![]() Thanks to Jan Kühnemund for generating the close caption for YouTube with Open Whisper. Lecture 8: Text generation 2: Autoregressive encoder-decoder with RNNs and attention Lecture 7: Text classification 4: Recurrent neural networks Lecture 6: Text classification 3: Learning word embeddings Lecture 5: Text generation 1: Language models and word embeddings Lecture 4: Text classification 2: Deep neural networks Lecture 3: Text classification 1: Log-linear models Lecture 2: Mathematical foundations of deep learning Subscribe the YouTube playlist to get updates on new lectures: Lecture 1: NLP tasks and evaluation If you're interested in the full previous 2022 content, checkout the latest 2022 Git commit. Note: The following content is continuously updated as the summer term progresses. The content is licenced under Creative Commons CC BY-SA 4.0 which means that you can re-use, adapt, modify, or publish it further, provided you keep the license and give proper credits. See the instructions below if you want to compile the slides yourselves. The slides are available as PDF as well as LaTeX source code (we've used Beamer because typesetting mathematics in PowerPoint or similar tools is painful). This course is jointly lectured by Ivan Habernal and Martin Tutek. This repository contains slides for the course "20-00-0947: Deep Learning for Natural Language Processing" (Technical University of Darmstadt, Summer term 2023). Deep Learning for Natural Language Processing - Lectures 2023
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