Support the Podcast
Andrew and I discuss his work exploring how various facets of deep networks contribute to their function, i.e. deep network theory. We talk about what he’s learned by studying linear deep networks and asking how depth and initial weights affect learning dynamics, when replay is appropriate (and when it’s not), how semantics develop, and what it all might tell us about deep learning in brains.
Show notes:
A few recommended texts to dive deeper:
Support the show to get full episodes and join the Discord community. Mac and I discuss his systems level approach to understanding brains, and...
Support the show to get full episodes and join the Discord community. Check out my free video series about what's missing in AI and...
Support the show to get full episodes and join the Discord community. Check out my free video series about what's missing in AI and...