Written exam

Within the last decade, deep neural networks have emerged as an indispensable tool in many areas of artificial intelligence including computer vision, computer graphics, natural language processing, speech recognition and robotics. This course will introduce the (practical and theoretical) principles of deep neural
networks and give an overview over the most established training and regularization techniques. The lecture will further discuss the most important network variants, including convolutional neural networks, generative neural networks, recurrent neural networks and deep reinforcement learning. Furthermore, the course will give an overview over the most important architectures hourglass
networks, skip connections, dense connections, dilated convolutions, permutation invariant networks, siamese networks, etc.). In addition, applications from various fields will be presented throughout the course. The tutorials will deepen
the understanding of deep neural networks by implementing, training and applying them using modern deep learning frameworks.

Course Website: https://uni-tuebingen.de/de/175884

Students gain an understanding of the theoretical and practical concepts of deep neural networks including optimization, inference, architectures and applications. After this course, students should be able to develop and train deep neural networks, reproduce research results and conduct original research in this area.

Related literature will be listed throughout the lecture.