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Oct 31, 2024
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CSCI 378 - Deep Learning This course is an introduction to deep neural architectures and their training. Beginning with the fundamentals of regression, optimization, and regularization, the course will then survey a variety of architectures and their associated applications. Students will develop projects that implement deep-learning systems to perform various tasks.
Unit(s): 1 Group Distribution Requirement(s): Distribution Group III Prerequisite(s): MATH 201 and CSCI 121 Instructional Method: Lecture-conference Grading Mode: Letter grading (A-F) Group Distribution Learning Outcome(s):
- Use and evaluate quantitative data or modeling, or use logical/mathematical reasoning to evaluate, test or prove statements.
- Given a problem or question, formulate a hypothesis or conjecture, and design an experiment, collect data, or use mathematical reasoning to test or validate it.
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