A highly practical course introducing the key areas of artificial intelligence that deal with deep learning. The focus is on practical exercises and real-life examples.
Content
- an introduction to deep learning,
- operation of deep learning models (Convolutional NN, Recurrent NN, Autoencoders, DQN…),
- differences between models,
- the use of models in real cases (recognition of objects in pictures, text generation, automatic chess playing, prediction…),
- use of libraries (keras tensorflow),
- optimization of models.
Recommended prior knowledge:
participation in the course “Machine learning in Python” or knowledge of the Python programming language (principles of object programming).
Learning objectives
- acquire basic knowledge about the operation of various deep learning models,
- to gain knowledge and practical experience on the use and optimization of various models in practice.
Target group
- anyone interested in the field of machine learning and artificial intelligence.