Kenny-EPAM-DIAL/applied-ml-evaluation
Applied ML Evaluation Papers is a public, practitioner-focused repository of two technical papers that document rigorous evaluation and validation methods for modern machine learning systems used in real deployments. The collection is designed for independent review and auditability: each paper is provided as a text-based PDF.
Stars
2
Forks
1
Language
—
License
GPL-2.0
Category
Last pushed
Dec 16, 2025
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/Kenny-EPAM-DIAL/applied-ml-evaluation"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
csinva/csinva.github.io
Slides, paper notes, class notes, blog posts, and research on ML 📉, statistics 📊, and AI 🤖.
ml-tooling/best-of-jupyter
🏆 A ranked list of awesome Jupyter Notebook, Hub and Lab projects (extensions, kernels, tools)....
louisfb01/start-machine-learning
A complete guide to start and improve in machine learning (ML), artificial intelligence (AI) in...
leehanchung/awesome-full-stack-machine-learning-courses
Curated list of publicly accessible machine learning engineering courses from CalTech, Columbia,...
harleyszhang/cv_note
记录cv算法工程师的成长之路，分享计算机视觉和模型压缩部署技术栈笔记。https://harleyszhang.github.io/cv_note/