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Attention mechanisms have been around since at least 2014, and they have been driving the state of the art in NLP (natural language processing) ever since. Now we are at the end of 2018, and yet it is still very uncommon to hear about their use in enterprise environments.
This talk will explain attention mechanisms and how they fit into the deep learning landscape. It will show you the different types of attention mechanisms and examples of them being applied to several different problems. Finally, you will see how you might apply them yourself in models build with Deeplearning4J.
Speaker: Paul Dubs (Paul Dubs IT Consulting)
Paul holds a Master of Science in Computer Science from TU Darmstadt and has over a decade of professional software engineering experience. He is an open source contributor to the Deeplearning4J project, and if you’ve ever asked a question on the Deeplearning4J Gitter, there is a good chance that he may have answered your question.
Currently Paul is the Head of Research & Development at ICTJob Deutschland GmbH, where he uses machine learning to improve the hiring experience for both, candidates looking to find a job in the IT industry as well as for the companies that are looking to hire qualified candidates.
Simultaneously he is working as an independent consultant, who helps companies evaluate and implement machine learning for their business.