Computational Experience in a Time of Machine Learning
Description
Drawing on process philosophy, Munster approaches computational experience through its relations and operations. She combines deep learning—the subfield of machine learning that uses neural network architectures—and aesthetics to offer a way to understand the insensible and frequently imperceptible forms of nonlinear and continuously modulating statistical function.
