CSCI 381
Deep Learning
Fall 2024
Division III
Q Quantitative/Formal Reasoning
Class Details
This course is an introduction to deep neural networks and how to train them. Beginning with the fundamentals of regression and optimization, the course then surveys a variety of neural network architectures, which may include multilayer feedforward neural networks, convolutional neural networks, recurrent neural networks, and transformer networks. Students will also learn how to use deep learning software such as PyTorch or Tensorflow.
The Class:
Format: lecture
Limit: 24
Expected: 24
Class#: 1056
Grading: no pass/fail option, no fifth course option
Limit: 24
Expected: 24
Class#: 1056
Grading: no pass/fail option, no fifth course option
Requirements/Evaluation:
Evaluation based on assignments, projects, and exams.
Prerequisites:
CSCI 136 and fulfillment of the Discrete Mathematics Proficiency requirement
Enrollment Preferences:
Current or expected Computer Science majors
Distributions:
Divison III
Quantitative/Formal Reasoning
QFR Notes:
The course will consist of programming assignments and problem sets in which quantitative/formal reasoning skills are practiced and evaluated.
Attributes:
COGS Interdepartmental Electives
Class Grid
Updated 7:12 am
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HEADERS
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CLASSESColumn header 2DREQColumn header 3INSTRUCTORSColumn header 4TIMESColumn header 5CLASS#Column header 6ENROLLColumn header 7CONSENT
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CSCI 381 - 01 (F) LEC Deep Learning
CSCI 381 - 01 (F) LEC Deep LearningDivision III Q Quantitative/Formal ReasoningMWF 9:00 am - 9:50 am
Schow Library Classroom 030B1056ClosedInst