Nach positiver Absolvierung der Lehrveranstaltung sind Studierende in der Lage unter Verwendung des PyTorch-Frameworks eigene neuronale Netzwerkmodelle für die Verarbeitung natürlicher Sprache zu entwerfen, zu implementieren und zu verstehen.
Welcome to this course on deep learning for natural language processing (NLP)! This course is designed to unravel the complexities of NLP through the lens of deep learning techniques. From fundamental concepts to advanced neural network architectures, we will explore the intricacies of how deep learning models can comprehend, generate, and manipulate human language. Get ready to embark on a transformative learning journey, where theory meets hands-on applications.
The topics covered in the course include the following:
Lecture part with frontal lectures in weeks 1 to 8 (4 hours per week) from mid November to end January.
Practicals are in weeks 1 to 8 (4 hours per week).
Your final grade results from your submitted work plan, the type of extensions made on the selected paper, the submitted poster, the poster presentation, and the final oral exam.
The lectures will be kept as a block of 8 weeks (4 hours per week) from mid November to end January. The course is currently being assigned to the modules "Knowledge Representation and Artificial Intelligence" in the master programme "Logic and Computation" and the module "Algorithmic" in the master programme "Software Engineering & Internet Computing", pending approval.
The course will be extended to 4 VU, pending approval.
Oral examination at the end of the course.
Basic Python knowledge will be an advantage.