A coding platform focused on deep learning algorithms and implementations of research papers.
- A curated collection of deep learning paper implementations
- A place to explore, test, and compare algorithms in code
- A learning-first codebase with clear, reproducible experiments
papers/: Paper-specific implementations and notesmodels/: Reusable model componentsdatasets/: Dataset loaders and preprocessingexperiments/: Training scripts and configsresults/: Metrics, checkpoints, and evaluation artifactsdocs/: Summaries and references
- Pick a paper in
papers/ - Review the accompanying notes and implementation
- Run the experiment script in
experiments/
- Add a new paper under
papers/ - Include a short summary and citation
- Keep experiments reproducible and well-documented
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