Courses :: Module, DEEP LEARNING FOR COMPUTER VISION

	Overview
	The course is a deep dive into the details of deep learning architectures with a focus on learning end-to-end models for computer vision. Students will implement neural networks and gain a detailed understanding of deep learning for computer vision. Through multiple hands-on assignments and the final course project, students will acquire the toolset for setting up deep learning tasks and practical engineering tricks for training and fine-tuning deep neural networks. Additionally, students will address the technical challenges of implementing deep networks on Edge AI cameras using the Intel Myriad X edge AI processorusing the OpenVINO toolkit.

Overview

The course is a deep dive into the details of deep learning architectures with a focus on learning end-to-end models for computer vision.
Students will implement neural networks and gain a detailed understanding of deep learning for computer vision.

Through multiple hands-on assignments and the final course project, students will acquire the toolset for setting up deep learning tasks and practical engineering tricks for training and fine-tuning deep neural networks.

Additionally, students will address the technical challenges of implementing deep networks on Edge AI cameras using the Intel Myriad X edge AI processorusing the OpenVINO toolkit.

	Learning Outcomes
	On successful completion of the module, students should be able to: Select the appropriate deep leaning architecture for a image processing task. Implement and evaluate a the deep learning architecture for a computer vision tasks. Demonstrate an understanding of the use of engineering tools to build and fine tune deep learning systems. Demonstrate the application of deep learning on an Edge AI device.

Learning Outcomes

	Autumn Supplementals/Resits
	Are supplemental/resit registrations permitted? No

Autumn Supplementals/Resits

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Courses / Module

DEEP LEARNING FOR COMPUTER VISION

Semester 1

Semester 2