Course Description
Neural network models, the most common architectures and their use in different domains; practical application of neural network models and their implementation using Python and Keras; end to end application of deep learning, including learning workflow; parallel hyperparameter search; hyperparameter configuration; mixed architectures combining several models; semi supervised learning; reinforcement learning agents.
Prerequisites
Corequisites
Schedule
| This Course was not Offered During Spring/Summer 2025 Term |
| This Course was not Offered During Fall 2025 Term |
| The tentative timetable is not yet available for the Winter 2026 Term |
| The tentative timetable is not yet available for the Spring/Summer 2026 Term |
