Deep Learning in Industry

Name: Deep Learning in Industry
Start: 2018-09-27T15:00:00+02:00
End: 2018-09-27T18:00:00+02:00
Location: Max Planck Institute of Molecular Cell Biology and Genetics

Thursday 27 Sep 2018, 15:00 → 18:00 Europe/Berlin

Big Auditorium (Max Planck Institute of Molecular Cell Biology and Genetics )

Big Auditorium

Max Planck Institute of Molecular Cell Biology and Genetics

Pfotenhauerstr. 108 01307 Dresden

Description

In this event, we are happy to welcome speakers from industry that provide a unique view of using Deep Learning from their field. This is your chance to get in touch with practitioners from the field. Some prior knowledge in machine learning (deep learning at best) is needed.

- 15:00 → 16:00
  
  Neural Machine Translation 1h
  
  Neural Machine Translation: Recent advances and remaining challenges
  Machine translation is the process of automatically translating text from one language into another. Research on designing and improving machine translation algorithms has been active for decades, but recent advances in machine learning with deep neural networks have led to revolutionary improvements in translation quality. Compared to the best technology of only a few years ago, these “neural machine translation” systems produce amazingly fluent and correct translations. In this talk, I will give a broad overview of how these systems work and address some of the remaining challenges and open questions. I will also speak about how machine translation is used at Amazon to help our customers cross language boundaries.
  
  Hagen Fuerstenau is a research manager at Amazon, leading the machine translation team in Berlin. Before joining Amazon, his research focus was on computational semantics, learning representations of the meanings of words and sentences from data. His PhD work at Saarland University and University of Edinburgh was on semi-supervised methods for semantic role labeling, trying to automatically identify events and their participants from text data and some human annotations. During a postdoc stay at Columbia University, he then investigated if such information can also be extracted from text alone in an unsupervised setting. He later became interested in machine translation as a challenging application, since translating well is not possible without capturing the meaning of texts.
  
  Speaker: Mr Hagen Fürstenau (Amazon)
  
  neural_machine_translation.pdf
- 16:00 → 16:30
  
  Coffee and Networking 30m
- 16:30 → 17:30
  
  Machine Learning and Data Science with AWS 1h
  
  Amazon has been investing deeply in artificial intelligence for over 20 years. Machine learning (ML) algorithms drive many of Amazon‘s internal systems and they are also core to the capabilities our customers experience. For Amazon Web Services (AWS) our mission is to share those learnings and ML capabilities as fully managed services, and to put them into the hands of every developer and data scientist. In this session I will share some challenges our customers have been facing and how they have been solved with the help of AWS ML capabilities.
  
  Christian Petters works as Senior Solutions Architect at Amazon Web Services (AWS). As such, he is often found near a white board helping his customers assemble the right building blocks to address their unique business challenges. His current focus is on fully managed services that put ML capabilities into the hands of every developer and data scientist. Prior to AWS, Christian helped organizations with the design, deployment, and operation of scalable web, messaging, and collaboration solutions.
  
  Speaker: Mr Christian Petters (Amazon)
  
  2018-09-27-DL_Dresden-Machine_Learning_and_Data_Science_with_AWS.pdf

Deep Learning in Industry

Big Auditorium

Max Planck Institute of Molecular Cell Biology and Genetics

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