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Tuesday, June 20 • 4:00pm - 6:00pm
Deep Learning with Julia

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Over the last few years we have seen Deep Learning rise to prominence not just in academia with state-of-the-art results for well-established tasks, but also in industry to leverage an ever-increasing amount of data becoming available. Due to the computationally heavy nature of Deep Learning approaches, Julia is in a unique position to serve as the language of choice for developing and deploying deep machine learning models.this workshop we will introduce Deep Learning for a general audience -- assuming only high school-level mathematics to gain a practical understanding of the topics covered. We will first introduce the history and theoretical underpinnings of Deep Learning. After this we will proceed to introduce the lay of the land in terms of libraries and frameworks in Julia -- demonstrating to the audience how one can implement state-of-the-art Deep Learning models for various forms of data. After attending the workshop the audience will have an understanding of how they can use Julia for Deep Learning and adapt these approaches to their own data.organisers of the workshop have between them many years of experience of teaching, research, and working with and implementing Deep Learning frameworks in Julia and other programming languages.


Speakers
MI

Mike Innes

Julia Computing, Inc.
I work with Julia Computing on Julia's IDE, Juno, as well as various projects within the machine learning ecosystem.
JM

Jonathan Malmaud

MIT
Ph.D. candidate at MIT studying artificial intelligence
PS

Pontus Stenetorp

University College London
Pontus Stenetorp is a research associate at University College London, that spends most of his research time on Natural Language Processing and Machine Learning -- with a particular focus on Deep Learning. He has been using Julia since 2014 due to a need for rapid prototyping and... Read More →


Tuesday June 20, 2017 4:00pm - 6:00pm
East Pauley Pauley Ballroom, Berkeley, CA