TIES4700 Deep Learning (5 cr)

This course takes the student into the world of deep learning, focusing on the most common deep learning methods, such as various neural networks (CNN, RNN, Transformer, GAN). The course deepens students' understanding of the mathematical models of deep learning, optimization algorithms, computational requirements, and hyperparameter optimization. During the course, the textbook 'Dive into Deep Learning' (https://d2l.ai/) is used, which provides students with a thorough understanding of the key concepts and techniques of deep learning. The course combines theory and practice, giving students the ability to apply deep learning to complex data-driven problems.

After completing the course, the student is familiar with the most common deep learning methods (CNN, RNN, Transformer, GAN) and understands the mathematical models related to deep learning, optimization algorithms, computational requirements, and hyperparameter optimization. The student can assess when a model can be expected to generalize to unseen data and can ensure this in practice. The student is able to apply the Pytorch library (or another suitable one) and they can choose an appropriate method for different problems and train it effectively.

Dive into Deep Learning: https://d2l.ai/