Workshop / Overview
The goal of generative machine learning is to learn data distribution in order to generate new data points during the inference. Classical examples of generative algorithms are generating text from the voice and vice versa, applying artistic styles of famous painters to pictures, creating fake faces of people and many more. This workshop will be a hands-on tutorial on deep auto-encoders and variational auto-encoders – one of the most popular unsupervised generative algorithms. We will go through a lot of Illustrative code examples to point out advantages and disadvantages of using either of these models.
Workshop / Outcome
Participants will get strong intuition about various types of auto-encoders and hands-on experience to build and adjust one to their needs.
Workshop / Difficulty
Workshop / Prerequisites
- Basic Python Programming
- Machine Learning knowledge
- Own laptop
Track / Co-organizers
A Conceptual Introduction to Reinforcement Learning
With Kevin Smeyers, Katrien Van Meulder & Bram Vandendriessche09:00-12:30 January 251ABC
Applied Machine Learning with R
With Dirk Wulff, Markus Steiner & Michael Schulte-Mecklenbeck09:00-17:00 January 25Foyer 6