awesome-experimental-standards-deep-learning
github.com/kaleidophon/awesome-experimental-standards-deep-learning ↗Repository collecting resources and best practices to improve experimental rigour in deep learning research.
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Add the Context Awesome MCP server to Claude, Cursor, or any MCP client, then ask:
"Show me :computer: codebase & model resources from awesome-experimental-standards-deep-learning"
Installation instructions →What's inside
Resources
- Anonymous Github:computer: Codebase & Model
Website to double-anonymize a Github repository.
- BayesianTestML:microscope: Experiments & Analysis
As baycomp, but also including Julia and R implementations.
- BitBucket:computer: Codebase & Model
A website and cloud-based service that helps developers store and manage their code, as well as track and control changes to their code.
- codecarbon:computer: Codebase & Model
Python package estimating and tracking carbon emission of various kind of computer programs.
- Conda:computer: Codebase & Model
Open Source package management systemand environment management system.
- confidenceinterval:microscope: Experiments & Analysis
Python package that computes confidence intervals for common evaluation metrics.
Experimental Standards for Deep Learning in Natural Language Processing Research
- CHECKLIST.md:jigsaw: Contributing
- RESOURCES.md:jigsaw: Contributing
Showing a sample of 28 resources. View the full list on GitHub →