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Conference on the Mathematical Theory of Deep Neural Networks


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The Conference on the Mathematical Theory of Deep Neural Networks focuses on the recent advances in deep neural networks (DNNs), combined with open, easily-accessible implementations, which have made DNNs a powerful, versatile method used widely in both machine learning and neuroscience. These advances in practical results, however, have far outpaced a formal understanding of these networks and their training. The purpose of this conference is to give visibility to these results, and those that will follow in their wake, to shed light on the properties of large, adaptive, distributed learning architectures, and to revolutionize our understanding of these systems.


Timings

09:00 AM-06:00 PM (expected)
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Participants

upto 100
Delegates
Estimated Count

Category & Type

Conference
Education & Training
IT & Technology

Editions

31 Oct - 01 Nov 2019


Frequency Not Available

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Princeton University USA

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