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@@ -6,13 +6,28 @@ Each repository in this catalog contains implementations of specific machine lea
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-**Description**: Brief introduction to the repository's purpose and links to relevant research papers
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-**Algorithms**: List of ML algorithms demonstrated in the repository
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-**Datasets**: Information on datasets used, with links to publicly available data
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-**Type**: The category of implementation:
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***bootcamp**: Educational implementations developed for workshops and learning purposes
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***tool**: Utility libraries and tools for broader use
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***applied-research**: Research code tied to specific projects or papers
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-**Year**: The year the implementation was published
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## Usage Notes
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!!! note
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- Many repositories contain code for reference purposes only. To run them, updates may be required to the code and environment files.
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- Links for only publicly available datasets are provided. Many datasets used in the repositories are only available on the Vector cluster.
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## Repository Organization
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The catalog is organized by implementation type to help you quickly find the resources you need:
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***Bootcamp implementations**: Educational resources designed for workshops and learning purposes
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***Tool implementations**: Utility libraries and general-purpose tools
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***Applied Research implementations**: Code tied to specific research projects or papers
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Each implementation includes algorithm tags, dataset information, and other metadata to aid in discovery.
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## Contributing
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If you are a Vector researcher or engineer and would like to add your implementation to this catalog, you can contribute by following our [contribution guidelines](https://github.com/VectorInstitute/reference-implementation-catalog/blob/main/CONTRIBUTING.md).
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*[Request a feature](https://github.com/VectorInstitute/reference-implementation-catalog/issues/new?template=feature_request.md) - for suggesting improvements or new additions
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For any questions, please reach out to the AI Engineering team at Vector Institute.
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