Deep Learning Day - Computer Vision


This hands-on academy takes you through every step of building your own, world-class image classifier, from idea to production.


This is a 1 day training with 2-4 hours of preparation.


Extremely practical introduction to computer vision (and AI in general) by building your own deep learning image classifier and deploying to a web app.

For example, the image below shows you an example of an AI model that recognizes whether or not someone is wearing glasses. But you can effectively choose what model you will build during the day. Example computer vision web app

Top down

Our approach is top down and hands-on. After a quick introdution and round table, we start building our first model and gradually explain more and more concepts, when you need them to make your deep learning model better.

99% boilerplate

We limit the slides to an absolute minimum and get our hands dirty while learning. The code is 99% written out for you (and fully documented), so you can focus on experimenting and learning, instead of copy-pasting.


There will be time to practice and ask questions. At the end of the day, we will show you the broader picture and prepare you to take the next step.


The target audience is people who want to get a hands-on experience building a world-class computer vision system, and learning all the details along the way. We initially focussed on the technically savvy, but so far, every participant has been able to complete and learn from the course.


Given the hands on nature of the academy, we expect you to come prepared so we can dive straight in. Prior to the academy, you will receive links to an article and some notebooks you will have to study/run through. Expect to spend between 2 and 4 hours on preparation.

Upcoming sessions

October 13 @ The Campus

November 13 @ Agoria Gent

November 19 @ Agoria Brussel

December 2 @ Voka Limburg

Host your own?

Would you like to host this academy for your audience, or internally at your company?

We are looking forward to hear from you: