University of Cambridge > > Churchill CompSci Talks > Deep Learning for Vision: Lecture and Workshop

Deep Learning for Vision: Lecture and Workshop

Add to your list(s) Download to your calendar using vCal

If you have a question about this talk, please contact Matthew Ireland.

In this interactive workshop, we will collaboratively build a robust object segmentation AI, trained a common item, and later identify its flaws and limitations. Participants will be given access to an interactive dataset platform where they can view, label, and capture additional image data to train a neural network. We will train a model during the lecture, and run it in real-time to identify its strengths and weaknesses and how this may affect real-world applications.

Deep learning allows computer vision systems to skyrocket form a number of hand-crafted heuristics in traditional vision engineering, to millions of automatically learned parameters, allowing it to learn almost anything. Backed by significant hype, it seems to break through all the challenges presented by AI, but where does it still fail, and why?

Topics covered include: A brief history of computer vision, what data can and cannot be learned, strengths and limitations of supervised learning, training a Mask-RCNN model via Pytorch, monitoring an AI’s performance.

Free pizza and beer available after the talk!

This talk is part of the Churchill CompSci Talks series.

Tell a friend about this talk:

This talk is included in these lists:

Note that ex-directory lists are not shown.


© 2006-2024, University of Cambridge. Contact Us | Help and Documentation | Privacy and Publicity