Applied Deep Learning with PyTorch

The courses is for professionals and part of the programme MAISTR (hh.se/maistr) where participants can study the entire programme or individual courses. The course is part of the course track machine learning and is held online in English.

The main content of the course concerns techniques for analysis, design, and programming of deep learning algorithms.
The course is broken down into two modules of 2.5 credits: theory and practice. The theoretical content covers basic principles of multi-layer perceptrons, spatio-temporal feature extraction with convolutional neural networks (CNNs) and recurrent neural networks (RNNs), classification and regression of big data, and producing novel data samples using generative models. The practical sessions cover the basics of programming with PyTorch, image classification, and semantic segmentation using CNNs, future image frame prediction with RNNs and image generation with generative adversarial networks.