NLP and Beyond

Course Description

In the present course, the student will learn how to build machine learning models for language processing (NLP), understanding, and generation. A large part of the course is based on the recent advances in transformer language models and their generalization abilities beyond NLP. After completion of the present course, the student will be able to comfortably work with the state-of-the-art approaches for various tasks related to NLP.

Course tools

  • Python, PyTorch, HuggingFace


  • Python, PyTorch
Level of complexity of course Intermediate


Dr Aleksander Kovalenko Czech Technical University in Prague PhD. in Physics, Czech Technical University in Prague, Faculty of Nuclear Sciences and Physical Engineering. Postdoctoral Fellow: Brno University of Technology, Czech Republic; Institute of Advanced Materials, Spain; The NOVA School of Science and Technology, Portugal; Johannes Kepler University, Austria. Assistant Professor: Czech Technical University in Prague, Faculty of Information Technologies. Fields of interests: resource-efficient machine learning; applied machine learning; biology-inspired machine learning Contacts:    

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