Latent Dirichlet Allocation (LDA) with Gibbs Sampling Explained

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In this video I explain LDA and go through a tutorial paper on how it works using collapsed gibbs sampling. The next video will be a implementation video :)

This method is an old school method and it's not clear how useful it is today, but a lot of course teach this topic so it must have some historical value I feel. It's one of Andrew Ng's most cited papers!

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Timestamps:
0:00 - Introduction
1:18 - What is topic modelling?
2:41 - LDA
13:30 - Posterior Inference
15:09 - Gibbs Sampling
18:06 - Collapsed Gibbs
21:50 - Finding conditional probability
26:48 - Implementation
30:08 - Pseudo code
32:25 - Ending

Aladdin Persson

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I really like the recent uploads. Bra jobbat mannen :D
Reading another comment, I think it's useful to not just jump into SOTA methods because a lot simpler things tend to be the backbones for many of the SOTA methods.
I find it extremely useful when you make videos like this one, because it just helps broaden my knowledge on new topics.
Looking forward to more of these! But really just keep doing what you find interesting because I'm sure I and others will be interested as well :)

stanislawcronberg

Oh my goodness, you have completely alleviated my stress for the past few weeks. It was extremely difficult for me to understand the generative process. I'm currently working on my final degree assignment. This video is hugely helpful! I'm wondering if I can ask you more questions through LinkedIn or another social media platform.

ahmadulfijihaddzulqornain

Thanks for the video! Just curious as to what app you were using as the pdf reader in the video.

ishanmistry

Which tool are you using to show and edit pdfs ?

hissaanali

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