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Showcase
Artificial Intelligence is about more than just discovering ways to make use of machine learning components, tensor computations, system software capabilities, and training algorithms. It's about being receptive and adaptive to new information as it becomes available. It's about synthesizing for understanding and how everything fits together.
As an open source library, Nervana Graph opens a broad door that lets just about anyone in to write code or develop a framework that can be enabled to iterate on computational efficiencies. Deep learning models can be written and nudged this way or that, to become more powerful or more revealing with respect to patterns or trends in the real-time real world. However, it's understandably easy to get lost in such a broad "library" of possibility.
Our Showcase Wiki page here is intended to be something of a community-curated help desk. Here we welcome contributions from the community: snippets and specifics, tutorials, walk-throughs, or a "recipe" you've created using framework X on backend Y. Feel free to add a link (or two or ten) to this page and share what you've done with us and with the greater deep learning community.
Examples of things you might want to add here:
- A sentence or two with a link to a Jupyter* notebook project
- A brief summary about and link to a tutorial explaining how your custom frontend works with with Intel® Nervana Graph APIs.
- A custom fork of this very repo, around which you've created an entire set of documentation and examples.
This feature page is open and experimental; please don't be shy. As always, and with everything in the deep learning world: it's perfectly okay to share a "work in progress." We're open and receptive to all kinds of learning and progress.