Deep Learning for Computer Vision: Fundamentals and Applications

	Weizmann Institute of Science Deep Learning for Computer Vision: Fundamentals and Applications
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Course Overview

This course covers the fundamentals of deep-learning based methodologies in area of computer vision. Topics include: core deep learning algorithms (e.g., convolutional neural networks, transformers, optimization, back-propagation), and recent advances in deep learning for various visual tasks. The course provides hands-on experience with deep learning for computer vision: implementing deep neural networks and their components from scratch, tackling real world tasks in computer vision by desigining, training, and debugging deep neural networks using leading mainly PyTorch.

Announcements

We encourage students to take "Introduction to Computer Vision" and "Basic Topics I" in conjuction with this course.
Make sure you have completed all the prerequisites.