Deep Learning für Natural Language Processing

All you need to know about contemporary natural langauge processing (NLP) using deep learning. More about foundations, less about particular frameworks or implementations. Content: - Deep learning foundations (learning from data, learning problem formalization, loss functions, training with backpropagation, evaluation) - NLP as supervised task learning - Language representation (word embeddings, multi-lingual embeddings) - Prominent architectures (convoluational neural networks, recurrent neural networks) - Contemporary architectures and foundational models (transformers and BERT) - Applications (text classification, text generation, translation)