Kaleidophon/awesome-experimental-standards-deep-learning
Repository collecting resources and best practices to improve experimental rigour in deep learning research.
This collection of resources helps deep learning researchers in Natural Language Processing improve the rigor and reproducibility of their experiments. It provides an up-to-date checklist and links to tools for managing data, developing codebases and models, conducting experiments and analysis, and preparing publications. Researchers in academic or industrial NLP settings will find this useful for standardizing their research workflows.
No commits in the last 6 months.
Use this if you are a deep learning researcher in Natural Language Processing and want to ensure your experiments are robust, reproducible, and meet high academic standards.
Not ideal if you are looking for a general guide to deep learning development or for resources outside the scope of Natural Language Processing research.
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27
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2
Language
Python
License
GPL-3.0
Category
Last pushed
Mar 30, 2023
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