Overview

Deep learning (DL) means using neural networks with many layers to perform machine learning tasks, such as classifying images, detecting objects, predicting future actions, and interacting with the environment. Deep learning did not only revolutionize entire fields like computer vision or robotics, it also enables novel applications, like autonomous driving, in the first place.
Hidden behind the buzzword generative artificial intelligence (GenAI), neural networks are used to generate realistic novel data, including text, images, videos, 3D models, and audio.

This lecture offers an overview on deep learning, reaching from fundamentals to cutting-edge applications.

Content

• Multi-layer perceptrons (MLPs) as basic building blocks
• Training of deep neural networks
• Convolutional neural networks (CNNs) and applications in computer vision
• Recurrent networks (RNNs)
• Graph neural networks (GNNs)
• Transformers and applications in neural language processing (NLP)
• Generative networks (GenAI)
• Reinforcement learning (RL) and applications in control

Next to lectures, we offer hands-on exercises and include a paper presentation.

Requirements

You should have experience with programming in Python or get familiar with its basics before the first exercise.