摘要： Machine learning is increasingly moving from hand-designed models to automatically optimized pipelines using tools such as H20, TPOT, and auto-sklearn. These libraries, along with methods such as random search, aim to simplify the model selection and tuning parts of machine learning by finding the best model for a dataset with little to no manual intervention. However, feature engineering, an arguably more valuable aspect of the machine learning pipeline, remains almost entirely a human labor.
摘要： 藉由犧牲個人利益來極大化團隊利益，追求團隊的目標，是人類社會進步的關鍵。然而，合作不再是人類的專利， DeepMind 用 2 對 2 足球賽訓練 AI ，要讓 AI 學會團隊協作。起初個別的 AI 也是以極大化自己利益，後來學會了合作，以最大化團隊利益為目標。雖然這項研究只有 4 個 AI 參與，但它的意義是讓 AI 學會協作，為未來的更多 AI 協作與應用打下基礎。
摘要：
In this post we’re going to work with time series data, and write R functions to aggregate hourly and daily time series in monthly time series to catch a glimpse of their underlying patterns. For this analysis we’re going to use public meteorological data recorded by the government of the Argentinian province of San Luis. Data about rainfalls, temperature, humidity and in some cases winds, is published in the REM website (Red de Estaciones Meteorológicas, http://www.clima.edu.ar/). Also, here you can download meteorological data (in .csv format) that has been recorded by weather stations around different places from San Luis.
