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<pubDate>Tue, 21 Apr 2026 22:28:29 +0800</pubDate>
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<title>大数据 & AI</title>
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<title>大数据 & AI</title>
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<dc:date>Tue, 21 Apr 2026 22:28:29 +0800</dc:date>
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<title><![CDATA[Linux环境离线安装docker&docker-compose]]></title>
<link><![CDATA[https://www.tsingfun.com/it/bigdata_ai/linux_docker_install.html]]></link>
<description><![CDATA[<img src=https://www.tsingfun.com/uploadfile/2024/0814/thumb_150_150_20240814021634552.png border='0' /><br />一、docker离线安装1、下载docker离线安装包下载最新版本的 docker （或者选择自己想要安装的版本）到本地。1）docker下载地址：Docker版本下载 ||Docker-compose版本下载备注   ]]></description>
<pubDate>2024-08-14 14:14:13</pubDate>
<guid><![CDATA[https://www.tsingfun.com/it/bigdata_ai/linux_docker_install.html]]></guid>
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<title><![CDATA[tinygrad：不到1000行代码的深度学习框架，天才黑客开源GitHub 2.3k+ stars]]></title>
<link><![CDATA[https://www.tsingfun.com/it/bigdata_ai/2541.html]]></link>
<description><![CDATA[<img src=https://www.tsingfun.com/uploadfile/2021/0104/thumb_150_150_20210104101632378.jpg border='0' /><br />近期，一个不到1000行的深度学习框架tinygrad火了，麻雀虽小，但五脏俱全，这个深度学习框架使用起来和PyTorch类似，目前已经开源在GitHub上，而且收获了2 3K星：项目地址：https:   ]]></description>
<pubDate>2021-01-04 10:07:01</pubDate>
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<title><![CDATA[Python学习之Jupyter Notebook和highchart安装]]></title>
<link><![CDATA[https://www.tsingfun.com/it/bigdata_ai/2295.html]]></link>
<description><![CDATA[<img src=https://m.tsingfun.com/statics/images/nopic.gif border='0' /><br />运行环境Win7 64位 + Python3 5 2一、安装网页端编译器Jupyter Notebook在cmd输入pip3 install jupyter在cmd输入jupyter notebook,   ]]></description>
<pubDate>2018-01-18 22:59:22</pubDate>
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<title><![CDATA[Python Charts库(Highcharts API的封装)]]></title>
<link><![CDATA[https://www.tsingfun.com/it/bigdata_ai/2294.html]]></link>
<description><![CDATA[<img src=https://m.tsingfun.com/statics/images/nopic.gif border='0' /><br />charts库实际是对调用Highcharts API 进行封装,通过python生成Highcharts脚本Highcharts中文网:http:  v1 hcharts cn demo index php?p=1   ]]></description>
<pubDate>2018-01-18 22:51:10</pubDate>
<guid><![CDATA[https://www.tsingfun.com/it/bigdata_ai/2294.html]]></guid>
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<title><![CDATA[理解Python的 with 语句]]></title>
<link><![CDATA[https://www.tsingfun.com/it/bigdata_ai/2293.html]]></link>
<description><![CDATA[With语句是什么？Python&amp;rsquo;s with statement provides a very convenient way of dealing with the situation where you   ]]></description>
<pubDate>2018-01-09 17:52:13</pubDate>
<guid><![CDATA[https://www.tsingfun.com/it/bigdata_ai/2293.html]]></guid>
<author>https://m.tsingfun.com</author>
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<title><![CDATA[Python中的X[:,0]和X[:,1]]]></title>
<link><![CDATA[https://www.tsingfun.com/it/bigdata_ai/2291.html]]></link>
<description><![CDATA[X[:,0]是numpy中数组的一种写法，表示对一个二维数组，取该二维数组第一维中的所有数据，第二维中取第0个数据，直观来说，X[:,0]就是取所有   ]]></description>
<pubDate>2018-01-03 18:09:47</pubDate>
<guid><![CDATA[https://www.tsingfun.com/it/bigdata_ai/2291.html]]></guid>
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<title><![CDATA[Windows下使用Anaconda环境安装tensorflow]]></title>
<link><![CDATA[https://www.tsingfun.com/it/bigdata_ai/2289.html]]></link>
<description><![CDATA[下载Anacondahttps:  www continuum io downloads 下载你要安装的平台的安装包，记得下载python3 6的版本Anconda配置源设置国内镜像      ]]></description>
<pubDate>2018-01-02 15:51:56</pubDate>
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<title><![CDATA[推荐引擎easyrec半天学习分享]]></title>
<link><![CDATA[https://www.tsingfun.com/it/bigdata_ai/2238.html]]></link>
<description><![CDATA[<img src=https://www.tsingfun.com/uploadfile/2017/0504/20170504095226443.png border='0' /><br />推荐引擎（Recommendation）的原理，大家可以参考这个文章：探索推荐引擎内部的秘密，第 1 部分: 推荐引擎初探这两天在学习推荐引擎，昨...]]></description>
<pubDate>2017-05-04 09:51:49</pubDate>
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<title><![CDATA[从源代码剖析Mahout推荐引擎]]></title>
<link><![CDATA[https://www.tsingfun.com/it/bigdata_ai/2236.html]]></link>
<description><![CDATA[<img src=https://www.tsingfun.com/uploadfile/2017/0504/thumb_150_150_20170504094637240.png border='0' /><br />前言Mahout框架中cf.taste包实现了推荐算法引擎，它提供了一套完整的推荐算法工具集，同时规范了数据结构，并标准化了程序开发过程。应用推...]]></description>
<pubDate>2017-05-04 09:44:28</pubDate>
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<title><![CDATA[mongodb, replicates and error: { “$err” : “not master and slaveOk=false”, “code” : 13435]]></title>
<link><![CDATA[https://www.tsingfun.com/it/bigdata_ai/1803.html]]></link>
<description><![CDATA[出现这个错误的原因是在从库上执行命令导致，默认情况下只有主库可以执行命令。当然可以通过设置使得从库也能执行命令，具体参见：http:  s   ]]></description>
<pubDate>2015-08-12 10:13:00</pubDate>
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<title><![CDATA[MongoVUE查询结果中时间相差8小时？]]></title>
<link><![CDATA[https://www.tsingfun.com/it/bigdata_ai/1801.html]]></link>
<description><![CDATA[<img src=https://www.tsingfun.com/uploadfile/2016/0628/thumb_150_150_20160628101129134.png border='0' /><br />现象，如下图：和实际时间相差8个小时，首先想到的就是时区问题，要对MongoVUE的时间显示进行设置，如下：设置完成后，点刷新，然后就可以   ]]></description>
<pubDate>2016-06-28 10:11:01</pubDate>
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<title><![CDATA[C# 操作MongoDb插入、更新、查询]]></title>
<link><![CDATA[https://www.tsingfun.com/it/bigdata_ai/1800.html]]></link>
<description><![CDATA[Mongo连接字符串形式：mongodb:  127.0.0.1:27017   插入或更新一条记录BsonDocument doc = new BsonDocument();...MongoServer serv...]]></description>
<pubDate>2016-06-28 10:09:57</pubDate>
<guid><![CDATA[https://www.tsingfun.com/it/bigdata_ai/1800.html]]></guid>
<author>https://m.tsingfun.com</author>
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<item>
<title><![CDATA[Tokumx 副本集（集群）全攻略]]></title>
<link><![CDATA[https://www.tsingfun.com/it/bigdata_ai/1798.html]]></link>
<description><![CDATA[cacheSize：缓存数据占用内存大小。（不设置默认缓存会占满机器内存）oplogSize：那什么是oplog的大小？前面说过oplog保存了数据的操作记录   ]]></description>
<pubDate>2016-06-28 10:07:21</pubDate>
<guid><![CDATA[https://www.tsingfun.com/it/bigdata_ai/1798.html]]></guid>
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<title><![CDATA[MongoDB.Driver.MongoConnectionException: Unable to connect to the primary member]]></title>
<link><![CDATA[https://www.tsingfun.com/it/bigdata_ai/1797.html]]></link>
<description><![CDATA[MongoDB Driver MongoConnectionException: Unable to connect to the primary member of the replica set: Too many thread   ]]></description>
<pubDate>2016-06-28 10:06:26</pubDate>
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<title><![CDATA[mongodb 以管理员登录并创建 database]]></title>
<link><![CDATA[https://www.tsingfun.com/it/bigdata_ai/1796.html]]></link>
<description><![CDATA[mongodb 以管理员登录并创建 database在一个有了用户名的数据库集中,即使在 admin 数据库中创建了用户,登录上去后还是不能访问其他数据   ]]></description>
<pubDate>2016-06-28 10:05:04</pubDate>
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<title><![CDATA[Too many threads are already waiting for a connection]]></title>
<link><![CDATA[https://www.tsingfun.com/it/bigdata_ai/1795.html]]></link>
<description><![CDATA[由于工作线程数大于MongoDB的最大连接池数量，从而出现此类异常。解决方法：一、减少工作线程数，示意代码如下：ParallelOptions parallel   ]]></description>
<pubDate>2016-06-28 10:02:52</pubDate>
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<title><![CDATA[mongodb最大连接数配置修改]]></title>
<link><![CDATA[https://www.tsingfun.com/it/bigdata_ai/1794.html]]></link>
<description><![CDATA[查看mongodb最大连接数mongodb bin mongo&gt;db serverStatus() connections;{ &quot;current&quot; : 308, &quot;available&quot; : 511, &quot;totalCreated&quot;    ]]></description>
<pubDate>2016-06-28 10:02:10</pubDate>
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<title><![CDATA[MongoDB sort排序、index索引教程]]></title>
<link><![CDATA[https://www.tsingfun.com/it/bigdata_ai/1107.html]]></link>
<description><![CDATA[MongoDB sort()排序方法在MongoDB中使用使用sort()方法对数据进行排序，sort()方法可以通过参数指定排序的字段，并使用 1 和 -1 来指...]]></description>
<pubDate>2015-11-25 09:16:10</pubDate>
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<title><![CDATA[如何选择机器学习算法]]></title>
<link><![CDATA[https://www.tsingfun.com/it/bigdata_ai/Choosing-a-Machine-Learning-Classifier.html]]></link>
<description><![CDATA[如何针对某个分类问题决定使用何种机器学习算法？ 当然，如果你真心在乎准确率，最好的途径就是测试一大堆各式各样的算法（同时确保在每个算法上也测试不同的参数），最后选择在交叉验证中表现最好的。]]></description>
<pubDate>2015-11-24 08:50:40</pubDate>
<guid><![CDATA[https://www.tsingfun.com/it/bigdata_ai/Choosing-a-Machine-Learning-Classifier.html]]></guid>
<author>https://m.tsingfun.com</author>
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<title><![CDATA[在MongoDB中模拟Auto Increment]]></title>
<link><![CDATA[https://www.tsingfun.com/it/bigdata_ai/1082.html]]></link>
<description><![CDATA[MySQL用户多半都有Auto Increment情结，不过MongoDB缺省并没有实现，所以需要模拟一下，编程语言以PHP为例，代码大致如下所示：&lt;?phpfunct...]]></description>
<pubDate>2015-11-23 16:06:37</pubDate>
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