Applied Deep Learning with PyTorch

5 credits

The aim of this course is that students will learn about the analysis, design, and programming of deep learning algorithms. The course is part of the programme MAISTR ( where participants can take the entire programme or individual courses. The course is for professionals and is held online in English. Application is open as long as there is a possibility of admission. The courses qualify for credits and are free of charge for participants who are citizens of any EU or EEA country, or Switzerland, or are permanent residents in Sweden. More information can be found at

About the course Applied Deep Learning with PyTorch, 5 credits

Who is this course for?
This course provides the theoretical and practical aspects of deep neural networks. It is intended for students with a background in computer science and engineering.

What will you learn from this course?
Students will learn about the analysis, design, and programming of deep learning algorithms. The course has two modules: theory and practice. The theoretical content covers basic principles of multi-layer perceptions, spatio-temporal feature extraction with convolutional neural networks (CNNs), and recurrent neural networks (RNNs), classification and regression of big data, and generating novel data samples using generative models. The practical sessions cover the basics of programming with PyTorch. For instance, image classification and semantic segmentation using CNNs, future image frame prediction with RNNs, and image generation with generative adversarial networks.

What is the format for this course?
Instruction type: Teaching is in English and fully online. It consists of lectures, computer exercises, and project work. In the computer exercises, the student solves small problems using deep learning models. After programming various exercises, the participants will develop an advanced deep learning project. Participants will be encouraged to bring their own data. High-end GPU machines can be provided for the exercises and project.

Autumn 2022 (Distance (Internet), Varied, 33%)


Advanced level

Application code:


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 or the equivalent of 180 Swedish credit points or 180 ECTS credits at an accredited university. Including 7.5 credits programming and 7.5 credits mathematics. Applicants must have written and verbal command of the English language equivalent to English course 6 in Swedish Upper-Secondary School.

Selection rules:

Credits: 100%

Start week:

week: 35

Number of gatherings:


Instructional time:

Various times

Language of instruction:

Teaching is in English.

Show education info