Deep Learning for Practical Natural Language Processing
Deep Learning for Practical Natural Language Processing


Natural language processing (NLP) is one of the most transformative technologies for modern businesses and enterprises. We will focus on practical applications and considerations of applying deep learning for NLP in industrial settings. This hands-on session will target algorithms and frameworks which are able to be deployed in products today. We will provide an overview the different components that go into end-to-end deep learning systems, including word vector representations (word2vec, GloVe, fastText, etc.), recurrent neural networks, convolutional neural networks, and attention mechanisms. We will also cover selected tips and tricks for ensuring deep learning products add maximal value in application architectures, as well as provide some guidelines for managing NLP systems in the wild.


Luke de Oliveira enjoys working on solving interesting problems in language understanding using deep learning. Luke is a Co-Founder of Vai Technologies, where he leads a team that builds software to help organizations and enterprises make sense of text and language data. He also holds a research appointment at Lawrence Berkeley National Laboratory, where he works on generative modeling for high energy particle physics. Luke earned his M.S. from Stanford ICME and his B.S. from Yale, and has held positions at Enlitic, SLAC National Accelerator Laboratory, and CERN.

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