Thanks a lot :)

Thank you so much :) Feel free to look around. And if you like the content, I would be happy if you share the channel with your colleagues and peers :)

You're welcome! 😊

Thanks 😊 I really appreciate the feedback.

Thank you very much 😊

Welcome on the channel :). Glad, I can help. Rest assured, when I started my Master, I also did not know about the Dirichlet distribution. :D probability theory is just such a huge field and it takes time to comprehend it, hope I can contribute to this understanding. :) Good luck with your studies ☺️

You're welcome, thanks for the kind comment 😊

You're very welcome 🤗

Thanks for the kind comment. I am glad I could help :)

Sehr gerne :) Also thanks a lot for the donation ☺️

You're welcome :) Thanks for the feedback. Glad, you enjoyed it :)

You're welcome 🤗 I'm glad I could help.

You're welcome :) Thank you for the motivation. It's amazing to see that the videos have so much value.

Thanks :)

You're welcome 😊

Hi, thanks a lot for the kind comment and feedback :). I will put your suggestion on my list of possible video ideas, but for now I do not want to go too deep into NLP, it's not my field of expertise and research.

Hey, do you have a particular timestamp you are referring to in the video? Maybe some general thoughts: The Dirichlet Distribution is defined over a D-dimensional vector such that all elements add up to one. This is a necessary property in order for this vector to be a valid input to a Categorical Distribution. The Dirichlet Distribution itself is parameterized by another D-dimensional vector that I called alpha here. This alpha moves around the "probability density on the (D-1) dimensional simplex", meaning whether it is more likely to have high values in some components of the D-dimensional vector or not. A uniform prior on this D-dimensional theta vector would mean that all entries are identical, i.e. each entry is 1/D which is necessary to adhere to the property of all components adding up to one.