The NLPwDL course provides in depth knowledge on Deep Learning methods in Natural Language Processing (NLP). It describes methods and architectures such as RNN, LSTM, Attentions, and Transformers, and studies them in NLP applications such as language modeling, document classification, machine translation, and abstractive/extractive summarization. Topics such as interpretability and energy consumption of NLP models as well as mitigating societal biases in NLP systems are discussed in the course.
Covered topics:
Information for the current semester (if available):
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Prerequisites:
Teaching materials of the latest course (Summer Semester 2022):