Happy I found this video.. even though it was rainy outside

@kebman

4 жыл бұрын

Happy I found this video.. even though there's a Corona lockdown :D

@pqppd8491

3 жыл бұрын

It's coincidentally rainy outside 😂

@a7md944

3 жыл бұрын

Based on previous experiance, because it is rainy at your side, i predict that you were probably not happy 😔

@TymexComputing

15 күн бұрын

@@a7md944 Bob was more likely not happy, we are the hidden state - whats the probability that the lockdown was not justified and that people were dying because of lack of medical help instead of the illness.

Usually Bayes Theorem and HMM are nightmare to even researchers. In this video these nightmares are made like child's play. I'm highly thankful for this service you are providing to the academic community- teachers, researchers and students. Keep it up Luis Serrano and hope to see many more plays in future!!!

your are one of those rarest breed of gifted teachers

You have just saved me, this was such a clear breakdown of Bayes Theorem and HMMs, and exactly what I needed at the 11th hour of a project I'm working on!

Beautiful work! It’s the most accessible introduction to Bayes inference I’ve seen. Great job! Please, keep them coming!

Thank you so much for this great video Luis. I am a Udacity alumni myself. I have watched & read many videos and articles on Bayes & HMMs, but your video by far is the best. It explains all the steps in the right amount of detail & does not skip any steps or switch examples. The video really helped solidify the concept, and giving the applications of these methods at the end really helps put them in context. Thank you again very much for your information & helpful video.

I can't believe how you did it so clear and simple. gorgeous

This is the best description of this topic I have ever seen. Crystal clear! True knowledge is when you can explain a complex topic as simple as this!

Being a teacher myself for long time all I can say is that this video is awesome! You have a talent my friend.

wow. perfect explanation . Even a kid can learn HMM by watching this video

I'm on a streak of watching your third video in a row and instantly liking it for outstandingly easy-to-understand breakdown of a quite complex topic. Well done, sir, I'll visit your channel in the future for sure! ✨

Your video tutorials are a great breakdown of very complex information into very understandable material. Thank You. It would be great if you could make a detailed video on PCA, SVD, Eginvectors, Random Forest, CV.

@jacobmoore8734

5 жыл бұрын

Eigenvectors and SVD for sure.

@ericbauer6595

4 жыл бұрын

@@jacobmoore8734 check out 3blue1brown's channel for the Essence of Linear Algebra. He explains that matrices are linear functions like y=f(x) or like a line 'y=mx', with y-intercept b=0. Eigenvectors are special inputs 'x' such that f(x) = kx, where k is some scalar coefficient (k is the eigenvalue associated with the special input x). For certain types of NxN matrices, (the covariance matrix used in PCA for example) are super interesting because any point in N-dimensional coordinates can be represented as a linear combination (ax1 + bx2+...) of the eigenvectors. The eigenvectors form a 'basis' for that space. This is where SVD (singular value decomposition) comes in. SVD essentially asks "instead of just multiplying x by your matrix, why don't you decompose this task into 3 easier tasks?" Let's say your matrix is C for covariance. Then SVD says that C = ULU' where U is made up of the eigenvectors for columns, U' is the transpose of U, and L is a diagonal matrix with the eigenvalues. Pretend we're doing y = C*x. Then first we do w = U'*x. This essentially represents x as a linear combination of eigenvectors. Said another way, you've changed the representation of point x from the original coordinate system to the eigenvector coordinate system. Next we do z = L*w, which scales every value of vector w by an eigenvalue. Some of these eigenvalues are very small and the result in z is perhaps closer to 0. Some of these eigenvalues are relatively large and upscale the result in z. Finally, when you do y = U*z, all you're doing it translating your scaled z vector back into the original coordinate system. So SVD basically splits a matrix into 3 different operations: 1. represents an input vector in terms of eigenvector coordinates 2. scales each coordinate by an eigenvalue 3. represents the scaled result back in terms of the original coordinates When you look at PCA (principal components analysis), you take your covariance matrix and decompose it to look at how much your eigenvalues scale the eigenvector coordinates. The largest eigenvalues correspond to the direction (eigenvector) of largest variation

@noduslabs

4 жыл бұрын

Definitely eigenvectors! Please!

@kapil_vishwakarma

4 жыл бұрын

Yes, please, do that.

@SaptarsiGoswami

4 жыл бұрын

You may have already found some, this is an attempt by University of Calcutta, not so coolly done, but please see if it makes sense kzread.info/dash/bejne/dWqaqpeHltLQZJM.html

Thank you so much for this video! I searched for hours, watched many videos, read many websites/ papers etc. but i never really understood what a HMM and the algorithms are and how they work. You explained everything from how it works to how to implement it so well that I got in 30 minutes, what i didnt get in hours before. Thank you so much!!

Simply amazing! After quite a long time struggeling to understand HHM now I finally get it. Thank you so much!!

Top notch and best explanations. You are taking complex subjects and making it intuitive not an easy thing to do !

It's impressing how simple you explain very complex issues! Thank you!!

Thank you so much for this. I wish more educators were more like you.

The most exciting thing I found in your video is that most of them is a one-stop solution for dummies like me, without the need to go to other 100 places to find 50 missing info pieces. Many thanks !

OMG! you are amazing! I consider myself as a information theory guy and should know this pretty well. But I can never present this idea as simple and easy understanding as you did! Great great job! I will for sure check around your other videos! Thank you!

@SerranoAcademy

5 жыл бұрын

Thank you Changyu!

Your videos are amazing! As someone who hasn't looked at calculus in 20 years, I find these "friendly introduction" videos extremely helpful in understanding high-level machine learning concepts, thank you! These videos really make me feel like this is something I can learn.

@generationgap416

Жыл бұрын

Isn't this opposite of calculus? Discrete vs continuous functions.

You are the best explainer I have found in youtube till now! Great work!

a beautiful combination of all the difficult concepts in probability in one video. great job.

Man Bayesian Theory has been having me for Breakfast! Thank you for this tutorial!

You made it so ease for learners... Appreciate the time you are spending in creating the content!!

best description about HMM, I had hard time to understand this topic, but your teaching keep me motivated for further learning.

I wish professors would just show this video in lectures... You are great at making these animations and your speech is perfect. Thank you!

Thank you so much! This video literally helps me understand 3 lectures in my machine learning class

I took a probability class and did badly. After recently finding out I'd need to revisit it for machine learning, I was a bit concerned. Then I come to understand an algorithm for Baye's Theorem!! How incredible, thank you!!

Thanks to your videos, I save a huge amount of time. Focusing on the intuition and mechanic allows an instant understanding BEFORE delving into the maths

So great by using sample example to explain confusing yet very important topics! Appreciate your excellent tutorial!

Thanks so much for this! It really helped with a research report I'm writing. Clear and easy to understand and the pacing was excellent for being able to take notes.

Nice job! Best explanation by now. Explained 6 weeks of my class in 32 minuts!

Amazing ... I just bought your book from Australia. Thank you for your time and effort!!!

Really hope to see more and more "friendly" videos from you ! Thanks a lot !

And please do a video on the baum-welch algorithms. Once again, no words to thank you! Happy New year!

Hi Luis Serrano thanks for the clear explanations, your informal way to explain this material is the best for us as a student, even my professor in Machine Learning class recommend this video for learning the HMM introduction!

I was going thru HMMs for robot localization and found this super clear explanation. Eres un fenomeno, Luis. Gracias!

I have a midterm in 8 hours and this video is the only thing that's really helped me so far. Cleared up all my confusions during 8 lectures in 32 minutes. Thank you so much, from the bottom of my heart.

@SerranoAcademy

6 жыл бұрын

Thank you for your note, I hope the midterm went great! :)

This is the best ever video you will find on HMM. Complicated concepts handled soooo wellll🥰

Omg. You just replaced an entire dry, non-understandable book for bioinformatics! I can’t thank you enough! It’s so easy!

this example made everything crystal clear, I have an exam tomorrow on HMM. Initially, I was anxious but after this video I'm sure I can solve any problem. Thank you very much, sir.

Best explanation of Hidden Markov Models on the Internet. Well done.

A very nicely done and visually appealing video on a slightly complex topic. Thank you!

Thank you for making this! Fantastic and easy-to-understand explanation of the topic.

I wasted the whole day understanding HMM model by watching useless youtube videos, untill I saw this. Thank you so much for this video. It is so simple and so intuitive. So very thankful to you :)

It was so nice with images! When you switched to letters, it was super clear how much easier it was to look at images!

Really amazing video that breaks down Bayes Theorem for simple understanding. Thanks Luis

This is the best video that explains HMM so simply to someone who doesn't have a computer science background. Godspeed to you

The best explanation of HMM ever! Very visual and easy to grasp. Enjoyed learning so much. Thanks! Edit: Can you please do a friendly video on EM algorithm, too?

This is the best explanation of HMM i ever seen up to now!

Very comprehensive and easily understandable. Even though I get increasingly impatient to watch the whole thing, I still managed to swing the thumb up.

Made my day...I learned Hidden Morkov Model for first ever time n guess wht? It was damn simple to understand the way explained.

I am a bio-organic chemist and we have a bioinformatics course which included Hidden Markov Model and your video helped me to learn the idea without immersing myself deep into mathematics. Thanks ...

Thanks Luis, I was taught HMMC using speech recognition, but will be having case study test on robot vacuums using this. I really appreciate it.

Thanks for the straightforward explanation of Bayesian networks + Hidden Markov Models. Cool stuff! Very powerful.

Excellent video, i remember looking at this on wikipedia and just not having a clue of what it meant, you did a fantastic job of explaining it!

Thank you so much! Your explanation and the way you presented the concept, was so crystal clear. Loved learning it.

Thank you so much for this beautiful explanation. learned about application of Bayes and Markov together ...Would happy to see more engineering application of these thermos..

This has taken me from 0 to 80% on HMM. Thanks for sharing

Thanks alot! I came across your video while searching for HMM-explanation for my computational biology course, and it helped a lot to understand the basic principle :)

Your videos are a real thing! Thank you very much for those explanations. That would be great if you could bring some videos on PCA, SVD, SVM and GMM.

I am a first time viewer but with such kind of amazing explanations, I will always stick to your teaching, vow so nicely explained!

Very good video! Simple examples make it very approachable and keeps it from being overwhelming

Dr Serrano, I think you are an embodiment of Feynman in ML education! Thanks a lot!!

In this video I explain what conditional probabilities are and I show how to calculate them in Excel and how to interpret them, using Solver to implicitly apply Bayes' theorem. Though in spanish, subtitles in english are available: kzread.info/dash/bejne/pKx8xpmtlJm-n5M.html.

Well illustrated. Thanks for putting this together.

Well, great video, the most fascinating thing is that you actually reacted to so many comments. That is so nice :)

I really like your example. It really helps with the understanding.

Excellent introduction into this topic. Thanks for your work.

Very easy to understand using Bob and Alice and the weather. Thanks.

As a feedback I would say your explanation is spot on .... A person with basic statistical knowledge can understand HMM with your explanation

Great job, I got it immediately. Very good illustrations too, simple and to the point

This video helped me a lot to understand these concepts and applications. Good job!

Super interesting explanation. I wish I had studied things like this when I was in school.

Very helpful and clear example and explanation. Thank you!

this is one of the best explanations of the HMM, it was very helpful to me, Thank you very much!

Nice video with clear explanation. I can see lot of work & heart put into making this video.

This video is really useful for me to learn HMM as well as probability calculation with algorithms. The example is easy to understand. Thank you so much.

my dad recommended i watch this, and i sure am thankful he did :D great video!

Your videos are very helpful and giving a good intuition of complex topics :) many thanks from Siberia

So I always just saw posts about HMM and I just decided to give your video a try and the explanations are just so fluid, I'm interested now

The content is excellent, so much hardwork ! Much appreciated :)

What a clear way of teaching. You're a total Rockstar of teaching stats. Ok, let's do the Baum-Welch algo

Excellent presentation. Simple to follow. I'll check out your book.

Really liked the video. Was looking to understand HMMs for neuron spiking and things are much clearer now.

Loved it. You are a great teacher. I was blessed finding your video first so I didn't waste any time 🥰

Thank you so much for share this video! It's the best explanation I found for this topic.

I was quite tensed when my supervisor pointed out to me that my master thesis should incorporate HMM. This video is my first introduction to HMM. You chased my fears away with your simple explanation and tone. Forever grateful

@carlosmspk

3 жыл бұрын

Similar situation here, I have a master thesis in anomaly detection, and using HMM is a candidate. I'm afraid it's much more complicated than this, but it sure made it look less scary

Great video! Thank you for spending time to make these videos.

Really Interesting and helpful video. Liked the way you took this topic from the basics and at the end moulded the concept of HMMs into the example making it so easy to understand and generalize to other sequences. Really appreciate the effort to make such a helpful and detailed video. Thank you for this resource. :)

Love this video, it has been extremely helpful for my research! Thank you!

THIS IS REALLY GOOD!!! Informative and easy to understand.

Excellent, excellent. Great job. Your all videos enlighning to all academicians

Dude, thanks a ton for explaining this so simply

Good Video easy explain those boring functions of hidden markov chain role and using example to explain is the best way

Thank you so much! This video was very well made, its made understanding the concept a cake walk.

Awesome explanation...Just perfect for practical application...really commendable job...Thanks a lot for the effort ....!

Absolutely magic. Thank you !

Very nicely explained. It takes a lot to teach a complex topic like HMM in such a simplistic way. Very well done. Thank you.

@generationgap416

Жыл бұрын

Did you mean in such a simple way?

Thank you for the effort you spent in making this great video