机器学习

MLOps Core

1. Machine Learning Operations: You Design It, You Train It, You Run It!
2. MLOps SIG Specification
3. ML in Production
4. Awesome production machine learning: State of MLOps Tools and Frameworks
5. Udemy “Deplo

A curated list of resources for R Shiny. (Featured on Awesome-R)

Contents

Software Engineering for Machine Learning are techniques and guidelines for building ML applications that do not concern the core ML problem -- e.g. the development of new algorithms -- but rather the surrounding activities like data ingestion, coding, testing, versioning, deployment, quality control, and team collaboration. Good software engineering practices enhance development, deployment and maintenance of production level applications using machine learning components.

⭐ Must-read

🎓 Scientific publication

A curated list of awesome Semi-Supervised Learning resources. Inspired by awesome-deep-vision, awesome-deep-learning-papers, and awesome-self-supervised-learning.

A collection of edge detection papers and corresponding source code/demo program (a.k.a. contour detection or boundary detection).

Feel free to create a PR or an issue. (Pull Request is preferred)

Outline

A curated list of awesome Semi-Supervised Learning resources. Inspired by awesome-deep-vision, awesome-deep-learning-papers, and awesome-self-supervised-learning.

A curated list of research in machine learning system. Link to the code if available is also present. Now we have a team to maintain this project. You are very welcome to pull request by using our template.

A curated list of MXNet examples, tutorials, papers, conferences and blogs.

Contributing

If you want to contribute to this list and the examples, please open a new pull request.

Table of Contents

• Survey
• Papers
• Talks

Survey

• Neural Volume Rendering: NeRF And Beyond, Dellaert and Yen-Chen, Arxiv 2020 | blog | 

Table of Contents

• Blogs
• Papers
• Talks

Blogs

• Breaking Linear Classifiers on ImageNet, A.