Natural Language Processing and Understanding with Transformer Models

For most natural language processing and understanding tasks, such as machine translation and question answering, transformers produce state of the art results.
Although these models are very popular, understanding the theory behind them can be a challenging.
This theoretical complexity therefore drives many developers away in an early stage, who actually might be interested in using these models themselves.
This is rather unfortunate, because there are many open source libraries that can be used “out-of-the-box”.
In this talk we want to de-mystify transformer models and discuss some open source libraries with the help of practical examples.

Target Group: People interested in Natural Language Processing who might want to train their own models (also suitable for beginners although prior deep learning knowledge is helpful to understand the theoretical concepts)