This is an hands-on introduction to deep learning and its implementation in Keras, intended for anyone familiar with machine learning.. The use of deep learning has seen a sharp increase of popularity and applicability over the last decade. While deep learning can be a useful tool for researchers from a wide range of domains, taking the first steps in the world of deep learning can be somewhat intimidating. This introduction aims to cover the basics of deep learning in a practical and hands-on manner, so that upon completion, you will be able to train your first neural network and understand what [...]

This is an hands-on introduction to deep learning and its implementation in Keras, intended for anyone familiar with machine learning.. The use of deep learning has seen a sharp increase of popularity and applicability over the last decade. While deep learning can be a useful tool for researchers from a wide range of domains, taking the first steps in the world of deep learning can be somewhat intimidating. This introduction aims to cover the basics of deep learning in a practical and hands-on manner, so that upon completion, you will be able to train your first neural network and understand what [...]

This is an hands-on introduction to deep learning and its implementation in Keras, intended for anyone familiar with machine learning.. The use of deep learning has seen a sharp increase of popularity and applicability over the last decade. While deep learning can be a useful tool for researchers from a wide range of domains, taking the first steps in the world of deep learning can be somewhat intimidating. This introduction aims to cover the basics of deep learning in a practical and hands-on manner, so that upon completion, you will be able to train your first neural network and understand what [...]

Sebastien Reich (Potsdam University): The Mathematics of Nurturing a Digital Twin. Mathematically speaking, a DT can often be described as a partially observed Markov decision process (POMDP). Solving POMDPs computationally constitutes one of the most challenging problems around. Still, tremendous progress has been made in closely related fields such as data assimilation, uncertainty quantification, control and optimisation, and model reduction. A key emerging question is thus how we can successfully synthesise these advances into a tool broadly applicable to DT.