<?xml version="1.0" encoding="UTF-8"?><rss xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:content="http://purl.org/rss/1.0/modules/content/" xmlns:atom="http://www.w3.org/2005/Atom" version="2.0"><channel><title><![CDATA[RSS Feed]]></title><description><![CDATA[RSS Feed]]></description><link>http://direct.ecency.com</link><image><url>http://direct.ecency.com/logo512.png</url><title>RSS Feed</title><link>http://direct.ecency.com</link></image><generator>RSS for Node</generator><lastBuildDate>Sun, 12 Apr 2026 14:32:19 GMT</lastBuildDate><atom:link href="http://direct.ecency.com/@tukey/rss" rel="self" type="application/rss+xml"/><item><title><![CDATA[Steem奖励计算（公式版）]]></title><description><![CDATA[ 基础版规则：主要针对奖金池、文章奖励、投票者奖励 1.奖励池总数 M=c*N  M：生成总奖金N：新生成区块的数量c：自定义常数 2.所有用户对单篇文章奖励权重 R=∑i*n*l  R：文章奖励权重 i：取值为+1或-1，赞成为+1，反对为-1； n：该投票用户拥有的VESTS数量；（1 SP 约等于2045 VESTS） l：该投票用户投票时消耗能量的百分比；]]></description><link>http://direct.ecency.com/python/@tukey/5jvczf-steem</link><guid isPermaLink="true">http://direct.ecency.com/python/@tukey/5jvczf-steem</guid><category><![CDATA[python]]></category><dc:creator><![CDATA[tukey]]></dc:creator><pubDate>Wed, 07 Mar 2018 07:39:12 GMT</pubDate></item><item><title><![CDATA[关于steem通货膨胀的理解]]></title><description><![CDATA[Steem的白皮书里面有这么一段话： 英文版：How many new tokens are generated by the blockchain? Starting with the network's 16th hard fork in December 2016, Steem began creating new tokens at a yearly inflation rate of 9.5%.]]></description><link>http://direct.ecency.com/inflationt/@tukey/steem</link><guid isPermaLink="true">http://direct.ecency.com/inflationt/@tukey/steem</guid><category><![CDATA[inflationt]]></category><dc:creator><![CDATA[tukey]]></dc:creator><pubDate>Mon, 29 Jan 2018 07:22:51 GMT</pubDate></item><item><title><![CDATA[Top 28 Cheat Sheets for Machine Learning, Data Science]]></title><description><![CDATA[Introduction Data Science is an ever-growing field, there are numerous tools & techniques to remember. It is not possible for anyone to remember all the functions, operations and formulas of each concept.]]></description><link>http://direct.ecency.com/machinelearning/@tukey/top-28-cheat-sheets-for-machine-learning-data-science</link><guid isPermaLink="true">http://direct.ecency.com/machinelearning/@tukey/top-28-cheat-sheets-for-machine-learning-data-science</guid><category><![CDATA[machinelearning]]></category><dc:creator><![CDATA[tukey]]></dc:creator><pubDate>Sat, 27 Jan 2018 09:43:12 GMT</pubDate></item><item><title><![CDATA[My first post—— word cloud visualization]]></title><description><![CDATA[  abstract ： The 19th of the party report word cloud visualization Why make "word cloud"? The most common way to analyze textual content is to extract the words in the text and count the frequency.]]></description><link>http://direct.ecency.com/cn/@tukey/6capfj</link><guid isPermaLink="true">http://direct.ecency.com/cn/@tukey/6capfj</guid><category><![CDATA[cn]]></category><dc:creator><![CDATA[tukey]]></dc:creator><pubDate>Wed, 03 Jan 2018 11:31:06 GMT</pubDate><enclosure url="https://images.ecency.com/p/hgjbks2vRxvp3vwJU3jjRKwcF16wsXHknMJGy6eaYDNo32FuSUm7UAmzTUjtaQUWfcmArcWdYr4qKf2douvZKF4Qqt?format=match&amp;mode=fit" length="0" type="false"/></item></channel></rss>