Deep Learning meets (Astro)physics
Date: 22 January 2020
Venue: ETH Zurich, Campus Hönggerberg, HIT K 51
Machine Learning, and in particular Deep Learning, is becoming an increasingly popular tool in science, and especially in (astro)physics. We are therefore excited to announce a one-day hands-on workshop on deep learning for (astro)physics, aimed mainly at scientists at ETH Zurich. Our goal for this tutorial-style workshop is to provide a beginner-friendly way to get started and see what the current machine learning "hype" is about, and to understand if and how machine learning can also help the participants with their own research.
The workshop will feature two sessions. In the morning, we will give a theoretical introduction to the basic concepts of deep learning, such as "What is a neural network?", "How do you train it?" or "What architectures are suitable for which task?". There will also be four short impulse talks, in which our speakers will give concrete examples of how to apply deep learning to specific science use cases. In the afternoon, things will get more hands-on with an interactive session in which we show the participants how to train your own neural networks using the latest version of Google's TensorFlow framework. For both parts, we will be supported by Umberto Michelucci, founder of TOELT LLC, and the first and only Google Developer Expert (GDE) in Switzerland.
More information you get external pagehere.