What will I learn?

You will learn the practical details of deep learning applications with hands-on coding using the fastai library built on top of PyTorch.

The key deep learning applications covered in the MOOC are:

  • Computer vision
  • Natural language processing
  • Tabular data
  • Collaborative filtering

In addition, we will dive deeper into the theory behind the practice, and enable you to deeply understand the algorithms as well as to dare to tweak, hack and change them for better performance and usage.

Format and Timeline

The course is made of 7 meetings. Before each meeting, you are expected to watch the course video and arrive to the meeting with insights and questions. The questions will be answered by other members in the study group. Thus, enabling members to learn and understand better by teaching others. As we all come from different backgrounds, we will enrich each other by expressing and emphasizing the different aspects of deep learning algorithms in practice and theory.

The first premeeting will be held on February 18th at 18:30. The exact dates for the 7 lectures meetings will be agreed on at the first premeeting.

What is expected from me?

You are expected to have at least 1 year experience with programming. Preferably, you should feel comfortable with programming in Python as well as having basic knowledge in Calculus and Linear Algebra. Some machine learning background is advised to make best use of the course, but is not necessary.

Who are we?

We are a group of volunteers who have met each other in Vienna's Meetup scene. We advocate new methods of learning and promote education through learning by doing and having a better understanding of studied material by teaching it. Coming from different disciplines and having differnet ways of seeing things is a way to learn from and enrich each other.

There is no smarter than all of us.

With the help and support of:

Still have questions? Just write us!