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602 lines
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5 years ago
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<!DOCTYPE HTML>
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<title>简介 · GitBook</title>
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<li class="chapter " data-level="1.1" data-path="../">
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<a href="../">
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简介
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</a>
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5 years ago
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<li class="chapter " data-level="1.2" data-path="../machine_learning.html">
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<a href="../machine_learning.html">
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机器学习概述
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</a>
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<li class="chapter " data-level="1.3" data-path="../algorithm.html">
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<a href="../algorithm.html">
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常见机器学习算法
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</a>
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<ul class="articles">
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<li class="chapter " data-level="1.3.1" data-path="../kNN.html">
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<a href="../kNN.html">
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近朱者赤近墨者黑-kNN
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</a>
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</li>
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<li class="chapter " data-level="1.3.2" data-path="../linear_regression.html">
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<a href="../linear_regression.html">
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最简单的回归算法-线性回归
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</a>
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</li>
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<li class="chapter " data-level="1.3.3" data-path="../logistic_regression.html">
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<a href="../logistic_regression.html">
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使用回归的思想进行分类-逻辑回归
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</a>
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</li>
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<li class="chapter " data-level="1.3.4" data-path="../decision_tree.html">
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<a href="../decision_tree.html">
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最接近人类思维的算法-决策树
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</a>
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</li>
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<li class="chapter " data-level="1.3.5" data-path="../random_forest.html">
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<a href="../random_forest.html">
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群众的力量是伟大的-随机森林
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</a>
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</li>
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<li class="chapter " data-level="1.3.6" data-path="../kMeans.html">
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<a href="../kMeans.html">
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物以类聚人以群分-kMeans
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</a>
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</li>
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<li class="chapter " data-level="1.3.7" data-path="../AGNES.html">
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<a href="../AGNES.html">
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以距离为尺-AGNES
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</a>
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</li>
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</ul>
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</li>
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<li class="chapter " data-level="1.4" data-path="../metrics.html">
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<a href="../metrics.html">
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模型评估指标
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</a>
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<ul class="articles">
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<li class="chapter " data-level="1.4.1" data-path="../classification_metrics.html">
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<a href="../classification_metrics.html">
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分类性能评估指标
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</a>
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</li>
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<li class="chapter " data-level="1.4.2" data-path="../regression_metrics.html">
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<a href="../regression_metrics.html">
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回归性能评估指标
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</a>
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</li>
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<li class="chapter " data-level="1.4.3" data-path="../cluster_metrics.html">
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<a href="../cluster_metrics.html">
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聚类性能评估指标
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</a>
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</li>
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</ul>
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</li>
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<li class="chapter " data-level="1.5" data-path="../sklearn.html">
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<a href="../sklearn.html">
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使用sklearn进行机器学习
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</a>
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</li>
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<li class="chapter " data-level="1.6" >
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<span>
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综合实战案例
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</span>
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<ul class="articles">
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<li class="chapter " data-level="1.6.1" >
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5 years ago
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<span>
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泰坦尼克生还预测
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</span>
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<ul class="articles">
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5 years ago
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<li class="chapter active" data-level="1.6.1.1" data-path="introduction.html">
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5 years ago
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<a href="introduction.html">
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简介
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</a>
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</li>
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5 years ago
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<li class="chapter " data-level="1.6.1.2" data-path="EDA.html">
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5 years ago
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<a href="EDA.html">
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探索性数据分析(EDA)
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</a>
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</li>
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5 years ago
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<li class="chapter " data-level="1.6.1.3" data-path="feature engerning.html">
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5 years ago
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<a href="feature engerning.html">
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特征工程
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</a>
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</li>
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5 years ago
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<li class="chapter " data-level="1.6.1.4" data-path="fit and predict.html">
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5 years ago
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<a href="fit and predict.html">
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构建模型进行预测
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</a>
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</li>
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5 years ago
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<li class="chapter " data-level="1.6.1.5" data-path="tuning.html">
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5 years ago
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<a href="tuning.html">
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调参
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</a>
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</li>
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</ul>
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</li>
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5 years ago
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<li class="chapter " data-level="1.6.2" >
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5 years ago
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<span>
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使用强化学习玩乒乓球游戏
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</span>
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<ul class="articles">
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5 years ago
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<li class="chapter " data-level="1.6.2.1" data-path="../pingpong/what is reinforce learning.html">
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5 years ago
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<a href="../pingpong/what is reinforce learning.html">
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什么是强化学习
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</a>
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</li>
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5 years ago
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<li class="chapter " data-level="1.6.2.2" data-path="../pingpong/Policy Gradient.html">
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5 years ago
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<a href="../pingpong/Policy Gradient.html">
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Policy Gradient原理
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</a>
|
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</li>
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5 years ago
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<li class="chapter " data-level="1.6.2.3" data-path="../pingpong/coding.html">
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5 years ago
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<a href="../pingpong/coding.html">
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使用Policy Gradient玩乒乓球游戏
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</a>
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</li>
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</ul>
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</li>
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5 years ago
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</ul>
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</li>
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<li class="chapter " data-level="1.7" data-path="../recommand.html">
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<a href="../recommand.html">
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实训推荐
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</a>
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</li>
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5 years ago
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<li class="divider"></li>
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<li>
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Published with GitBook
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<h1>
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<a href=".." >简介</a>
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<h1 id="写在前面的话">写在前面的话</h1>
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<p>怎样处理数据，使用什么样的机器学习模型并没有所谓的正确答案。这篇文章只是抛砖引玉，若您是刚刚接触数据科学，我相信这一篇不错的指引；若您已经是老手，我相信文中的一些技巧您肯定也用过，可以温故而知新；所以希望这篇文章对您或多或少的有所帮助。</p>
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<h1 id="泰坦尼克生还问题简介">泰坦尼克生还问题简介</h1>
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<p>泰坦尼克号的沉船事件是是历史上最臭名昭著的沉船事件之一。1912年4月15日，泰坦尼克在航线中与冰山相撞，2224 名乘客中有 1502 名乘客丧生。</p>
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<p>泰坦尼克号数据集是目标是给出一个模型来预测某位泰坦尼克号的乘客在沉船事件中是生还是死。而且该数据集是一个非常好的数据集，能够让您快速的开始数据科学之旅。</p>
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<a href="EDA.html" class="navigation navigation-next navigation-unique" aria-label="Next page: 探索性数据分析(EDA)">
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var gitbook = gitbook || [];
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gitbook.page.hasChanged({"page":{"title":"简介","level":"1.6.1.1","depth":3,"next":{"title":"探索性数据分析(EDA)","level":"1.6.1.2","depth":3,"path":"titanic/EDA.md","ref":"./titanic/EDA.md","articles":[]},"previous":{"title":"泰坦尼克生还预测","level":"1.6.1","depth":2,"ref":"","articles":[{"title":"简介","level":"1.6.1.1","depth":3,"path":"titanic/introduction.md","ref":"./titanic/introduction.md","articles":[]},{"title":"探索性数据分析(EDA)","level":"1.6.1.2","depth":3,"path":"titanic/EDA.md","ref":"./titanic/EDA.md","articles":[]},{"title":"特征工程","level":"1.6.1.3","depth":3,"path":"titanic/feature engerning.md","ref":"./titanic/feature engerning.md","articles":[]},{"title":"构建模型进行预测","level":"1.6.1.4","depth":3,"path":"titanic/fit and predict.md","ref":"./titanic/fit and predict.md","articles":[]},{"title":"调参","level":"1.6.1.5","depth":3,"path":"titanic/tuning.md","ref":"./titanic/tuning.md","articles":[]}]},"dir":"ltr"},"config":{"gitbook":"*","theme":"default","variables":{},"plugins":["katex"],"pluginsConfig":{"katex":{},"highlight":{},"search":{},"lunr":{"maxIndexSize":1000000,"ignoreSpecialCharacters":false},"sharing":{"facebook":true,"twitter":true,"google":false,"weibo":false,"instapaper":false,"vk":false,"all":["facebook","google","twitter","weibo","instapaper"]},"fontsettings":{"theme":"white","family":"sans","size":2},"theme-default":{"styles":{"website":"styles/website.css","pdf":"styles/pdf.css","epub":"styles/epub.css","mobi":"styles/mobi.css","ebook":"styles/ebook.css","print":"styles/print.css"},"showLevel":false}},"structure":{"langs":"LANGS.md","readme":"README.md","glossary":"GLOSSARY.md","summary":"SUMMARY.md"},"pdf":{"pageNumbers":true,"fontSize":12,"fontFamily":"Arial","paperSize":"a4","chapterMark":"pagebreak","pageBreaksBefore":"/","margin":{"right":62,"left":62,"top":56,"bottom":56}},"styles":{"website":"styles/website.css","pdf":"styles/pdf.css","epub":"styles/epub.css","mobi":"styles/mobi.css","ebook":"styles/ebook.css","print":"styles/print.css"}},"file":{"path":"titanic/introduction.md","mtime":"2019-07-05T01:18:54.983Z","type":"markdown"},"gitbook":{"version":"3.2.3","time":"2019-07-06T07:31:21.537Z"},"basePath":"..","book":{"language":""}});
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