Hidden Markov Models 12: the Baum-Welch algorithm

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  • Опубликовано: 24 дек 2024

Комментарии • 128

  • @059812
    @059812 4 года назад +101

    Stop searching, this is the best HMM series on youtube

  • @kevinigwe3143
    @kevinigwe3143 4 года назад +22

    Thoroughly explained. The best series I have seen so far about HMM. Thanks

    • @djp3
      @djp3  4 года назад

      Great to hear!

  • @ligengxia3423
    @ligengxia3423 3 года назад +2

    I don't think anyone is gonna hit a dislike button on this series of video. Prof Patterson truly explained the abstract concept from an intuitive point of view. A million thanks Prof Patterson!

  • @onsb.605
    @onsb.605 3 года назад +3

    You are definitely a life saviour! One can be studying about EM and HMM for a long while, but the need to go back to the basics is always there.

  • @simonlizarazochaparro222
    @simonlizarazochaparro222 Год назад +1

    I love you! I listened the lecture of my professor and I couldn't even understand what they were trying to say. I listened to you and things are so clear and easily understandable! I wish you were my professor! Also very entertaining!

    • @djp3
      @djp3  Год назад +1

      Glad I could help!

  • @benjaminbenjamin8834
    @benjaminbenjamin8834 3 года назад +1

    This is the best series on HMM, not only the Professor explains the concept and working of HMM but most importantly he teaches the core Mathematics of the HMM.

  • @marlene5547
    @marlene5547 4 года назад +5

    You're a lifesaver in these dire times.

  • @rishikmani
    @rishikmani 4 года назад +8

    whoa, what a thorough explanation. Finally I understood what Xi is! Thank you very much sir.

    • @djp3
      @djp3  4 года назад +1

      Glad it was helpful! I wish I had pronounced it correctly.

  • @veronikatarasova1314
    @veronikatarasova1314 Год назад +2

    Very interesting, and the examples and the repetitions made clear the topic I thought I would never understand. Thank you very much!

    • @djp3
      @djp3  Год назад +1

      You're very welcome!

  • @vaulttech
    @vaulttech Год назад +1

    There is a good chance that I am wrong, but I think that your description of Beta is backwards. You say (e.g., at 7:40 ) it answers "what is the probability that the robot is here knowing what is coming next", but it should be "what is the probability of what is coming next, knowing that I am here". (in any case, thanks a lot! I am trying to learn this in details, and I found the Rabiner paper quite hard to digest, so your videos are super helpful)

  • @idiotvoll21
    @idiotvoll21 3 года назад +2

    Best video I've seen so far covering this topic! Thank you!

    • @djp3
      @djp3  3 года назад

      Glad it was helpful!

  • @hannahalex3789
    @hannahalex3789 16 дней назад

    One of the best videos on Baum-Welch!!

  • @linkmaster959
    @linkmaster959 3 года назад +3

    One of the main things that has always confused me with HMM's is the duration T. For some reason, I thought the duration T needed to be fixed, and every sequence needed to be the same duration. Now, I believe I finally understand the principles of the HMM. Thank you!

  • @karannchew2534
    @karannchew2534 2 года назад

    14:30 Why is bij (Ot+1) needed?
    aij = the probability of moving from state_i to state_j
    βt+1(j) = probability of being at state_j at time t+1

  • @vineetkarya1393
    @vineetkarya1393 4 месяца назад

    I completed the course today and it is still the best free material for learning hmm. Thankyou professor

    • @djp3
      @djp3  4 месяца назад

      I'm glad it was helpful. This is a tough concept

  • @barneyforza7335
    @barneyforza7335 3 года назад +1

    This video comes up so far down on the searches but is good (best) xx

  • @sheepycultist
    @sheepycultist 3 года назад +2

    My bioinformatics final is in two days and im completely lost, this series is helping a lot, thank you!

    • @djp3
      @djp3  3 года назад

      Good luck. Hang in there! There's no such thing as "junk" DNA!

  • @ribbydibby1933
    @ribbydibby1933 2 года назад +1

    Doesn't get much clearer than this, really easy to follow!

  • @garimadhanania1853
    @garimadhanania1853 3 года назад +3

    best lecture series for HMM! Thanks a lot Prof!

  • @SStiveMD
    @SStiveMD 2 года назад +1

    Astonishing explanation! Now I can resolve and understand better my homework for Knowledge Representation and Resoning

    • @djp3
      @djp3  2 года назад

      Glad it was helpful!

  • @bengonoobiang6633
    @bengonoobiang6633 2 года назад +1

    Very interesting to understand the signal alignment. Thanks

  • @comalcoc5051
    @comalcoc5051 8 месяцев назад +1

    Thanks proff
    really help me understand HMM on my research. Hope you have a good life

    • @djp3
      @djp3  4 месяца назад

      Pay it forward!

  • @AmerAlsabbagh
    @AmerAlsabbagh 4 года назад +3

    Your lectures are great, thanks, one note is that, beta is wrongly expressed in your video, and it should be the following:
    β is the probability of seeing the observations Ot+1 to OT, given that we are in state Si at time t and given the model λ, in other words, what is the probability of getting a specific sequence from a specific model if we know the current state.

    • @djp3
      @djp3  3 года назад

      That sounds right. did I misspeak?

    • @konradpietras8030
      @konradpietras8030 Год назад

      @@djp3 In 7:00 u said that beta captures the probability that we would be in a givent state knowing what's going to come in the future. So it's the other way round, you should condition on current state not future observations.

  • @SPeeDKiLL45
    @SPeeDKiLL45 2 года назад +1

    Thanks so much. Very talented in explaining complex things.

  • @mindthomas
    @mindthomas 4 года назад +3

    Thanks for a thorough and well-taught video series.
    Is it possible to download the slides anywhere?

  • @matasgumbinas5717
    @matasgumbinas5717 4 года назад +22

    There's a small mistake in the equation for the update of b_j(k), see 22:37. In both, the denominator and the numerator, gamma_t(i) should be gamma_t(j) instead. Other than that, this is a fantastic series!

    • @djp3
      @djp3  3 года назад +5

      Yup you are right. THanks for the catch

  • @preetgandhi1233
    @preetgandhi1233 4 года назад +5

    Very clear explanation, Mr. Ryan Reynolds....XD

  • @Steramm802
    @Steramm802 3 года назад +2

    Excellent and very intuitive explanations, thanks a lot for this amazing Tutorials!

  • @leonhardeuler9028
    @leonhardeuler9028 4 года назад +1

    Thanks for the great Series. This series helped me to clearly understand the basics of HMMs. Hope you'll make more educative videos!
    Greets from Germany!

    • @djp3
      @djp3  3 года назад

      Glad it was helpful!

  • @dermaniac5205
    @dermaniac5205 2 года назад

    05:45 is this the right interpretation of alpha? Alpha is P(O1...Ot, qt=Si), which is the probability of observing O1..Ot AND being in state Si at timepoint t. But you said it is the probability of being in state Si at timepoint t GIVEN the Observations O1..Ot. That would P(qt=Si | O1...Ot) which is different.

  • @teemofan7056
    @teemofan7056 Год назад +2

    Oh welp there goes 10000 of my brain cells.

    • @djp3
      @djp3  Год назад

      Hopefully 10,001 will grow in their place!

  • @sahilgupta2210
    @sahilgupta2210 Год назад +2

    Well this was one of the best playlists I have gone through to pass my acads :) lol

  • @shabbirk
    @shabbirk 3 года назад +2

    Thank you very much for the wonderful series!

  • @IamUSER369
    @IamUSER369 4 года назад +4

    Great video, thanks for clearing up the concepts

    • @djp3
      @djp3  4 года назад +1

      My pleasure!

  • @benjaminbenjamin8834
    @benjaminbenjamin8834 3 года назад +8

    I wish Professor could also implement those concepts in python notebook also.

    • @djp3
      @djp3  2 года назад

      there is a package called hmmlearn in conda-forge that has an implementation.

  • @voxgun
    @voxgun 2 года назад +1

    Thankyou so much for sharing Prof !

    • @djp3
      @djp3  2 года назад

      You’re welcome!

  • @iAmEhead
    @iAmEhead 4 года назад +1

    Echoing what others have said... great videos, very useful. If you feel inclined I'd love to see some on other CS topics.

  • @xntumrfo9ivrnwf
    @xntumrfo9ivrnwf 2 года назад +1

    "... 2 dimensional transition matrix (in principle)..." --> could anyone help with an example where e.g. a 3D transition matrix is used? Thanks.

    • @djp3
      @djp3  2 года назад +1

      Moving through a skyscraper. Going from x,y,z to a new x,y,z

  • @oriion22
    @oriion22 4 года назад +1

    Hi Donald, Thanks for putting this easy to understand HMM series. I wanted to know a little bit more on how to apply it in other fields. How can I connect with you to discuss this.

    • @djp3
      @djp3  3 года назад

      Twitter? @djp3

  • @samlopezruiz
    @samlopezruiz 3 года назад +1

    Amazing series. Very clear explanations!

  • @sanketshah7670
    @sanketshah7670 2 года назад +1

    thank you so much for this....this is better than my ivy league tuition

    • @djp3
      @djp3  2 года назад

      Glad it helped!

  • @minhtaiquoc8478
    @minhtaiquoc8478 4 года назад +4

    Thank you for the lectures. The sound at the beginning and the end is really annoying though

  • @edoardogallo9298
    @edoardogallo9298 4 года назад +2

    WHAT A SERIES! that is a teacher..

    • @djp3
      @djp3  4 года назад

      thanks!

  • @timobohnstedt5143
    @timobohnstedt5143 3 года назад +1

    Excellent content. If I got it right, you state that the EM-algorithm is called gradient ascent or decent. If I got it right, this is not the same. The algorithms result can be in the same local optima, but they are not the same.

    • @djp3
      @djp3  3 года назад

      if you abstract the two algorithms enough they are the same. But most computer scientists would recognize them as different algorithms that both find local optima.

  • @Hugomove
    @Hugomove Год назад +1

    Great explained, thank you very very much!

    • @djp3
      @djp3  Год назад

      Glad it was helpful!

  • @harikapatel3343
    @harikapatel3343 4 дня назад

    You explained it so well.... thank you so much

  • @arezou_pakseresht
    @arezou_pakseresht 3 года назад +1

    Thanks for the AMAZING playlist!

    • @djp3
      @djp3  3 года назад +1

      Glad you like it!

  • @akemap4
    @akemap4 3 года назад

    One thing I cannot understand. If gamma is the sum of zeta over all j, then how can gamma have the dimension of T. If zeta only goes from 1 to T?

    • @alexmckinney5761
      @alexmckinney5761 3 года назад +1

      I noticed this too, it is better to use the alternate formulation for gamma, which is \gamma_t(i) = \alpha_t(i) * \beta_t(i) / \sum_i (\alpha_t(i) * \beta_t(i)). This should give you the correct dimension

    • @djp3
      @djp3  3 года назад

      there is a matrix of gamma's for each t and each i and a 3-D matrix Xi's for each t,i,j. Each gamma_t is the sum over as set of Xi's at that time. You could also notate gamma as being gamma(t,i) and Xi and Xi(t,i,j)

    • @akemap4
      @akemap4 3 года назад

      @@alexmckinney5761 yes. I did it. However I still am getting error in my code. My a matrix goes to 1 on one side and zero on the other side. I am still trying to figure out the problem, but without success till then.

  • @lakshmipathibalaji873
    @lakshmipathibalaji873 Год назад +1

    Thanks for such a great explanation

    • @djp3
      @djp3  Год назад

      Glad it was helpful!

  • @quonxinquonyi8570
    @quonxinquonyi8570 2 года назад +1

    Simply brilliant

  • @hariomhudiya8263
    @hariomhudiya8263 4 года назад +1

    That's some quality content, great series

    • @djp3
      @djp3  3 года назад

      Glad you enjoy it!

  • @danilojrdelacruz5074
    @danilojrdelacruz5074 Год назад +1

    Thank you and well explained!

    • @djp3
      @djp3  Год назад

      Glad you enjoyed it!

  • @alikikarafotia4788
    @alikikarafotia4788 2 месяца назад

    Amazing series.

  • @myzafran1
    @myzafran1 4 года назад +1

    Thank you so much for your very clear explanation.

  • @edwardlee6055
    @edwardlee6055 3 года назад +2

    I get through the vedio series and feel rescued.

  • @sanketshah7670
    @sanketshah7670 2 года назад

    it seems you're mixing up gamma and delta?

    • @djp3
      @djp3  2 года назад

      Possibly, do you mean the slides are wrong or I am misspeaking? I'm really bad with my Greek letters.

    • @sanketshah7670
      @sanketshah7670 2 года назад

      @@djp3 no just delta is viterbi, not gamma, i think you say gamma is viterbi.

  • @lejlahrustemovic541
    @lejlahrustemovic541 2 года назад +1

    You're a life saver!!!

  • @parhammostame7593
    @parhammostame7593 4 года назад +1

    Great series! Thank you!

  • @hayoleeo4891
    @hayoleeo4891 9 месяцев назад

    Thank you so much! I found it so hard to understand baum welch!

    • @djp3
      @djp3  4 месяца назад

      You're very welcome!

  • @fgfanta
    @fgfanta 7 месяцев назад

    Quite the tour de force, thank you!

    • @djp3
      @djp3  4 месяца назад

      ha!

  • @Chi_Pub666
    @Chi_Pub666 8 месяцев назад

    You are the goat of teaching bw algorithm🎉🎉🎉

  • @VishnuDixit
    @VishnuDixit 4 года назад +2

    Amazing playlist
    Thanks

  • @anqiwei5784
    @anqiwei5784 4 года назад +1

    Wow! This video is so great!!!

    • @djp3
      @djp3  3 года назад

      Thank you so much!!

  • @pauledson397
    @pauledson397 2 года назад +2

    Ahem: "ξ" ("xi") is pronounced either "ksee " or "gzee". You were pronouncing "xi" as if it were Chinese. But... still a great video on HMM and Baum-Welch. Thank you!

    • @djp3
      @djp3  2 года назад +2

      Yes you are correct. I'm awful with my Greek letters.

  • @toopieare
    @toopieare Месяц назад

    Thank you professor!

  • @punitkoujalgi7701
    @punitkoujalgi7701 4 года назад +2

    You helped a lot.. Thank you

  • @naveenrajulapati3816
    @naveenrajulapati3816 4 года назад

    Great explanation sir...Thank You

    • @djp3
      @djp3  3 года назад

      You're most welcome

  • @AakarshNair
    @AakarshNair 2 года назад +1

    Really helpful

  • @markusweis295
    @markusweis295 4 года назад +22

    Thank you! Nice video. (You look a bit like Ryan Reynolds)

    • @djp3
      @djp3  4 года назад

      You think so? Amazon's automatic celebrity recognizer thinks I look like Shane Smith (at least with my beard)

    • @threeeyedghost
      @threeeyedghost 4 года назад +2

      I was thinking the same for the whole video.

    • @anqiwei5784
      @anqiwei5784 4 года назад

      Haha I think it's more than just a bit

  • @snehal7711
    @snehal7711 9 месяцев назад

    greatttttt lecture indeed!

  • @abdallahmahmoud8642
    @abdallahmahmoud8642 4 года назад +2

    Thank you!
    You are truly awesome

    • @djp3
      @djp3  4 года назад

      You too!!

  • @glassfabrikat
    @glassfabrikat 4 года назад +1

    Nice! Thank you!

    • @djp3
      @djp3  3 года назад

      No problem

  • @HuyNguyen-sn6kh
    @HuyNguyen-sn6kh 3 года назад

    you're a legend!

  • @jiezhang3689
    @jiezhang3689 2 года назад +1

    ξ is pronounced as "ksaai"

    • @djp3
      @djp3  2 года назад

      Yes. I pretty much botched that.

  • @m_amirulhadi
    @m_amirulhadi 3 года назад +2

    are u Deadpool?

  • @ozlemelih
    @ozlemelih 8 месяцев назад

    Who's she?

    • @djp3
      @djp3  4 месяца назад

      ?

  • @fjumi3652
    @fjumi3652 2 года назад +1

    the ending :D :D :D

  • @kuysvintv8902
    @kuysvintv8902 2 года назад +2

    I thought it's ryan reynolds

  • @TheCaptainAtom
    @TheCaptainAtom Год назад +1

    great video. pronounced 'ksi'.

    • @djp3
      @djp3  4 месяца назад

      Yes. I totally blew that.