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algorithm.html
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<?xml version="1.0" encoding="utf-8"?>
<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Strict//EN"
"http://www.w3.org/TR/xhtml1/DTD/xhtml1-strict.dtd">
<html xmlns="http://www.w3.org/1999/xhtml" lang="en" xml:lang="en">
<head>
<meta http-equiv="Content-Type" content="text/html;charset=utf-8" />
<meta name="viewport" content="width=device-width, initial-scale=1" />
<title>About Algorithm</title>
<meta name="generator" content="Org-mode" />
<meta name="author" content="dirtysalt" />
<link rel="shortcut icon" href="http://dirtysalt.info/css/favicon.ico" />
<link rel="stylesheet" type="text/css" href="./css/site.css" />
</head>
<body>
<div id="content">
<h1 class="title">About Algorithm</h1>
<ul class="org-ul">
<li><a href="algorithm-note.html">Algorithm Notes</a>
<ul class="org-ul">
<li><a href="./cracking-the-coding-interview.html">Cracking The Coding Interview</a> 据说算法分为三种：面试算法，ACM算法，算法=D</li>
<li><a href="./do-you-think-you-have-gambling-problem.html">Do You Think You Have a Gambling Problem?</a> 赌博其实是概率问题</li>
<li><a href="./probabilistic-data-structures-for-web-analytics-and-data-mining.html">Probabilistic Data Structures for Web Analytics and Data Mining</a> 用于Web分析和数据挖掘的概率化数据结构</li>
</ul></li>
<li><a href="./compression.html">Compression</a> 压缩技术
<ul class="org-ul">
<li><a href="snappy.html">snappy</a> Google的开源压缩解压库。在满足一定压缩比率的条件下着重提升压缩和解压速度。</li>
<li><a href="lzf.html">lzf</a> <a href="redis.html">redis</a> 使用的开源压缩解压库。轻量(两个文件)可以很容易地独立纳入项目。</li>
<li><a href="lzma.html">lzma</a> Lempel-Ziv-Markov chain-Algorithm ，压缩速度相对较慢但是压缩比超高。</li>
</ul></li>
<li><a href="./machine-learning.html">Machine Learning</a> 机器学习
<ul class="org-ul">
<li><a href="./sklearn.html">sklearn</a> python scikit learn. 机器学习包.</li>
<li><a href="caffe.html">caffe</a> C++实现的深度学习框架，有python和matlab的扩展接口</li>
<li><a href="nolearn.html">nolearn</a> scikit-learn compatibile wrapper for neural nets. 底层可以使用不同的NN实现比如 <a href="./caffe.html">caffe</a>, <a href="https://github.com/Lasagne/Lasagne">lasagne</a>.</li>
<li><a href="http://www.autonlab.org/tutorials/list.html">Statistical Data Mining Tutorials</a> by <a href="http://www.cs.cmu.edu/~awm/">Andrew W. Moore</a></li>
<li>Coursera: Machine Learning by Andrew Ng. <a href="ml-class.html">笔记和一些习题代码</a> (仅供学习) 以及 <a href="images/coursera-ml-2014.pdf">证书</a></li>
<li><a href="./ml-the-hard-way.html">Machine Learning the Hard Way</a> 哥们用ml来赌马，虽然最后赚钱了，但是结果却特别悲惨:)</li>
<li><a href="./deconstructing-recommender-systems.html">Deconstructing Recommender Systems</a> 关于推荐系统的介绍性文章</li>
<li><a href="./beauty-of-math.html">数学之美</a> wujun</li>
<li><a href="./ml-foundations.html">机器学习基石 on Coursera</a></li>
<li><a href="./ml-techniques.html">机器学习技法 on Coursera</a></li>
<li><a href="./neuralnets.html">Neural Networks for Machine Learning on Coursera</a></li>
<li><a href="./mmds.html">Mining Massive Datasets on Coursera</a> 海量数据挖掘课程</li>
</ul></li>
<li><a href="computational-advertising.html">Computational Advertising</a> 计算广告</li>
<li><a href="./bitcoin.html">Bitcoin: A Peer-to-Peer Electronic Cash System</a> 比特币论文</li>
</ul>
</div>
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