Objectives
After completing the course, the student will be able to
- understand basic and advanced deep neural network architectures and their application to various tasks in natural language processing
- select appropriate language resources and deep learning models and fine-tune state-of-the-art models for a range of tasks involving natural language
- understand and explain the capabilities and limitations of deep learning-based models and concepts such as transfer learning, multi- and cross-lingual models, and large-scale pre-training
- independently implement multi-stage natural language processing systems combining several task-specific models
After completing the course, the student will be able to
- understand basic and advanced deep neural network architectures and their application to various tasks in natural language processing
- select appropriate language resources and deep learning models and fine-tune state-of-the-art models for a range of tasks involving natural language
- understand and explain the capabilities and limitations of deep learning-based models and concepts such as transfer learning, multi- and cross-lingual models, and large-scale pre-training
- independently implement multi-stage natural language processing systems combining several task-specific models
- Opettaja
Filip Ginter