DESCRIPTION
Learn how to apply and fine-tune a Transformer-based Deep Learning model to Natural Language Processing (NLP) tasks. In this course, you will: — Construct a Transformer neural network in PyTorch — Build a named-entity recognition (NER) application with BERT — Deploy the NER application with ONNX and TensorRT to a Triton inference server. Upon completion, you’ll be proficient in task-agnostic applications of Transformer-based models.
DETAILS
Course type: programming short course
Duration: 8 hours
Institution of lecturer: Department of Data Science and Visualization, Faculty of Informatics, University of Debrecen
Notes: Students need to complete an assessment in the form of a Jupyter notebook using the NVIDIA DLI platform. After completing the assessment, students receive a certificate signed by NVIDIA as a proof of successfully completing the workshop.
Course link: https://learn.nvidia.com/courses/course-detail?course_id=course-v1:DLI+C-FX-03+V3
LECTURER
Prof. Dr. Andras Hajdu
Andras Hajdu received an MSc degree in Mathematics from the University of Debrecen, Hungary, in 1996. He obtained his PhD degree in Mathematics and Computer Science in 2003. He worked as a PostDoc researcher for the Artificial Intelligence Information Analysis Laboratory, Dept. of Informatics, Aristotle University of Thessaloniki between 2005-2006. Since 2017 he has been a full professor, since 2011 the head of the Department of Data Science and Visualization, and since 2019 the dean of the Faculty of Informatics, University of Debrecen. He is the leader of the local Data Science and Visualization doctoral program and the founder and director of the Gyorgy Hajos Data Science Students College. He worked as a data scientist for the Hungarian Data Asset Agency between 2019-2022. He serves as an instructor for the Microsoft Learn for Educators program and is a certified NVIDIA instructor and university ambassador. He has co-authored 57 journal papers and 120 conference papers cited more than 3500 times; his H-index is 29. His main interest lies in data science/artificial intelligence, medical image processing, and discrete mathematics.