Implementation Details¶
Each repository in this catalog contains implementations of specific machine learning techniques and algorithms. The following information is provided for each repository:
- Repository Link: Direct link to the GitHub repository
- Description: Brief introduction to the repository's purpose and links to relevant research papers
- Algorithms: List of ML algorithms demonstrated in the repository
- Datasets: Information on datasets used, with links to publicly available data
- Type: The category of implementation:
- bootcamp: Educational implementations developed for workshops and learning purposes
- tool: Utility libraries and tools for broader use
- applied-research: Research code tied to specific projects or papers
- Year: The year the implementation was published
Usage Notes¶
Note
- Many repositories contain code for reference purposes only. To run them, updates may be required to the code and environment files.
- Links for only publicly available datasets are provided. Many datasets used in the repositories are only available on the Vector cluster.
Repository Organization¶
The catalog is organized by implementation type to help you quickly find the resources you need:
- Bootcamp implementations: Educational resources designed for workshops and learning purposes
- Tool implementations: Utility libraries and general-purpose tools
- Applied Research implementations: Code tied to specific research projects or papers
Each implementation includes algorithm tags, dataset information, and other metadata to aid in discovery.
Contributing¶
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.
To submit issues or suggestions, please use our provided templates:
- Report a bug - for reporting problems or errors
- Request a feature - for suggesting improvements or new additions
For any questions, please reach out to the AI Engineering team at Vector Institute.