Programming Deep Learning Algorithms

Organisation

Announcements

• First lesson in term SS 25: 19.03.26

Prerequisites for participation

• Successful completion of the CSM Machine Learning Lecture.

Structure, Contents, Documents

In this lab student-groups must implement selected applications of Deep Learning. All applications must be implemented in Python Jupyter-Notebooks. The Python machine learning libraries PyTorch will be applied.

Each of the lab-exercises (applications) will be graded. The final grad of the course is the mean of the exercise grades.

This lecture is closely related to and usually combined with the lecture Programming Reinforcement Learning Algorithms.

The first half of the term is covered by the lecture Programming Deep Learning Algorithms, i.e. this lecture stretches over the first 8 thursdays of the term, each from 8:15h to 11:30h.

The second half of the term is covered by the lecture Programming Reinforcement Learning Algorithms, i.e. this lecture stretches over the last 6 thursdays of the term, each from 8:15h to 11:30h.

Students can either select both lectures or only the first. However, it is not possible to select only the second.

Timeplan

Note: The exercise Graph Neural Networks is somehow artificial located in the Reinforcement Learning lecture. Actually Graph Neural Networks do not belong to RL.