星海浮生

In this work we present a definition of a basis certificate and develop a strongly polynomial algorithm which given a Farkas type certificate of infeasibility computes a basis certificate of infeasibility.

R语言的leaflet包使用说明，可用于以地图为背景的经纬度位置的可视化 注：本文件只含有leaflet的使用说明，不含leaflet.extra的使用说明

R语言的leaflet包使用说明，可用于以地图为背景的热力图绘制 注：本文件只含有leaflet包的额外功能，而非基本功能，如只需基本功能的说明，请查询leaflet.pdf

Forecasting is a common data science task that helps organizations with capacity planning, goal setting, and anomaly detection. Despite its importance, there are serious challenges associated with producing reliable and high quality forecasts - especially when there are a variety of time series and analysts with expertise in time series modeling are relatively rare. To address these challenges, we describe a practical approach to forecasting "at scale" that combines configurable models with analyst-in-the-loop performance analysis. We propose a modular regression model with interpretable parameters that can be intuitively adjusted by analysts with domain knowledge about the time series. We describe performance analyses to compare and evaluate forecasting procedures, and automatically flag forecasts for manual review and adjustment. Tools that help analysts to use their expertise most effectively enable reliable, practical forecasting of business time series.

李宏毅老师的 2021 年机器学习课程讲义：self-attention 课程链接： https://www.bilibili.com/video/BV1Wv411h7kN?p=38 https://www.bilibili.com/video/BV1Wv411h7kN?p=39

李宏毅老师的 2021 年机器学习课程讲义：transformer 课程链接： https://www.bilibili.com/video/BV1Wv411h7kN?p=49 https://www.bilibili.com/video/BV1Wv411h7kN?p=50

李宏毅老师的 2021 年机器学习课程讲义：CNN（卷积神经网络）

2011, False-Positive Psychology: Undisclosed Flexibility in Data Collection and Analysis Allows Presenting Anything as Significant

2005, An Idea Based on Honey Bee Swarm for Numerical Optimization

线性规划中，原问题与对偶问题的可行性分析 This set of notes proves one such theorem, called the Farkas alternative and shows that, in fact, it underpins all the duality theory of linear programming. It underlies, in fact, most of optimisaiton, itself being a particular case of the Separating Hyperplane Theorem.