Deep Learning: Fundamentals and Applications



B = Basic
M = Intermediate
A = Advanced

Next course


Further and more detailed information, including the schedule, can be found in the current course tables in the syllabus of the respective course, if the course is offered in the next sessions. The following text serves as information on what can be expected in terms of content in the course.

Course content: – Machine Learning Refresh o Supervised Learning vs. Unsupervised Learning o Traditional Machine Learning vs. End-to-End Learning – Fundamentals of Neuronal Networks: o Rosenblatt Perceptron and Neurons o Network Structure (feed-forward, recurrent), matrix notation, forward evaluation – Training as optimization o Loss and Error functions o Backpropagation o SGD and other optimizer – Activation functions and topologies o Convolutional neural networks o Generative Adversarial Networks o Long short-term memory networks o Special layer types (inception, resnet) o Embeddings o Attention Mechanis & Transformer – Applications to real-world problems: o Acoustic keyword recognition (audio/speech processing) o Sentiment analysis (text processing) o Digit recognition (image processing) o Tiny Image Recognition (image processing) o Face Detection and Tracking (image/video processing) o Stock market prediction (time series prediction) – Training on large data sets (Hardware, GPU) – Trustworthy AI Structure: The course is a theoretical content in the morning and practical exercises in the afternoon in form of lab Jupyter notebook programming.