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Latent Dirichlet Allocation (Part 1 of 2)

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Thanks a lot, awesome videos and explanation, god bless you Comment from : @salok1508

Thanks Very useful! Comment from : @sorushii

Thank you for the great video! Question: am i correct in assuming that the order of the words in the documents do not count in the calculations? For example the document "this is a great video" is for this method the same as "great, is, video, this, a"? Comment from : @websciencenl7994

Best video on the topic I've seen so far! You really are helping me in my journey Thank you very much!! Keep going Comment from : @marcserraortega8772

Thank you for this helpful video I tried understanding the original paper, but this is much more understandable Comment from : @asdfmoviesssssssssss

Please create a video on unigram topic modeling, BERT, and c_v coherence Comment from : @MursaleenFayyaz-c5b

Very clear explanation with examples Thanks a lot Comment from : @MursaleenFayyaz-c5b

ur the goat Comment from : @femboymadara

Harris Eric Jackson Frank Jackson Gary Comment from : @AnthonyMoon-i4w

Reilly Rapid Comment from : @MerleMoskos-j2o

Lewis John Hall Barbara Anderson Joseph Comment from : @YeatesBoyd

Thanks Comment from : @iamr0b0tx

Fire Comment from : @bhuvandwarasila

I want to use LDA for my thematic analysis, but the process to use LDA in python is very complex i tried to learn, but cant do so, do you suggest using GUI-TOOL LDA? Comment from : @Freetradingsignals4every1

Luis Serrano, sos un capo Lo explicas genial, muy buenas imágenes brYou are the best, marvelously explained with such beautiful images which help us grasp the concepts behind topic modelling <33 Comment from : @bidaneleon1106

super clear and extremely helpful! Comment from : @sarinstein

Beautiful 👍👍👍 Comment from : @DrNikolausRudak

This is genius explanation, you prob just saved my master's degree Thank you!!! Comment from : @bellahuang8522

On YT, there are tons of videos for LDA This one is the BEST out of all of them Comment from : @anakinskywalk1891

Dr Serrano, you just have put me over the moon Thank you for your elegant explanation of this complex generative model topic Comment from : @raghavamorusupalli7557

This is SUCH a good explanation Thank you!!! Comment from : @ahmaquindi

You are a really good teacher! I really like your illustration and graphs! Thank you very much! Comment from : @ChuyueTang

very good Comment from : @rishavdhariwal4782

amazingly good Comment from : @agr77

Great video, very useful!! Comment from : @MatteoLoRubbio

I loved the way you gave the intuition gently by using the analogies Great teaching and explanation of the topic Comment from : @ardaicen2664

Thank you so much! This is so well explained Comment from : @bellathempress

Nice presentation sir Have you upload the second video on LDA? Comment from : @francisattah-e9m

this guy is god of interpretation Comment from : @sejaljadhav7232

this is simply beautiful Comment from : @asamoahboakye5159

I dont want an invite to your party! Comment from : @robinj5

Really impressive Thanks a lot Will there be more topics? Comment from : @RowanBlooming

Thank you so much, it's extremely helpful I'm moving right away to the next video You're a hero Comment from : @MLA263

Really nice explanation! Comment from : @renaspersonal9854

absolute gem Can't find any clearer and intuitive explanation of LDA than this Comment from : @khengkok

Genuinely incredible video Comment from : @usagibutt

can you please mention the reference for the image shown at 23:23 Comment from : @bharathwajan6079

Brilliant ! really a teaching Legend !! Comment from : @HazemAzim

Damn all your videos are treasures, how am I just discovering you Comment from : @NeotenicApe

Thank you for your tutorial I have a question between minutes 22:47 - 23:18 Is there a way for the machine to give the right topics instead of requiring human intervention? Comment from : @janedorris2424

Thank you very much for this video I have watched many similar videos on youtube about the working of LDA Your explanation is by far - I say by a long way - the most comprehensive and easy to follow You are a great teacher I have told my students and friends about your fantastic video Comment from : @jays9591

Thank you for giving us a so vivid, intuitive illustration for LDA It is really helpful for me as a newby who just encountered this topic recently Comment from : @simonamusicaux

Thanks! Comment from : @simonamusicaux

such a great video ! thank you very much sir ! god bless Comment from : @VauRDeC

If the goal of the LDA is to tease out the hidden topics to begin with, how do you know they are science, politics and sports to begin with? Do you make an educated guess about the topics that the documents are about in the beginning to make the triangle? Or is (science, sport, politics) just a random(but educated) trial that is just one of the many trials that the LDA is going to sample before outputting the document with the highest probability of matching ? Comment from : @sangwookim5551

I really loved this - Comment from : @asmaaziz2436

I started watching this just for fun, but it's been a while since I last seen something so nicely explained Congratulations, top quality stuff! Comment from : @gianpierocea

Best video on Dirichlet by a long shot Thanks! Comment from : @jonathanzevi2425

great explanation Comment from : @deepakwalia9878

La latent dirichlet allocation parece cómo algunos jugadores, que gambetean y gambetean y cuando llegan al arco, la tiran afuera Linda herramienta pero a la hora de crear los documentos, la forma en que lo hace está mal usada Comment from : @FabulusIdiomas

Beautiful explaination to an extremely beginner unfriendly model Thank you! Comment from : @anondoggo

Could you please provide slides used by you Comment from : @burhanuddinmoizali3955

Really intuitive! Thank you! Comment from : @abyssus9934

The best description I've ever found on LDA!brThank you very much The visualization helped a lot Comment from : @saharshayegan

Very good and brief explanation, Thank you Comment from : @aramun7614

Your explanation is an outlier compared with other Youtubers for LDA, because it's tooooo goood Comment from : @andrewzhou1870

This is really good! Great Explanation bro These videos might take time to prepare, It will stand for a long time to come! Comment from : @skviknesh

thnak you so muchbeen reading for months and you sorted out my queries in 26 minuteswish you well and keep up the great work you've done! Comment from : @rush19772112

Luis que pasada de video, increíble Me ha encantado con la facilidad que has explicado este tema que para mí era imposible de planteármelo, estoy deseando seguir con lo que haces es oro Comment from : @franciscojavierestrellarod4721

Awsome video! thank you Comment from : @lavi1172

Brilliant video Thanks for your effort! You've made it seem trivial and it's not Very well explained Comment from : @caetanocardeliquio7174

Extremely useful! Thank u very much for the animation that was an excellent high level explanation, definitevely you are great teacher!! Congrats :) greetings from Ecuador :D Comment from : @eunicegalvez3353

Absolutely fantastic presentation! Thank you so much! Comment from : @matakos22

lots of intuition which is greatly appreciated I spotted the same presenter as a couple of years ago during my ML training with udacity: cool Comment from : @josephpareti9156

Great job! Comment from : @TheMeltone1

This is one of the best and concise explainations of this topic that I have seen Thanks! Comment from : @danielchacreton2401

Great class, very didadict! Congratulations Comment from : @cleitonmoya

Great explanation! Thank you! Comment from : @haoma7151

Very well explained Comment from : @revanthsrirangaraju8863

Im surprised to see that there is no explanation on why a particular word is assumed to be part of a topic/cluster brFor eg: how does LDA decide if "tennis" is closer to "football" than "burger"? Comment from : @slkslk7841

thank you this was helpful Comment from : @ziyadob111e

Good explanation! But the multimodal probability is a conditional probability conditioned on the prior probability Multimodal probability is not very well explained You just copy the prior probability as multimodal probability, which is questionable Comment from : @DigitalAlligator

One word with probability 1: Fantastic Comment from : @alfhes8628

Very clear! Comment from : @frankzhang6009

This was amazing Can you do one of the videos on Hierarchical LDA ? Comment from : @yashkapadia1350

Hands down the best video on LDA! Great job, Serrano! Comment from : @asifhaiderelhan

Fucking Amazing Comment from : @yagzdereboy1600

Great video I have one question though, why don't we do it in reverse? Find the most common words in a document and from there find the topic probability? Or we should do it this way so it's fit for applying machine learning? Comment from :

Thank you so much sir for making this video ! I really developed interest to learn NLP after watching your video Please make more videos on NLP Comment from : @vigneshlakshminarayanan3914

Great Explanation Comment from : @zareshahi

Great review Very very pleased How do we evaluate topic model Can you also give q detailed information about topic model evaluation Comment from : @michealajinaja7142

This video is pure art Comment from :

You are a god among men, thankyou Comment from : @zoasis7805

I must congratulate you Mr Serrano, because you prepared a decent explanation Comment from : @mehmetalinebioglu1851

Superb explanation masterlove it Comment from : @sukumarpalanisamy3180

Amazing and very intuitive explanation, thanks for the video! Comment from : @jmrludan

Thank you for this video I was able to understand properly with this visualization Comment from : @charujames6021

Thank you Comment from : @羅雅蓉

Lovely Example Great Explanation Comment from : @srinivasachary7392

Man, you are the best Comment from : @american-professor

Looks very similar to a backpropagation learning method, with all its weights tuning and loss function Comment from : @8eck

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