Hi @rocketheat, have you checked the main examples landing page? There are some tips for orienting yourself in the tutorials there.
Beyond Pyro’s tutorials, a popular community resource for getting started with Bayesian data science is the book “Statistical Rethinking”, for which all code snippets have been ported to Pyro and NumPyro by @fehiepsi and others.
For probabilistic machine learning methods, Chris Bishop’s book Pattern Recognition and Machine Learning and Kevin Murphy’s book Machine Learning: A Probabilistic Perspective are the standard references. For deep learning, Ian Goodfellow and Yoshua Bengio’s textbook Deep Learning and the official PyTorch tutorials are both excellent resources.
As for videos, @fritzo and I recently gave an introductory talk on Pyro and primer on gradient-based variational inference at the Broad Institute of MIT and Harvard, and I gave a 4-part introductory tutorial a couple years ago at a training event as part of UAI 2018.
Does any of that help, or is there some more specific topic you were looking for?