Deep Learning

Catalog description

Theoretical examination of neural networks at the structural level and topological impacts of hyperparameters on deep learning models. An in-depth review of the methods and techniques of various deep learning models as applied to common and novel data sets. Topics include model selection for maximizing performance, theories for generalizing, bias reduction, ethical challenges, and future directions of the field.