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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!
Paper tutorial: https://coli-saar.github.io/cl19/materials/darling-lda.pdf
Paid Courses I recommend for learning (affiliate links, no extra cost for you):
⭐ Machine Learning Specialization https://bit.ly/3hjTBBt
⭐ Deep Learning Specialization https://bit.ly/3YcUkoI
📘 MLOps Specialization http://bit.ly/3wibaWy
📘 GAN Specialization https://bit.ly/3FmnZDl
📘 NLP Specialization http://bit.ly/3GXoQuP
✨ Free Resources that are great:
NLP: https://web.stanford.edu/class/cs224n/
CV: http://cs231n.stanford.edu/
Deployment: https://fullstackdeeplearning.com/
FastAI: https://www.fast.ai/
💻 My Deep Learning Setup and Recording Setup:
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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