Artificial Intelligence has become ubiquitous in our society, with applications in search, image understanding, apps, mapping, medicine, drones, and self-driving cars. Recent developments in neural network (aka “deep learning”) approaches have greatly advanced the performance of these state-of-the-art AI resp. machine learning systems. This lecture is a deep dive into details of the deep learning architectures with a focus on learning end-to-end models, particularly for image classification. Your will also get in touch with how to implement, train and debug your own neural networks and gain a detailed understanding of main approaches within the research on deep learning.