Applied Deep Learning with PyTorch

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.

Frequency: The course consists of weekly lectures and assignments, distributed over a total of 9 weeks.

Examination: The overall grades of Fail or Pass will be awarded for the course. Each 2.5 credit module is examined separately. Exams consist of a written exam for theory and a project for practice. The students are also expected to complete practical exercises before starting the project.


Course responsible:

Eren Erdal Aksoy

Email: eren.aksoy [AT] hh [DOT] se