Deep Learning (DL) has enjoyed a surge in popularity during the last decade. This is mainly owed to the fact that DL is computationally quite expensive and needs vast amounts of data to train useful models. Both, computational resources and large datasets have become more readily available in recent years, leading to breakthroughs in DL applications.

This course aims at interested participants from all domains, who have not yet jumped on the bandwagon of the DL trend.

The course will cover the basic principles of fully connected neural networks, convolutional neural networks (CNNs) for computer vision, recurrent neural networks (RNNs) for text or speech recognition, transfer learning to leverage pretrained models, and touch on large language models (LLMs) which have become all the rage in the past year.