[ 993MLPEDN1U19 ] UE Deep Learning and Neural Nets I

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.
  • 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
Language English
Changing subject? No
On-site course
Maximum number of participants 35
Assignment procedure Direct assignment