Evidence Lower Bound (ELBO) - CLEARLY EXPLAINED!

This tutorial explains what ELBO is and shows its derivation step by step.

#variationalinference
#kldivergence
#bayesianstatistics

[AndreiMargeloiu]

Cristal clear explanation, the world needs more people like you!

[TheProblembaer]

Again, thank you. This is incredible well explained, the small steps and the explanation behind, pure gold.

[genericperson]

Absolutely beautiful. The explanation is so insanely well thought out and clear.

[sonny]

Best explanation ever! I found this video for my understanding of VAE at first, but I recently found that this is also directly related to diffusion models. Thanks for making this video.

[speedbird]

That was great, been going through paper after paper, all I needed was this! Thanks!

[thatipelli]

Thanks, your tutorial cleared my doubts!!

[bevandenizclgn]

Best explanation I have found so far, thank u!

[FredocasS]

Thank you so much for this explanation :) Very clear and well explained. I wish you all the best

[T_rex-teus]

Insane explanation Mr. Sachdeva! Thank you so much - I wish you all the best in life

[danmathewsrobin]

Fantastic tutorial!! Hoping to see more similar content. Thank you

[schrodingerac]

excellent presentation and explanation
Thank you very much sir

[AruneshKumarSinghPro]

This one is masterpiece. Can you please put one video on Hierarchical Variational AutoEncoders when you have time. Looking forward to it.

[vihnupradeep]

Thankyou so much sir ! I'm glad that I found your video 💯

[brookestephenson]

Very clear explanation! Thank you very much!

[ajwadakil]

Great Explanation. Can you tell me which books / articles that I may refer to for further and deeper reading regarding variational inferences, bayesian statistics and concepts related to in depth probability?

[HelloWorlds__JTS]

Great explanations! I do have one correction to suggest: At (6:41) you say D_KL is always non-negative; but this can only be true if q is chosen to bound p from above over enough of their overlap (... for the given example, i.e. reverse-KL).

[easter.bunny.]

Thanks for the lecture sir! I have a question at 4:54, how did you expand that E[log_p_theta(x)] into Thanks!

[sahhaf]

Good explanation. I can follow the algebra easily. The problem is this: what is known and what is not known in this formulation? In other words, @0:26, I think we try to find the posterior. But, do we know the prior? Do we know the likelihood? Or, is it that we do not know them but can sample them?

[Aruuuq]

Amazing tutorial! Keep up the good work.

[chethankr]

This is an awesome explaination. Thank you.