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atom.xml
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<?xml version="1.0" encoding="utf-8"?>
<feed xmlns="http://www.w3.org/2005/Atom">
<title>iyuge2</title>
<icon>https://www.gravatar.com/avatar/0c42aabf4c6601686c22cc1308c318de</icon>
<subtitle>Cosine的贝壳屋</subtitle>
<link href="https://iyuge2.github.io/atom.xml" rel="self"/>
<link href="https://iyuge2.github.io/"/>
<updated>2022-07-08T15:00:00.714Z</updated>
<id>https://iyuge2.github.io/</id>
<author>
<name>iyuge2</name>
<email>[email protected]</email>
</author>
<generator uri="https://hexo.io/">Hexo</generator>
<entry>
<title>搜广推资源合集</title>
<link href="https://iyuge2.github.io/2022/reco-collections/"/>
<id>https://iyuge2.github.io/2022/reco-collections/</id>
<published>2022-07-07T16:00:00.000Z</published>
<updated>2022-07-08T15:00:00.714Z</updated>
<summary type="html"><h1 id="相关-github"><a class="markdownIt-Anchor" href="#相关-github"></a> 相关 Github</h1>
<h2 id="论文合集"><a class="markdownIt-Anchor"</summary>
<category term="技术区" scheme="https://iyuge2.github.io/categories/%E6%8A%80%E6%9C%AF%E5%8C%BA/"/>
<category term="合集, 搜索，广告，推荐" scheme="https://iyuge2.github.io/tags/%E5%90%88%E9%9B%86-%E6%90%9C%E7%B4%A2%EF%BC%8C%E5%B9%BF%E5%91%8A%EF%BC%8C%E6%8E%A8%E8%8D%90/"/>
</entry>
<entry>
<title>推荐常用指标解析</title>
<link href="https://iyuge2.github.io/2022/reco-rank-evals/"/>
<id>https://iyuge2.github.io/2022/reco-rank-evals/</id>
<published>2022-06-30T16:00:00.000Z</published>
<updated>2022-07-30T15:33:55.240Z</updated>
<summary type="html"><h2 id="参考"><a class="markdownIt-Anchor" href="#参考"></a> 参考</h2>
<ul>
<li><a</summary>
<category term="技术区" scheme="https://iyuge2.github.io/categories/%E6%8A%80%E6%9C%AF%E5%8C%BA/"/>
<category term="推荐" scheme="https://iyuge2.github.io/tags/%E6%8E%A8%E8%8D%90/"/>
</entry>
<entry>
<title>机器学习系列——EM算法</title>
<link href="https://iyuge2.github.io/2021/ml-em/"/>
<id>https://iyuge2.github.io/2021/ml-em/</id>
<published>2021-04-18T16:00:00.000Z</published>
<updated>2021-04-21T02:42:10.030Z</updated>
<summary type="html"><h3 id="基本原理"><a class="markdownIt-Anchor" href="#基本原理"></a> 基本原理</h3>
<h3 id="优缺点"><a class="markdownIt-Anchor" href="#优缺点"></a></summary>
<category term="技术区" scheme="https://iyuge2.github.io/categories/%E6%8A%80%E6%9C%AF%E5%8C%BA/"/>
<category term="机器学习" scheme="https://iyuge2.github.io/tags/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0/"/>
</entry>
<entry>
<title>Python可视化包汇总</title>
<link href="https://iyuge2.github.io/2021/python-vis/"/>
<id>https://iyuge2.github.io/2021/python-vis/</id>
<published>2021-03-03T02:43:11.000Z</published>
<updated>2021-04-18T15:07:44.889Z</updated>
<summary type="html"><table>
<thead>
<tr>
<th>Package Name</th>
<th>Tutorial</th>
</tr>
</thead>
<tbody>
<tr>
<td>Matplotlib</td>
<td><a</summary>
<category term="技术区" scheme="https://iyuge2.github.io/categories/%E6%8A%80%E6%9C%AF%E5%8C%BA/"/>
<category term="Python" scheme="https://iyuge2.github.io/tags/Python/"/>
</entry>
<entry>
<title>Git使用理解</title>
<link href="https://iyuge2.github.io/2021/use-git/"/>
<id>https://iyuge2.github.io/2021/use-git/</id>
<published>2021-01-27T15:46:20.000Z</published>
<updated>2021-03-01T09:20:07.192Z</updated>
<summary type="html"><ul>
<li>撤销提交</li>
</ul>
<figure class="highlight plain"><table><tbody><tr><td class="gutter"><pre><span class="line">1</span><br><span</summary>
<category term="技术区" scheme="https://iyuge2.github.io/categories/%E6%8A%80%E6%9C%AF%E5%8C%BA/"/>
<category term="git" scheme="https://iyuge2.github.io/tags/git/"/>
</entry>
<entry>
<title>Python中日志模块的简易用法</title>
<link href="https://iyuge2.github.io/2021/python-logging/"/>
<id>https://iyuge2.github.io/2021/python-logging/</id>
<published>2021-01-25T05:44:58.000Z</published>
<updated>2021-03-01T09:20:07.186Z</updated>
<summary type="html"><h3 id="写在前面"><a class="markdownIt-Anchor" href="#写在前面"></a></summary>
<category term="技术区" scheme="https://iyuge2.github.io/categories/%E6%8A%80%E6%9C%AF%E5%8C%BA/"/>
<category term="Python" scheme="https://iyuge2.github.io/tags/Python/"/>
</entry>
<entry>
<title>Python中推荐学习的模块集合</title>
<link href="https://iyuge2.github.io/2021/python-stl/"/>
<id>https://iyuge2.github.io/2021/python-stl/</id>
<published>2021-01-25T03:56:22.000Z</published>
<updated>2021-03-01T09:20:07.189Z</updated>
<summary type="html"><p>###写在前面</p>
<blockquote>
<p>这篇文章总结了python中必会库的使用教程。若有易读的官方文档，则转官方文档链接；否则，笔者会记录自己的学习笔记。</p>
</blockquote>
<h3 id="总结"><a</summary>
<category term="技术区" scheme="https://iyuge2.github.io/categories/%E6%8A%80%E6%9C%AF%E5%8C%BA/"/>
<category term="Python" scheme="https://iyuge2.github.io/tags/Python/"/>
</entry>
<entry>
<title>R语言-批量生存分析</title>
<link href="https://iyuge2.github.io/2021/batch-surv-analysis/"/>
<id>https://iyuge2.github.io/2021/batch-surv-analysis/</id>
<published>2021-01-16T16:00:00.000Z</published>
<updated>2021-01-17T08:11:08.587Z</updated>
<summary type="html"><h3 id="需求"><a class="markdownIt-Anchor" href="#需求"></a></summary>
<category term="技术区" scheme="https://iyuge2.github.io/categories/%E6%8A%80%E6%9C%AF%E5%8C%BA/"/>
<category term="R" scheme="https://iyuge2.github.io/tags/R/"/>
</entry>
<entry>
<title>R语言知识点小结</title>
<link href="https://iyuge2.github.io/2021/r/"/>
<id>https://iyuge2.github.io/2021/r/</id>
<published>2021-01-03T16:00:00.000Z</published>
<updated>2021-01-26T15:21:45.595Z</updated>
<summary type="html"><h3 id="写在前面"><a class="markdownIt-Anchor" href="#写在前面"></a> 写在前面</h3>
<blockquote>
<p>之前没接触过R，所以，会稍微记得详细点,</summary>
<category term="技术区" scheme="https://iyuge2.github.io/categories/%E6%8A%80%E6%9C%AF%E5%8C%BA/"/>
<category term="R" scheme="https://iyuge2.github.io/tags/R/"/>
</entry>
<entry>
<title>python中的json操作</title>
<link href="https://iyuge2.github.io/2020/python-json/"/>
<id>https://iyuge2.github.io/2020/python-json/</id>
<published>2020-11-30T11:17:36.000Z</published>
<updated>2021-01-13T15:27:30.938Z</updated>
<summary type="html"><script>
document.querySelectorAll('.github-emoji')
.forEach(el => {
if (!el.dataset.src) { return; }
</summary>
<category term="技术区" scheme="https://iyuge2.github.io/categories/%E6%8A%80%E6%9C%AF%E5%8C%BA/"/>
<category term="python" scheme="https://iyuge2.github.io/tags/python/"/>
</entry>
<entry>
<title>Mysql的常用操作</title>
<link href="https://iyuge2.github.io/2020/use-mysql/"/>
<id>https://iyuge2.github.io/2020/use-mysql/</id>
<published>2020-10-18T16:00:00.000Z</published>
<updated>2021-01-13T17:09:39.997Z</updated>
<summary type="html"><blockquote>
<p>mysql 5.7</p>
</blockquote>
<h3 id="基本知识"><a class="markdownIt-Anchor" href="#基本知识"></a></summary>
<category term="技术区" scheme="https://iyuge2.github.io/categories/%E6%8A%80%E6%9C%AF%E5%8C%BA/"/>
<category term="数据库" scheme="https://iyuge2.github.io/tags/%E6%95%B0%E6%8D%AE%E5%BA%93/"/>
</entry>
<entry>
<title>Linux环境下的“Python + Flask + Mysql”配置</title>
<link href="https://iyuge2.github.io/2020/linux-flask-mysql/"/>
<id>https://iyuge2.github.io/2020/linux-flask-mysql/</id>
<published>2020-10-16T16:00:00.000Z</published>
<updated>2021-01-13T17:06:37.189Z</updated>
<summary type="html"><h3 id="linux服务端配置"><a class="markdownIt-Anchor" href="#linux服务端配置"></a></summary>
<category term="技术区" scheme="https://iyuge2.github.io/categories/%E6%8A%80%E6%9C%AF%E5%8C%BA/"/>
<category term="Linux" scheme="https://iyuge2.github.io/tags/Linux/"/>
<category term="环境配置" scheme="https://iyuge2.github.io/tags/%E7%8E%AF%E5%A2%83%E9%85%8D%E7%BD%AE/"/>
</entry>
<entry>
<title>程序员面试题汇总</title>
<link href="https://iyuge2.github.io/2020/interview/"/>
<id>https://iyuge2.github.io/2020/interview/</id>
<published>2020-10-12T02:13:00.000Z</published>
<updated>2021-01-13T15:46:57.452Z</updated>
<summary type="html"><h3 id="计算机基础"><a class="markdownIt-Anchor" href="#计算机基础"></a> 计算机基础</h3>
<h4 id="操作系统"><a class="markdownIt-Anchor" href="#操作系统"></a></summary>
<category term="技术区" scheme="https://iyuge2.github.io/categories/%E6%8A%80%E6%9C%AF%E5%8C%BA/"/>
<category term="工作" scheme="https://iyuge2.github.io/tags/%E5%B7%A5%E4%BD%9C/"/>
</entry>
<entry>
<title>机器学习系列——支持向量机</title>
<link href="https://iyuge2.github.io/2020/ml-svm/"/>
<id>https://iyuge2.github.io/2020/ml-svm/</id>
<published>2020-10-08T08:14:49.000Z</published>
<updated>2021-01-13T15:46:47.430Z</updated>
<summary type="html"><h3 id="基本原理"><a class="markdownIt-Anchor" href="#基本原理"></a></summary>
<category term="技术区" scheme="https://iyuge2.github.io/categories/%E6%8A%80%E6%9C%AF%E5%8C%BA/"/>
<category term="机器学习" scheme="https://iyuge2.github.io/tags/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0/"/>
</entry>
<entry>
<title>机器学习系列——逻辑斯谛回归</title>
<link href="https://iyuge2.github.io/2020/ml-lr/"/>
<id>https://iyuge2.github.io/2020/ml-lr/</id>
<published>2020-10-08T07:17:49.000Z</published>
<updated>2022-05-13T12:03:46.681Z</updated>
<summary type="html"><h3 id="基本原理"><a class="markdownIt-Anchor" href="#基本原理"></a></summary>
<category term="技术区" scheme="https://iyuge2.github.io/categories/%E6%8A%80%E6%9C%AF%E5%8C%BA/"/>
<category term="机器学习" scheme="https://iyuge2.github.io/tags/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0/"/>
</entry>
<entry>
<title>机器学习系列—朴素贝叶斯</title>
<link href="https://iyuge2.github.io/2020/ml-naivebayes/"/>
<id>https://iyuge2.github.io/2020/ml-naivebayes/</id>
<published>2020-10-07T07:59:29.000Z</published>
<updated>2021-01-13T17:08:05.848Z</updated>
<summary type="html"><blockquote>
<p>朴素贝叶斯算法是基于贝叶斯定理和特征条件独立性假设的分类方法。</p>
</blockquote>
<h3 id="基本原理"><a class="markdownIt-Anchor" href="#基本原理"></a></summary>
<category term="技术区" scheme="https://iyuge2.github.io/categories/%E6%8A%80%E6%9C%AF%E5%8C%BA/"/>
<category term="机器学习" scheme="https://iyuge2.github.io/tags/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0/"/>
</entry>
<entry>
<title>机器学习系列——KMeans</title>
<link href="https://iyuge2.github.io/2020/ml-kmeans/"/>
<id>https://iyuge2.github.io/2020/ml-kmeans/</id>
<published>2020-10-06T16:00:00.000Z</published>
<updated>2021-04-19T12:36:55.649Z</updated>
<summary type="html"><blockquote>
<p>K-Means算法是一种常用的无监督聚类算法。"其步骤是，预将数据分为K组，则随机选取K个对象作为初始的聚类中心，然后计算每个对象与各个种子聚类中心之间的距离，把每个对象分配给距离它最近的聚类中心。聚类中心以及分配给它们的对象就代表一个聚类。每分配</summary>
<category term="技术区" scheme="https://iyuge2.github.io/categories/%E6%8A%80%E6%9C%AF%E5%8C%BA/"/>
<category term="机器学习" scheme="https://iyuge2.github.io/tags/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0/"/>
</entry>
<entry>
<title>机器学习系列—决策树</title>
<link href="https://iyuge2.github.io/2020/ml-decisiontree/"/>
<id>https://iyuge2.github.io/2020/ml-decisiontree/</id>
<published>2020-10-06T16:00:00.000Z</published>
<updated>2021-04-19T12:40:11.704Z</updated>
<summary type="html"><blockquote>
<p>决策树模型可以同时用于分类和回归任务，该模型的解释性较好。包含三个关键步骤：特征选择、树的生成和树的剪枝。</p>
</blockquote>
<h3 id="基本原理"><a class="markdownIt-Anchor"</summary>
<category term="技术区" scheme="https://iyuge2.github.io/categories/%E6%8A%80%E6%9C%AF%E5%8C%BA/"/>
<category term="机器学习" scheme="https://iyuge2.github.io/tags/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0/"/>
</entry>
<entry>
<title>数学系列——对偶问题</title>
<link href="https://iyuge2.github.io/2020/dual-problem/"/>
<id>https://iyuge2.github.io/2020/dual-problem/</id>
<published>2020-10-06T08:09:36.000Z</published>
<updated>2021-01-13T17:05:23.482Z</updated>
<summary type="html"><h3 id="原始问题"><a class="markdownIt-Anchor" href="#原始问题"></a> 原始问题</h3>
<p>考虑一般的带约束优化问题：</p>
<p class="katex-block"><span</summary>
<category term="技术区" scheme="https://iyuge2.github.io/categories/%E6%8A%80%E6%9C%AF%E5%8C%BA/"/>
<category term="数学" scheme="https://iyuge2.github.io/tags/%E6%95%B0%E5%AD%A6/"/>
</entry>
<entry>
<title>机器学习系列——K近邻法</title>
<link href="https://iyuge2.github.io/2020/ml-knn/"/>
<id>https://iyuge2.github.io/2020/ml-knn/</id>
<published>2020-10-05T16:00:00.000Z</published>
<updated>2021-04-19T12:51:18.862Z</updated>
<summary type="html"><blockquote>
<p>K近邻法（K-nearest neighbor,</summary>
<category term="技术区" scheme="https://iyuge2.github.io/categories/%E6%8A%80%E6%9C%AF%E5%8C%BA/"/>
<category term="机器学习" scheme="https://iyuge2.github.io/tags/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0/"/>
</entry>
</feed>