DataScienceAI Toolkit List

• [原]深入对比数据科学工具箱：Python 和 R 之争

• Evaluation of Deep Learning Toolkits

• 2015-Neural Networks, Types, and Functional Programming: At the moment, deep learning is held together by an extremely successful tool. This tool doesn’t seem fundamental; it’s something we’ve stumbled on, with seemingly arbitrary details that change regularly. As a field, we don’t yet have some unifying insight or shared understanding. In fact, the field has several competing narratives!

Resource

Course

• 2022-机器学习编译 #Course#: 这些模型训练和推理问题，涉及机器学习编程范式、基于学习的搜索算法、编译优化以及计算运行时。这些话题的组合生成了一个全新主题——机器学习编译，并且该方向正在不断持续发展。在本课程中，我们讲按照其中的关键元素，系统地研究这一新兴领域的关键要素。我们将学习一些核心的概念，用以表示机器学习程序、自动优化技术，以及在端到端机器学习部署中优化环境依赖、内存和性能的方法。

StandML

• 2019-A Tour of Standard ML #Series#: The tour consists of a set of chapters, each intended to showcase different features of Standard ML.

Production

• 2020-12 Factors of reproducible Machine Learning in production: The last two decades have yielded us some great understandings about Software Development. A big part of that is due to the emergence of DevOps and it’s wide adoption throughout the industry.