Deep Learning Lab
People
Description
This course will introduce students to practical implementations of various deep learning models using Python and the PyTorch library. Recommended lectures are: Machine Learning, and basic courses on Linear Algebra, Analysis, Probability & Statistics. Basic knowledge of Python is expected, but it is not a hard requirement as long as the student is capable of learning it quickly. However, additional material will be provided to students that need to learn Python from scratch.
Objectives
Through practical programming exercises, students will deepen their understanding of neural network based models. They will be exposed to various practical considerations that are crucial to make deep learning systems to perform well in practice.
Teaching mode
In presence
Learning methods
About 50 percent of the sessions consist of guided exercises. Different programming exercises will cover various building blocks and applications of deep learning.
Examination information
Students will be evaluated through practical assignments.
Education
- Master of Science in Artificial Intelligence, Lecture, 1st year
- Master of Science in Computational Science, Lecture, Elective, 1st year
- Master of Science in Computational Science, Lecture, Elective, 2nd year
- Master of Science in Informatics, Lecture, Artificial Intelligence, Elective, 1st year
- Master of Science in Informatics, Lecture, Artificial Intelligence, Elective, 2nd year
- PhD programme of the Faculty of Informatics, Lecture, Elective, 1st year
