机器学习

这篇文章由胡晨辉，VanjaPaunic，洪，吴涛，周贤德合著。

时间序列预测是数据科学中最重要的课题之一。假设你是一名企业主，你可能想预测未来不同类型的事件，以做出更好的决策并优化资源分配。时间序列预测用例的典型示例包括零售销售预测、包裹发货延迟预测、能源需求预测和财务预测。正如你所看到的，预测无处不在！

鉴于其无处不在的性质和广泛的业务应用，我们开发了一个开源预测回购，将世界级的模型和预测最佳实践交给数据科学家和行业专家，即您！

谷歌广受欢迎的开源TensorFlow机器学习平台的创新包括自动微分（Autograd）和用于深度学习的XLA（加速线性代数）优化编译器。JAX同样来自谷歌，是另一个将这两种技术结合在一起的项目，它在速度和性能方面提供了相当大的好处。当在GPU或TPU上运行时，JAX可以替换其他调用NumPy的程序，但它的程序运行得更快。（Autograd引擎可以自动区分原生Python和NumPy代码。）此外，将JAX用于神经网络可以使添加新功能比扩展TensorFlow等更大的框架更容易。

——马丁·海勒

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.