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<?xml version="1.0" encoding="utf-8"?> | ||
<feed xmlns="http://www.w3.org/2005/Atom"> | ||
<title>希亚的西红柿のBlog</title> | ||
<icon>https://npm.elemecdn.com/nova1751-sources/blogImg/icon.png</icon> | ||
<subtitle>分享学习与生活</subtitle> | ||
<link href="http://refrain.cf/atom.xml" rel="self"/> | ||
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<link href="http://refrain.cf/"/> | ||
<updated>2023-09-26T14:08:00.000Z</updated> | ||
<id>http://refrain.cf/</id> | ||
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<author> | ||
<name>希亚的西红柿</name> | ||
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</author> | ||
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<generator uri="https://hexo.io/">Hexo</generator> | ||
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<entry> | ||
<title>Windows/Ubuntu双系统安装教程</title> | ||
<link href="http://refrain.cf/posts/d3748e5f/"/> | ||
<id>http://refrain.cf/posts/d3748e5f/</id> | ||
<published>2023-03-04T09:52:55.000Z</published> | ||
<updated>2023-09-26T14:08:00.000Z</updated> | ||
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<summary type="html"><div class="tip home"><p>本文主要记录笔者 PC 安装 Windows/Ubuntu 双系统的过程、Windows 平台下 VMware 虚拟机安装 Ubuntu 的配置过程、以及双系统 Ubuntu 卸载的注意事项。</p> | ||
</div> | ||
<div</summary> | ||
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<category term="百宝箱" scheme="http://refrain.cf/categories/%E7%99%BE%E5%AE%9D%E7%AE%B1/"/> | ||
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<category term="Windows" scheme="http://refrain.cf/tags/Windows/"/> | ||
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<category term="Ubuntu" scheme="http://refrain.cf/tags/Ubuntu/"/> | ||
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<category term="虚拟机" scheme="http://refrain.cf/tags/%E8%99%9A%E6%8B%9F%E6%9C%BA/"/> | ||
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</entry> | ||
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<entry> | ||
<title>JAVA学习笔记</title> | ||
<link href="http://refrain.cf/posts/8d55d49a/"/> | ||
<id>http://refrain.cf/posts/8d55d49a/</id> | ||
<published>2023-01-17T06:44:22.000Z</published> | ||
<updated>2023-03-07T06:44:22.000Z</updated> | ||
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<summary type="html"><div class="tip home"><p>本篇文章将作为编程语言 <strong><span class='p red'>JAVA</span></strong> 的学习笔记,供以后查阅与复习使用。</p> | ||
</div> | ||
<div class="tip</summary> | ||
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<category term="Java" scheme="http://refrain.cf/categories/Java/"/> | ||
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<category term="Java" scheme="http://refrain.cf/tags/Java/"/> | ||
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<category term="编程学习" scheme="http://refrain.cf/tags/%E7%BC%96%E7%A8%8B%E5%AD%A6%E4%B9%A0/"/> | ||
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</entry> | ||
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<entry> | ||
<title>栈与队列</title> | ||
<link href="http://refrain.cf/posts/6534ce06/"/> | ||
<id>http://refrain.cf/posts/6534ce06/</id> | ||
<published>2022-12-31T06:18:56.000Z</published> | ||
<updated>2023-01-09T06:18:56.000Z</updated> | ||
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<summary type="html">本人书写的数据结构教程</summary> | ||
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<category term="数据结构" scheme="http://refrain.cf/categories/%E6%95%B0%E6%8D%AE%E7%BB%93%E6%9E%84/"/> | ||
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<category term="数据结构" scheme="http://refrain.cf/tags/%E6%95%B0%E6%8D%AE%E7%BB%93%E6%9E%84/"/> | ||
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<category term="栈" scheme="http://refrain.cf/tags/%E6%A0%88/"/> | ||
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<category term="队列" scheme="http://refrain.cf/tags/%E9%98%9F%E5%88%97/"/> | ||
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</entry> | ||
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<entry> | ||
<title>线性表</title> | ||
<link href="http://refrain.cf/posts/40f24371/"/> | ||
<id>http://refrain.cf/posts/40f24371/</id> | ||
<published>2022-12-24T13:23:10.000Z</published> | ||
<updated>2022-12-31T13:23:10.000Z</updated> | ||
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<summary type="html">本人书写的数据结构教程</summary> | ||
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||
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||
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||
<category term="数据结构" scheme="http://refrain.cf/categories/%E6%95%B0%E6%8D%AE%E7%BB%93%E6%9E%84/"/> | ||
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<category term="数据结构" scheme="http://refrain.cf/tags/%E6%95%B0%E6%8D%AE%E7%BB%93%E6%9E%84/"/> | ||
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<category term="线性表" scheme="http://refrain.cf/tags/%E7%BA%BF%E6%80%A7%E8%A1%A8/"/> | ||
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</entry> | ||
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<entry> | ||
<title>Win10/11任务栏透明美化</title> | ||
<link href="http://refrain.cf/posts/b67f488/"/> | ||
<id>http://refrain.cf/posts/b67f488/</id> | ||
<published>2022-12-02T12:18:18.000Z</published> | ||
<updated>2022-12-02T12:18:18.000Z</updated> | ||
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<summary type="html">win10/11任务栏美化教程</summary> | ||
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<category term="百宝箱" scheme="http://refrain.cf/categories/%E7%99%BE%E5%AE%9D%E7%AE%B1/"/> | ||
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<category term="Windows" scheme="http://refrain.cf/tags/Windows/"/> | ||
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<category term="任务栏" scheme="http://refrain.cf/tags/%E4%BB%BB%E5%8A%A1%E6%A0%8F/"/> | ||
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<category term="美化" scheme="http://refrain.cf/tags/%E7%BE%8E%E5%8C%96/"/> | ||
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</entry> | ||
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<entry> | ||
<title>数据结构绪论</title> | ||
<link href="http://refrain.cf/posts/b06cc6ec/"/> | ||
<id>http://refrain.cf/posts/b06cc6ec/</id> | ||
<published>2022-09-27T08:42:26.000Z</published> | ||
<updated>2022-12-12T08:42:26.000Z</updated> | ||
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<summary type="html">本人书写的数据结构教程</summary> | ||
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||
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||
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||
<category term="数据结构" scheme="http://refrain.cf/categories/%E6%95%B0%E6%8D%AE%E7%BB%93%E6%9E%84/"/> | ||
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<category term="数据结构" scheme="http://refrain.cf/tags/%E6%95%B0%E6%8D%AE%E7%BB%93%E6%9E%84/"/> | ||
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<category term="绪论" scheme="http://refrain.cf/tags/%E7%BB%AA%E8%AE%BA/"/> | ||
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</entry> | ||
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<entry> | ||
<title>机器学习基本概念与知识</title> | ||
<link href="http://refrain.cf/posts/75378e04/"/> | ||
<id>http://refrain.cf/posts/75378e04/</id> | ||
<published>2022-08-20T02:42:24.000Z</published> | ||
<updated>2022-08-20T02:42:24.000Z</updated> | ||
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<summary type="html">机器学习基础知识清单</summary> | ||
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<category term="机器学习" scheme="http://refrain.cf/categories/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0/"/> | ||
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<category term="机器学习" scheme="http://refrain.cf/tags/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0/"/> | ||
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||
</entry> | ||
|
||
<entry> | ||
<title>使用numpy实现k-means聚类算法</title> | ||
<link href="http://refrain.cf/posts/170bc017/"/> | ||
<id>http://refrain.cf/posts/170bc017/</id> | ||
<published>2022-08-17T01:03:20.000Z</published> | ||
<updated>2022-08-17T01:03:20.000Z</updated> | ||
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<summary type="html"><h2 id="k-means-算法基础原理"><a href="#k-means-算法基础原理" class="headerlink" title="k-means 算法基础原理"></a>k-means 算法基础原理</h2><blockquote> | ||
<p>本文注重对</summary> | ||
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<category term="机器学习" scheme="http://refrain.cf/categories/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0/"/> | ||
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<category term="Numpy" scheme="http://refrain.cf/tags/Numpy/"/> | ||
|
||
<category term="Python" scheme="http://refrain.cf/tags/Python/"/> | ||
|
||
<category term="k-means" scheme="http://refrain.cf/tags/k-means/"/> | ||
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</entry> | ||
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<entry> | ||
<title>使用Numpy实现k-Nearest-Neighbor算法</title> | ||
<link href="http://refrain.cf/posts/775eb342/"/> | ||
<id>http://refrain.cf/posts/775eb342/</id> | ||
<published>2022-08-14T06:08:58.000Z</published> | ||
<updated>2022-08-14T06:08:58.000Z</updated> | ||
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<summary type="html"><h2 id="KNN-算法基本原理"><a href="#KNN-算法基本原理" class="headerlink" title="KNN 算法基本原理"></a>KNN 算法基本原理</h2><p><strong>kNN 算法的核心思想是用距离最近的 k</summary> | ||
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<category term="机器学习" scheme="http://refrain.cf/categories/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0/"/> | ||
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<category term="机器学习" scheme="http://refrain.cf/tags/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0/"/> | ||
|
||
<category term="python" scheme="http://refrain.cf/tags/python/"/> | ||
|
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<category term="Numpy" scheme="http://refrain.cf/tags/Numpy/"/> | ||
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<category term="kNN算法" scheme="http://refrain.cf/tags/kNN%E7%AE%97%E6%B3%95/"/> | ||
|
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</entry> | ||
|
||
<entry> | ||
<title>Python实现决策树(Decision Tree)算法</title> | ||
<link href="http://refrain.cf/posts/54c08517/"/> | ||
<id>http://refrain.cf/posts/54c08517/</id> | ||
<published>2022-08-07T06:13:25.000Z</published> | ||
<updated>2022-08-07T06:13:25.000Z</updated> | ||
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<summary type="html"><h2 id="决策树算法基础原理分析"><a href="#决策树算法基础原理分析" class="headerlink" title="决策树算法基础原理分析"></a>决策树算法基础原理分析</h2><h3 id="信息的不纯度-决定决策树分支的指标"><a</summary> | ||
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<category term="机器学习" scheme="http://refrain.cf/categories/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0/"/> | ||
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<category term="机器学习" scheme="http://refrain.cf/tags/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0/"/> | ||
|
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<category term="python" scheme="http://refrain.cf/tags/python/"/> | ||
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||
<category term="决策树" scheme="http://refrain.cf/tags/%E5%86%B3%E7%AD%96%E6%A0%91/"/> | ||
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</entry> | ||
|
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<entry> | ||
<title>Numpy实现逻辑回归(Logistic Regression)算法</title> | ||
<link href="http://refrain.cf/posts/355699d5/"/> | ||
<id>http://refrain.cf/posts/355699d5/</id> | ||
<published>2022-08-06T09:20:58.000Z</published> | ||
<updated>2022-08-06T09:20:58.000Z</updated> | ||
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<summary type="html"><blockquote> | ||
<p>前言:本文旨在对如何使用 numpy 实现逻辑回归拟合的过程做具体分析,有关逻辑回归原理部分不做过多论述。</p> | ||
</blockquote> | ||
<h2 id="数据集准备"><a href="#数据集准备" class="headerlink"</summary> | ||
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|
||
<category term="机器学习" scheme="http://refrain.cf/categories/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0/"/> | ||
|
||
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||
<category term="机器学习" scheme="http://refrain.cf/tags/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0/"/> | ||
|
||
<category term="python" scheme="http://refrain.cf/tags/python/"/> | ||
|
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<category term="Numpy" scheme="http://refrain.cf/tags/Numpy/"/> | ||
|
||
<category term="逻辑回归" scheme="http://refrain.cf/tags/%E9%80%BB%E8%BE%91%E5%9B%9E%E5%BD%92/"/> | ||
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</entry> | ||
|
||
<entry> | ||
<title>线性回归 (Linear Regression)</title> | ||
<link href="http://refrain.cf/posts/20642/"/> | ||
<id>http://refrain.cf/posts/20642/</id> | ||
<published>2022-07-22T06:59:02.000Z</published> | ||
<updated>2022-07-22T06:59:02.000Z</updated> | ||
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||
|
||
<summary type="html"><h3 id="利用-Numpy-实现简单的机器学习算法"><a href="#利用-Numpy-实现简单的机器学习算法" class="headerlink" title="利用 Numpy 实现简单的机器学习算法"></a>利用 Numpy 实现简单的机器学习算法</h3><blockquote> | ||
<p>线性回归(Linear Regression) 可能是最流行的机器学习算法。线性回归就是要找一条直线,并且让这条直线尽可能地拟合散点图中的数据点。</p> | ||
</blockquote></summary> | ||
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<category term="机器学习" scheme="http://refrain.cf/categories/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0/"/> | ||
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||
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<category term="机器学习" scheme="http://refrain.cf/tags/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0/"/> | ||
|
||
<category term="python" scheme="http://refrain.cf/tags/python/"/> | ||
|
||
<category term="Numpy" scheme="http://refrain.cf/tags/Numpy/"/> | ||
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<category term="线性回归" scheme="http://refrain.cf/tags/%E7%BA%BF%E6%80%A7%E5%9B%9E%E5%BD%92/"/> | ||
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</entry> | ||
|
||
<entry> | ||
<title>关于cloudflare对网站搭建的使用</title> | ||
<link href="http://refrain.cf/posts/47341/"/> | ||
<id>http://refrain.cf/posts/47341/</id> | ||
<published>2022-07-18T10:54:13.000Z</published> | ||
<updated>2022-07-18T10:54:13.000Z</updated> | ||
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||
<summary type="html"><h2 id="为-hexo-博客添加自定义域名"><a href="#为-hexo-博客添加自定义域名" class="headerlink" title="为 hexo 博客添加自定义域名"></a>为 hexo 博客添加自定义域名</h2><blockquote> | ||
<p>域名注册方面不做赘述,下面详细讲述利用 cloudflare 中 DNS 解析域名至 hexo 博客站的过程</p> | ||
</blockquote></summary> | ||
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<category term="DNS" scheme="http://refrain.cf/categories/DNS/"/> | ||
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<category term="cloudflare" scheme="http://refrain.cf/tags/cloudflare/"/> | ||
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<category term="域名" scheme="http://refrain.cf/tags/%E5%9F%9F%E5%90%8D/"/> | ||
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</entry> | ||
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</feed> |
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