HazyResearch/data-centric-ai
Resources for Data Centric AI
This resource provides a curated collection of techniques and insights for improving artificial intelligence systems by focusing on the quality and structure of data, rather than just tweaking models. It offers guidance on how to programmatically label data, augment datasets, and clean information to get better results from your AI. This is for AI practitioners, machine learning engineers, and data scientists who are building or deploying AI models and need practical strategies to enhance their performance.
1,134 stars. No commits in the last 6 months.
Use this if you are struggling with poor AI model performance and suspect the issue lies with your training data rather than the model architecture itself.
Not ideal if you are primarily interested in developing new AI model architectures or optimizing existing ones without touching the underlying data.
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TeX
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Apache-2.0
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Last pushed
Dec 13, 2023
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