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/
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.
Questions: How to use fraction point to create new Gaussians, Example lets say we are in 2D with x1=2, y=8 and we find this point belongs to a Gaussian with 60% next how to use What should I do x1 = 2 * .6, y1 = 8 * .6 sort of ?? Please provide clarity on Hypothesis.
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?
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: th-cam.com/video/QXOkPvFM6NU/w-d-xo.html
fabulous explanation. Most of the authors try explaining the subject in machine learning mathematically using jargon and symbols which become too hard to understand.
Probably the most intuitive explanation of expectation maximization within gaussian mixture models . Cannot be more simple than this, just loved it
agreed!
I wish every complicated model is explained with this kind of simplicity.. amazing skill. Thanks a lot
after 2 years, finally I found someone that explains covariance the good way
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!
Machine Learning Mastery
Never saw a better explanation of GMMs
Best video in explaining GMM in a not-so techical way that I come across so far!
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!
DAMN this is exactly what I needed for my project. One of the best TH-cam's recommendations so far. Thank you.
This is the best channel for such ML stuff that I have come across by far! Thanks!!!
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 :)
Can't Thank you enough for this great explanation. You made it look so simple :)
Best video I found on understanding the top level concept of Gaussian Mixture Models, thanks!
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.
Just want to leave a comment so that more people could learn from your amazing videos! Many thanks for the wonderful and fun creation!!!
Thank you,professor,you have saved my life.
I love the clear visualizations! Thank you for your great work. :)
Please keep making videos. I've never understood concepts better than when watching your videos
What an intuitive explanation! Kudos to you!
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
I just needed a beginner level understanding and this video was amazing.
The best video about GMM by far! Thank you!
Thank you for the video. It is extremely helpful for me as a visual learner.
the best video on this topic
Thanks! Nice figures you made in the slide!
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.
Thank you! :)
I have everything organized by topic here: serrano.academy , otherwise, you can also look at the channel page: th-cam.com/users/LuisSerrano, where a bunch of playlists appear more organized. I hope that helps. Happy learning!
@@SerranoAcademy yes but is not complete.
thanks, this is the first time I understant how it works!
Your videos are a great refresher brother. Really Appreciate your work.
thank you for the best explanation of GMMs
Thank you sir. Learning GMM from you helped me a lot
Clearly explained thank you so much.
Jumped from the Standford cs229 class to this one, love the visualization, totally beats the Standford class
Superb! Am glad I held this long to read abt GMMs, till your explanatory video came 😄
Please do an Attention/Transformer video
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/
best explanation on GMMs. Thank you.
Best video on GMMs. Thank you!
Fantastic explanation.
Great explanation. Wonderful video
As always, clear and concise explanation.
Loved the visuals , the maths part is so confusing to visualize !! Thanks
Thank you. Very helpful video!
Great job on introducing a concept! Thank you 😊
Very clear and well illustrated, many thanks !
Thanks for your great explanation. This helps me understand GMM a lot!
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!!
Luis, como siempre, ¡gracias!
Thank you, Sir. A great video on GMM.
Very informative and very helpful. Best explanation so far. Thank you!
Bravo, hope to see more videos like that. That was very nice explaining. Wish to see more especially Reinforcement Learning!
Thanks, glad you liked it!
Check this video out, it’s one I made on reinforcement learning! th-cam.com/video/SgC6AZss478/w-d-xo.html
Very clear explanation!
Great video
very well explained
Thanks for sharing with us. Nicely done!
Really awesome work sir.
Thank youuu sooo much sir. 🙂
Thank you for such an intuitive explanation! One of the best out there :)
Thanks Luis 🙏
@shravan6457, thank you so much for your really kind contribution, I really appreciate it! :)
Thank you for your great work! I really enjoy watching your videos:)
very good explanation. Thank You !
Thank you!
Excellent!
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.
thanks, it was very well explained
Awesome content
you are doing great
Thanks 👍
amazingggggg video!
Awesome 🎉🎉
Questions: How to use fraction point to create new Gaussians, Example lets say we are in 2D with x1=2, y=8 and we find this point belongs to a Gaussian with 60% next how to use What should I do x1 = 2 * .6, y1 = 8 * .6 sort of ?? Please provide clarity on Hypothesis.
Thank you so much!! But how to know the new Gaussian is not converging?
This was very good.
I want to see the code implementation of GMM model
bruh thank you so much
Thanks Luis, when is SVD not a good choice for reducing dimensionality?
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?
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: th-cam.com/video/QXOkPvFM6NU/w-d-xo.html
Your videos are great, but the music is a bit loud and can be distracting.
How do you decide how many gaussian needed ?
can u turn the music up even higher? We can almost hear u
That’s a good video.
great video sir but it would be better without the background instrumental music
FYI: I had to skip past the portion with music - I am not able to follow math while listening to music.
fyi... i liked music which motivates me
Luis, thanks for the video it is the same a expectation maximisation?
I would say yes: it is the application of the general EM algorithm to this concrete problem, is it right?
@@MrMannyCalavera Gracias por la aclaración profe
@@mauriciosalazar2733 It is just my guess, I'm waiting for the explanation of the boss :-) amazing channel!
Kill the music, my man :) Otherwise great video.
Please in the next Time, make the muisc quiter. The first chapter of the Video was very difficult to understand you
turn the volume of the music down pleaseeeeeee
I like watching your videos but music is very noisy and distracting.
Please remove the music
Horrible background music… please upload without music
69
Please remove the background music
lose the piano music. then you're good to go
Wow excellent video, without any background on GMM I was able to understand the concept and logic behind it. Gracias!!
This is so clear, thank you. perfect for learning quickly and in detail how Gaussian mixture models work.
Cool video!
fabulous explanation. Most of the authors try explaining the subject in machine learning mathematically using jargon and symbols which become too hard to understand.
Oh, btw, there is a typo for the normal distribution pdf, the denominator should be sqrt(2*pi) * sigma