Es ist eine neuere Version 2024W dieser LV im Curriculum Master's programme Computational Mathematics 2024W vorhanden.
Workload
Education level
Study areas
Responsible person
Hours per week
Coordinating university
1,5 ECTS
M1 - Master's programme 1. year
(*)Artificial Intelligence
Günter Klambauer
1 hpw
Johannes Kepler University Linz
Detailed information
Original study plan
Master's programme Artificial Intelligence 2019W
Objectives
This course will show practical applications and implementations of the contents of the “Deep Learning and Neural Nets I (3 VL)” class. Students will exercise the theory presented in the accompanying lecture and solve programming assignments. Programming assignments will be done in Python using the PyTorch framework.
Subject
Automatic differentiation
Common deep learning frameworks in python
Implementing feedforward neural networks
Solving problems using deep learning techniques
Applying convolutional neural networks
Transfer learning
Criteria for evaluation
bi-weekly assignments, exam at the end of the semester
Methods
Slide presentations, presentations on blackboard, discussions, and code examples
