Show Notes
Today’s clip is from episode 144 of the podcast, with Maurizio Filippone. In this conversation, Alex and Maurizio delve into the intricacies of Gaussian processes and their deep learning counterparts. They explain the foundational concepts of Gaussian processes, the transition to deep Gaussian processes, and the advantages they offer in modeling complex data. The discussion also touches on practical applications, model selection, and the evolving landscape of machine learning, particularly in relation to transfer learning and the integration of deep learning techniques with Gaussian processes. Get the full discussion here. Intro to Bayes Course (first 2 lessons free) Advanced Regression Course (first 2 lessons free)
Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work! Visit our Patreon page to unlock exclusive Bayesian swag ;) Transcript This is an automatic transcript and may therefore contain errors. Please get in touch if you're willing to correct them.
More from Learning Bayesian Statistics
Support & Resources → Support the show on Patreon → Bayesian Modeling Course (first 2 lessons free)
Today's clip is from Episode 166, featuring Bill Engels & Jesse Grabowski. In this conversation, Bill explains what a kernel means in a Gaussian process and how it defines the idea of similarity between data points. He then breaks down the role of the length scale, showing how it controls how quickly the relationship between two points changes, and how amplitude controls the overall scale of the functions a GP can represent.
Support & Resources → Support the show on Patreon → Bayesian Modeling Course (first 2 lessons free)
Today's clip is from Episode 165, featuring Alex Fengler. In this conversation, Alex introduces Bayesify , a tool that uses AI to analyze research papers and assess how well they follow a Bayesian workflow.
Support & Resources → Support the show on Patreon → Bayesian Modeling Course (first 2 lessons free)