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The Algorithmic Differentiation (AD) world basically splits in two. Source transformation can give incredibly efficient adjoints, but is restricted to "simple languages" like subsets of C. On the other hand, operator overloading has successfully handled large production codes, but in general is less efficient than source transformation. But there's nothing stopping us combining these two ideas. It turns out that this is somewhat tricky to do, but by no means impossible.
In this webinar, Senior Technical Consultant, Viktor Mosenkis talks about this hybrid approach to Adjoint AD. This approach provides source transformation like performance whilst being as easy to apply as operator overloading.
This webinar will provide:
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This webinar hosted by Oracle Java is designed to empower developers with the skills to integrate artificial intelligence seamlessly into Java applications.
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Join Macrium to get a clear blueprint for resilience and see how Macrium helps organizations restore operations quickly and reliably.
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This joint webinar with the Jisc AI and Licensing will give an overview and demonstration of TeacherMatic's assessment and feedback tools
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