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SYDE 670Deployment of Deep Learning Models

Units
0.50
Level
Graduate
Faculty
Engineering
Subject
Systems Design Engineering

SYDE 670 as listed in the Fall 2026 Graduate Calendar. The main page has the current version, when it runs, and seats.

This course focuses on the deployment of computer vision networks for real-world applications. Students will gain practical knowledge on various computer vision tasks such as semantic segmentation, object detection, optical flow, and depth estimation. The course covers all critical aspects from selecting appropriate network architectures, data preparation and handling, to deployment on edge devices. The course also delves into infrastructure setup, training, and model compression techniques to ensure efficient deployment. By the end of the course, practitioners will be equipped with the skills needed to devise, implement, and maintain robust computer vision solutions in diverse real-world scenarios.

Prerequisites

None listed.

Antirequisites

  • Not completed nor concurrently enrolled in:

Leads to

No course lists it as a requisite.

Check it elsewhere for Fall 2026

Calendar versions

Graduate Calendar