Turing out to be just amazing set of lectures .. exactly touching upon the most painful (to understand) points in "probabilistic machine learning" .. awesome !

Hi professor, once Neural Autoregressive model(NADE) is trained or for that matter any model like wavenet or pixel CNN, How do i compute the probability of a new image belonging to learnt distribution?

Thanks for sharing such good materials! Does RNN belong to autoregressive models?

Why is pixel x3 dependent on x1, x2, but not x4..? Okay I get it it is using Chain rule in Probability to decompose the joint probability distribution.. Not saying x3 depends only on x1 and x2, but learni learning that marginal distribution as a way to predict joint distribution.

Prof could you please upload the slides since your github slides arent updated?

@malay.shukla

Ай бұрын

Bro these slides are exactly same as stanford cs236 slides. You can easily download them. He is teaching the same material.