This lecture will provide a general introduction to modern deep learning methods with a particular emphasis on practical applicability. At the same time, the course will provide an introduction to the popular PyTorch Deep Learning framework while requiring only basic programming skills in Python. The course will cover a range of common machine learning tasks across different data modalities ranging from tabular data over Computer Vision (image classification, image segmentation) to time series and natural language processing. It will cover the most important model architectures in these domains ranging from convolutional neural networks over recurrent neural networks to transformers. The lecture will be accompanied by a tutorial class where students are supposed to acquire hands-on experience in working with PyTorch and are supposed to acquire the skills to apply Deep Learning methods in their respective fields of study.