Hidden Markov Model Clearly Explained! Part - 5

[@cmfrtblynmb02]

I got my PhD back in 2000s. I wish material like this existed back then. I struggled so much with a lot of basic concepts. I had to look at material written in the most extreme mathematical notation without gaining any intuition. You have done an amazing job explaining something I really had hard time grasping. Well done.

[@amrdel2730]

yeah phd level is almost all time in use or link to some machine learnning theorie understanding i doin it now

[@kurghen988]

I thought exactly the same!

[@HeartDiamond_variedad]

En español no hay contenido así, tenemos que recurrir a videos en inglés

[@stryker2322]

because of people like you we alll are able to go deep into artificial intelligence and able to simplify what we are watching right now

[@RkR2001]

Great teaching Nerd. Where are You based now? ,I am a doctor from INDIA need your academic help

[@pradipkumardas2414]

I am 60 by age. I am learning how to simplify a complex subject. You are a great teacher! God bless you

[@NormalizedNerd]

Thanks a lot sir 🙏 I am very flattered.

[@BhargavSripada]

didnt ask

[@mr.nobody125]

@@BhargavSripada and we certainly didn't ask for your opinion

[@gracemerino3267]

Thank you for making these videos! I am starting my master's thesis on Markov Chains and these videos help me get a solid introduction to the material. Much easier to understand the books now!

[@abdullahalsulieman2096]

That is great. How is it so far? I want to apply it to predict system reliability. Not sure how complicated it is. Could you advise please?

[@xherreraoc]

This series of videos has been incredibly useful for me to write my Masters thesis. Thank you so much.

[@adaelasm6467]

This might be the fastest I've gone from never having used a concept to totally grokking it. Very well explained.

[@dheerajmakhija9752]

Very rare topic explained in most precise manner. Very helpful 👍

[@mertenesyurtseven4823]

Dude your videos are severly underappreciated. The animations, basic but complete style of you used when talking about abstract and complex topics. I just discovered this channel, and I will recommend all the people including beginners because even they will be able to understand as well as advanced people.

[@NormalizedNerd]

Thanks a lot for appreciating!!

[@mienisko]

I'm just going through the markov chain playlist and because of its quality I'm gonna subscribe to this channel. Great material!

[@HKwak]

Thanks for videos. I am majoring in Data Science and obviously videos like this sometimes help enormously comparing to reading texts. Very Intuitive and visual. I don't think I will forget the weather signs you showed us today.

[@NormalizedNerd]

That's the goal 😉

[@Portfolio-qb7et]

​​@@NormalizedNerdCan you briefly explain the steps involved in finding the probability of sunny day..I really don't understand

[@k.i.a7240]

Better explained than over 3 AI university classes I have gone through. Simple, efficient. Thank you

[@josippardon8933]

Great! Just Great! I really dont understand why most professors at colleges hate this type of explaining things. They always choose standard "lets be super formal and use super formal mathematical notation". Yes, it is important to learn formal mathematical things, but why not combine both formal and informal approach and put them together in textbook?

[@mayurdeo627]

Amazing job. This really helps if you are preparing for interviews, and want a quick revision. Thank you for doing this.

[@NormalizedNerd]

You're very welcome!

[@IndianDefenseAnalysis]

What a channel. Have never came across any Data Science channel like yours. You are doing a fantastic work. Love your videos and going thru them ❤

[@muneshchauhan]

Many thanks for the beautiful visualization and summarization of the Markov model. It was effortless understanding it. May require a little revision but comprehensible with ease. 🙂

[@songweimai6411]

Thank you, really appreciate your work. Watched this video let me consider that my professor in school class is not a good teacher.

[@aramisfarias5316]

After going through your Markov chains series, you my friend got yourself a new subscriber! Great work. Your channel deserves to grow!

[@ivanportnoy4941]

Loved it! I am looking forward to (maybe) seeing a video on the Markov Chain Monte Carlo (MCMC) algorithms. Best regards!

[@tatvamkrishnam6691]

Excellent! Skipped 4th part of this Magnificient Markov series. Took roughly 3hrs to verify at moments and coninvce myself. HIT MOVIE!!

[@Hangglide]

Hello author from the past! Your video is really helpful! Thank you!

[@Mosil0]

Hey, thanks a lot for making these! One suggestion if you don't mind: you could avoid using red and green (especially those particular shades you used) as contrasting colors, given that they're close to indistinguishable to about 8% of males. Basically any other combination is easier to tell apart, e.g. either of those colors with blue. Just a minor quibble, the videos are otherwise very good!

[@NormalizedNerd]

Thanks a lot for pointing this out. Definitely will keep this in mind.

[@sye9522]

Amazing!! It really helps me understand the logic behind those scary HMM python codes. Thank you.

[@brainnuke5450]

Don't stop what you are doing! It's amazing.

[@NormalizedNerd]

Thanks!! :D

[@asaha9479]

From the accent u r a bengali but r u from isi? GREAT vdo,keep going

[@NormalizedNerd]

@@asaha9479 Yup I'm a bong...But I don't study in ISI.

[@philipbutler]

this was the perfect video for where I'm currently at. I learned about Markov chains last year, and just finally got a good grasp on Bayes' (I struggled through Prob & Stats years ago). Thanks so much! keep it up!

[@NormalizedNerd]

That's amazing! :D

[@adrenochromeaddict4232]

you are one of the rare indian youtubers that don't have a god awful incomprehensible accent and you're also good at teaching. congrats

[@sreelatharajesh2365]

Wonderful video. Amazing explanation. Please explain why P(Y) is neglected? Or is considered as 1?

[@rodrigodiana1774]

arg max is computed by varying X, so we can neglect P(Y) because it's not varying on each iteration and will not change the final result

[@gregs7809]

A really well laid out video. Looking forward to watching more

[@NormalizedNerd]

Thanks! Keep supporting :D

[@girishthatte]

Awesome explanation ! Loved the way you explained Math used for calculations !

[@NormalizedNerd]

Thanks a lot! :)

[@debasiskar4662]

You explain things nicely. I would request you to make videos on advanced stochastic processes like semi-Markov Process, martingale etc.

[@ramchillarege1658]

Very nice and clear. Thank you.

[@noame]

Thank you for the clarity of explanation. Why did you neglect the denominator P(Y). How can we calculate it ? I assume that the correct arg max should take into consideration the denominator P(Y)

[@Ujjayanroy]

we are taking argmax of a function with X as the variables, so Y doesnt matter because of argmax...you can refer to bayes theorem for maximum likelihood, they always do the same thing

[@ViralPanchal97]

Thanks a ton, I wish my professors from Monash Uni taught this way.

[@zoegorman8233]

Super entertaining videos helping me with my Oxford master's thesis. Study night or movie night? Plus he has an awesome accent :-)

[@NormalizedNerd]

Thanks a lot mate! :D :D

[@ITTOMC]

Simply wonderful. Keep up your excellent work. Really really well done!

[@aveenperera3128]

please do more examples for hidden markov chains

[@VersatileAnthem]

superb explanations ! that shows how in depth your knowledge is !

[@Louisssy]

Really cool explanation! Can you also explain why is P(Y) ignored?

[@NormalizedNerd]

Because of two reasons... 1. It's often hard to compute P(Y) 2. To maximize that expression we only need to maximize the numerator (depends on X). Note that P(Y) doesn't depend on X.

[@ts-ny6mx]

@@NormalizedNerd Thank you! I had the same concern and your explanation makes sense!

[@mariamichela]

@@NormalizedNerd 1. can't we just compute P(Y) as P(X1,Y) x P(X1) + P(X2,Y) x P(X2) + P(X3,Y) x P(X3) ? 2. true, I agree. Since you didn't say it in the video I was just wondering where did P(Y) disappear, and didn't bother to think that the max was actually over X

[@tyrannicalguy7262]

"Hello People From The Future!" that was very thoughtful

[@vocabularybytesbypriyankgo1558]

Great explanation, super helpful

[@artemiosantiagopadillarobl4280]

Great videos, keep it up! :) I would be nice to have a video about MCMC (Markov Chains via Monte Carlo) and the Metropolis-Hastings algorithm

[@NormalizedNerd]

Great suggestion.

[@nonconsensualopinion]

@@NormalizedNerd I'm on the edge of subscribing. A video on MCMC would convince me to subscribe and never leave!

[@NormalizedNerd]

@@nonconsensualopinion 😂😂...Let's see what comes next

[@tetamusha]

I got a little confused with the two HMM videos. I thought the second video would solve the argmax expression presented at the end of the first one, but the algorithm that solves this expression is the Viterbi algorithm and not the Forward algorithm from the second video. Just a heads-up to those that got a little lost like me.

[@abdsmmeat7492]

We need more videos on Markov chains

[@asmaaziz2436]

Explained so simply. Well done. Helped me a lot.

[@NormalizedNerd]

Great to hear!

[@xiaoruidu8603]

please keep updating~ you are doing an amazing job~~.

[@NormalizedNerd]

Sure I will

[@varshachinnammal]

Dear sir, Your explanation was very well explained and understandable. It is full of mathematics coming here matrix, probability and so. I'm from a science background without maths. I needed this for bioinformatics but it is difficult to compare with nitrogenous bases with these matrix and formulae. Will you explain it in simpler method? It would be very helpful sir 🥺

[@freenrg888]

Beautiful! Thank you. Question: in the final formula, "arg max(over X) Prod P(Yi | Xi) P(Xi | Xi-1)" ... We have a product term P(X1 | X0) the assumes there is an X0 value. However, there is no X0. Don't we need to replace this term with a different expression that does not rely on X0?

[@ishhanayy]

Very well explained ! 👏👏

[@truongsonmai6915]

Very helpful video as well as the rest of the Markov series. Wish you luck from Vietnam!

[@NormalizedNerd]

Thanks a lot mate :)

[@Gamer2002]

You should make more videos, you are awesome

[@muqaddaszahranaqvi5585]

Finally, some video helped.

[@najeebjebreel2885]

Nice explanation. Thanks.

[@surajpandey86]

I really like the visuals and your presentation with the content. Good work!

[@NormalizedNerd]

Glad you like them!

[@mika-ellajarapa5646]

hello!!!! please explain conditional random field :( thank you

[@Garrick645]

As soon as the emoji's left, the video went over my head

[@streamindigoflags8035]

Thanks a lot! These type of videos are amazing and helps me understand the concepts in good way that are there in the books. It boosts my interest to this area. It helps me a lot in doing my project! U make these kind of videos for almost all concepts!😍

[@Sam-rz5hw]

excellent explanation!

[@acesovernines]

Superb explanation

[@keithmartinkinyua2067]

this video is good but it left me hanging. I was expecting you to calculate the probability at the end.

[@MB-rc8ie]

This is gold. Thanks for uploading. Very helpful

[@NormalizedNerd]

You're welcome :D

[@nad4153]

nice video ! but what happen to P(Y) at 8:30 in the final formula, why does it disappear ?

[@papa01-h2z]

We want to find the X_1, ..., X_n that gives us the maximum value. Note that P(Y) does not depend on the Xs and is therefore a constant. A constant does not change the Xs that give us the maximum.

[@islemsahli737]

Thank you well explained

[@ameyapawar7097]

Very well explained!

[@barhum5765]

Bro you are a king

[@aadi.p4159]

ssuper super good. awesome work bro

[@thejswaroop5230]

hey you from the past....good video

[@nchsrimannarayanaiyengar8003]

very nice explanation thank you

[@丰岛龙健]

best videos！ hope to see more of the videos about markov chain! Thank you

[@NormalizedNerd]

Sure thing!

[@dannymoore1530]

This was good! Thank you.

[@shivkrishnajaiswal8394]

good one

[@stefankermer7782]

this is great - had to laugh a lot when you inroduced indices to the matrices haha

[@Cookies4125]

Thanks for the explanation! You went through the math of how to simplify \argmax P(X=X_1,\cdots,X_n\mid Y=Y_1,\cdots,Y_n) but how do you actually compute the argmax once you've done the simplification? There must be a better way than to brute force search through all combinations of values for X_1,\cdots,X_n right?

[@ThePablo505]

something that looks so hard, you make it understandable even for a 4 years old kid

[@elixvx]

thank you for your videos! please continue the great work!

[@NormalizedNerd]

Thanks, will do!

[@TariqulHasan-p2y]

HI.. thanks for wonderful videos on Markov Chain. I just want to know how do you define the probability of transition state and emission state? What to do about unknown probabilities of state?. Regards

[@annanyatyagip]

excellent video!

[@nakjoonim]

You deserve my subscription thanks a lot!

[@NormalizedNerd]

:D :D

[@hyeonjusong1159]

You have a knack for teaching! The explanation was clear and the example along with the emojis was so cute! Thank you!!!

[@lucasqwert1]

How do we calculate the stationary distribution of the first state? I watched your previous videos but still cant calculate it! Thanks for answering!

[@rohanthakrar7599]

can you explain the calculation of P(X|Y), the last step of the video when you put inside the products of P(Y|X) and P(X|X). Where does the P(Y) in the denominator go? Thanks

[@Periareion]

Howdy! I think of it like this: P(Y) is a constant, and doesn't affect which sequence X has the highest probability of occurring. In other words: since every term gets divided by P(Y), we can just ignore it. Perhaps he could've made that a little clearer in the video. Cheers!

[@michaeljfigueroa]

Excellent

[@mediwise2474]

Sir pl.make a video on finite element method

[@jasonhuang2270]

Thank you so much!

[@deepayaganti7653]

Clearly explained.superb

[@NormalizedNerd]

Glad you liked it

[@jonathanisai9286]

Amazing video bro! you rock!

[@NormalizedNerd]

Thanks bro!

[@vrl9037]

Excellent explanation

[@NormalizedNerd]

Glad you liked it!

[@MERIEMELBATOULEDDAIF]

hiii please i just want to know the tool you create those exemples with , its urgent save me

[@stephenchurchill3929]

Great Video, Thanks!

[@NormalizedNerd]

You're welcome!

[@minjun87]

Thanks for the great video. What would be the hidden state sequence data and observed sequence data in speech to text use case?

[@malikabdulsalam6628]

highly appriciated Sir. Can you please share your ppt slide.

[@marcovieira8356]

Thank you for the vid, it is probably the arg max (understandable) available around. But something remains unclear for me. You said at 09:30 that one Markov property is that X_i "depends only of X_i-1 but Markov property I know is the opposit: X_i is independent of X_i-1 (future does not depends on past, just on the current state). Where I am missing the point?

[@user-se9uk2py5k]

Really good explanation ! Thank you!

[@NormalizedNerd]

Glad it was helpful!

[@xxanton8xx]

Thanks for the awesome video!

[@sudhasenthilkumar335]

kindly provide video for viterbi and forward backward algorithm

[@24CARLOKB]

Great video! Just a little lost where you get the prob(sad or happy | weather), which I think are emission probabilities? Thanks!

[@jtanium]

I've been going through these videos and doing the calculations by hand to make sure I understand the math correctly. When I tried to calculate the left eigenvector ([0.218, 0.273, 0.509]) using the method described in the first video (Mark Chains Clearly Explained Part 1), I got a different result ([0.168, 0.273, 0.559]), and I'm wondering if I missed a step. Here's what I did: starting with [0 0 1] meaning it is sunny, pi0A = [0.0 0.3 0.7], pi1A = [0.12 0.27 0.61], pi2A = [0.168, 0.273, 0.559]. It's interesting the second element matches. If anyone might help me understand where I went wrong, I'd greatly appreciate it!

[@sushobhitrathore2555]

you must start the probability of states as [0 1 0].....you will get the same values

[@angadbagga9166]

@@sushobhitrathore2555 - lol why 010 ..sunny is at last position. And even if we go through your way we don't get the right result. Result of pi2A as per you funda is [0.252, 0.272, 0.476]

[@abhivaryakumar3107]

Amazing video I really needed that and thank you so so much for explaining everything so clearly. If I may have one request, could you please share the python code?? Thank you so so much

[@bassami74]

Thank you for sharing, could you please explain how to implement HMM on measuring the earnings quality. Need your help 🙏🙏

[@festusboakye3698]

Thank you so much for making this video. Could you please extend HMM to treeHMM using Forward algorithm to make inferences.

[@Extra-jv5xr]

Is this related to the viterbi algorithm? Could you make a video on that?

[@DungPham-ai]

Thank so much .Can you make video about conditional random fields ?

[@NormalizedNerd]

TBH I haven't explored that yet...If I do, I'll make a video 😌

[@meriembaha6887]

Thank you