<p>Today's clip is from<a rel="noopener noreferrer nofollow" href="https://learnbayesstats.com/episode/151-diffusion-models-in-python-a-live-demo-with-jonas-arruda" target="_blank"> Episode 151</a> of the podcast, with Jonas Arruda<br /><br />In this conversation, Jonas Arruda explains how diffusion models generate data by learning to reverse a noise process. The idea is to start from a simple distribution like Gaussian noise and gradually remove noise until the target distribution emerges. This is done through a forward process that adds noise to clean parameters and a backward process that learns how to undo that corruption. A noise schedule controls how much noise is added or removed at each step, guiding the transformation from pure randomness back to meaningful structure.<br /><br />Get the full discussion <a rel="noopener noreferrer nofollow" href="https://learnbayesstats.com/episode/151-diffusion-models-in-python-a-live-demo-with-jonas-arruda" target="_blank">here</a><br /><br />• Join this channel to get access to perks:<br /><a rel="noopener noreferrer nofollow" href="https://www.patreon.com/c/learnbayesstats" target="_blank"><b>https://www.patreon.com/c/learnbayesstats</b></a><br /><br />• Intro to Bayes Course (first 2 lessons free): <a rel="noopener noreferrer nofollow" href="https://topmate.io/alex_andorra/503302" target="_blank">https://topmate.io/alex_andorra/503302</a><br />• Advanced Regression Course (first 2 lessons free): <a rel="noopener noreferrer nofollow" href="https://topmate.io/alex_andorra/1011122" target="_blank">https://topmate.io/alex_andorra/1011122</a><br /><br />Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at <a rel="noopener noreferrer nofollow" href="https://bababrinkman.com/" target="_blank">https://bababrinkman.com/</a> ! </p>