Generative Models are insanely cool! They help create never before seen data. We'll go over the mathematical difference between discriminative and generative models, talk about a few types, then dive into a basic one called Latent Dirichlet Allocation to generate a set of topics for some news articles.

Code for this video:
https://github.com/llSourcell/Latent_Dirichlet_Allocation

Hammad's Winning Code:
https://github.com/hammadshaikhha/Math-of-Machine-Learning-Course-by-Siraj/tree/master/Bayesian%20Linear%20Regression

Noah's Runner up code:
https://github.com/NoahLidell/math-of-intelligence/tree/master/hyperparameter_optimization

Carykh's channel:
https://www.youtube.com/user/carykh

More learning resources:
https://www.youtube.com/watch?v=qCA1Dk_Ih_c&t=383s
http://blog.echen.me/2011/08/22/introduction-to-latent-dirichlet-allocation/
http://ai.stanford.edu/~ang/papers/jair03-lda.pdf
https://rstudio-pubs-static.s3.amazonaws.com/79360_850b2a69980c4488b1db95987a24867a.html
https://www.quora.com/What-is-a-good-explanation-of-Latent-Dirichlet-Allocation
https://blog.bigml.com/2016/11/16/introduction-to-topic-models/
https://www.analyticsvidhya.com/blog/2016/08/beginners-guide-to-topic-modeling-in-python/
https://github.com/blei-lab/onlineldavb

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