After successful completion of the course, students are able to design, implement, and understand their own neural network models for natural language processing via deep learning, using the PyTorch framework.
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 from April to June (12 weeks, 2 teaching hours per week).
Practicals with solving exercises and working in a team project are from April to June (12 weeks, 2 teaching hours per week):
Exercises have to be solved by each student individually.
ECTS Breakdown:
18h Lectures in the lecture hall18h Exercises in the lecture hall18h Preparation at home18h Solving exercise problems57h project20h Preparation for final oral exam 1h Poster presentation and final oral exam-------------------------------------------------------------------------------150h = 6 ECTS Total workload
Solving exercise problems, working in a team project, and poster presentation and oral exam at the end of the course. Your final grade results from your solutions to the given exercise problems, 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 final registration takes place during the first lecture: Places will be allocated among those present in the order of the initial registration in TISS.
Basic Python knowledge will be an advantage.