Deep Learning for Computer Vision

Our courses are open to all and free of charge for students, pupils, trainees, founders, scientific staff, job seekers, refugees, and pensioners. For employees and self-employed persons, a fee of 200 euros is charged when acquiring a certificate of achievement. Employees and self-employed persons can apply for a scholarship via stipendium@opencampus.sh.

What you get

This course extends the fourth course (Convolutional Neural Networks of the online Coursera Specialization "Deep Learning" provided by deeplearning.ai).

The course is a mix of Coursera content, additional materials prepared for you and your own project. We will start by learning the basics of a Convolutional Neural Network (CNN), the reason behind their diffusion, and having an overview of the classical architectures. Then we discuss about how to train a CNN, their advantages and limitations, and different approaches to this topic. A large part of the course will be devoted to applications for computer vision, we will see examples of object detection, face recognition, image segmentation and neural style transfer.

You will be supported in the creation of a group project during the course. We will have discussion round, peer review checkpoints and some hints for the deploying your project.

This course is part of the opencampus.sh Machine Learning Degree. Participants of the program for the Machine Learning Degree get preferred access to this course. Find more information on the opencampus.sh Machine Learning Degree page. For example, the page for this course is this one and each week has a subpage with the material and useful links.

All needed software and online course content is free. As Germany's first Coursera for Campus partner, opencampus.sh will in particular provide you with full access to the Coursera online learning platform.

What you should bring

This course is the natural follow-up of the the first course Deep Learning from Scratch.

If you successfully finished the first course Deep Learning from Scratch, you have nothing to worry about and your application will be granted (as long as there are available places).

If you did not participate in the first course Deep Learning from Scratch, you are welcome to apply, just note that it will be assumed that you have some experience with neural networks (not with CNN, but at least with general machine learning applications).

During the course you will be expected to work through the assigned online course content and complete home assignments on a weekly basis, plus you will work on a group project (you can decide how to distribute the time yourself). For keeping up with the course we usually recommend to plan 5 to 10 hours each week, depending on your preparation and background.

In order to receive a certificate of achievement ("Leistungszertifikat"/ ECTS) for this course, active participation is expected, no more than two classes may be missed, and you have to conduct a practice project in a team of 2 to 4 persons. At the end of the course the project has to be presented and a well documented project source code has to be submitted.

Before the first meeting there will be a Semester Kick-Off Event on October 21.

In the Kick-Off you will meet your course instructor, can ask questions about the participation in the course, and get helpful information to prepare for the start of the course. The attendance at the Kick-Off is not mandatory but recommended for all participants. During the current Coronavirus outbreak, the Kick-Off as well as the course will be streamed online via Zoom. Consultation hours will be organized to allow also personal meeting and an easier communication.