Applied Deep Learning with PyTorch

Education facts

Code: DT8058

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. <br> The main content of the course concerns techniques for analysis, design, and programming of deep learning algorithms.<br> 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.

Entry requirements

Degree of Bachelor of Science with a major in Computer Science and Engineering or Degree of Bachelor of Science in Engineering, Computer Science and Engineering. The degree must be equivalent to a Swedish kandidatexamen or Swedish högskoleingenjörsexamen and must have been awarded from an internationally recognised university. Including 7.5 credits programming and 7.5 credits mathematics. English 6 or English level 2. Exemption of the requirement in Swedish is granted for those with foreign grades.

Education offerings

Each offering has its own dates and conditions. Closed offerings are retained as history and do not mean that a new application is open.

• Applied Deep Learning with PyTorch

  Halmstad University

  Ortsoberoende

  Start date: 31 August 2026

  End date: 1 November 2026

  Pace of study: 33 %