<?xml version="1.0" encoding="utf-8" ?><rss version="2.0"><channel><title><![CDATA[笔记本]]></title><description><![CDATA[]]></description><link>https://blog.youkuaiyun.com/Turinglife</link><language>zh-cn</language><generator>https://blog.youkuaiyun.com/</generator><copyright><![CDATA[Copyright &copy; Turinglife]]></copyright><item><title><![CDATA[什么是凸优化问题]]></title><link>https://blog.youkuaiyun.com/Turinglife/article/details/49307097</link><guid>https://blog.youkuaiyun.com/Turinglife/article/details/49307097</guid><author>Turinglife</author><pubDate>Wed, 21 Oct 2015 17:09:45 +0800</pubDate><description><![CDATA[https://zh.wikipedia.org/wiki/%E5%87%B8%E5%87%BD%E6%95%B0





数学中最优化问题的一般表述是求取，使，其中是n维向量，是的可行域，是上的实值函数。凸优化问题是指是闭合的凸集合且是上的凸函数的最优化问题，这两个条件任一不满足则该问题即为非凸的最优化问题。




其中，是凸集是指对集合中的任意两点，有，即任意两点的连]]></description><category></category></item><item><title><![CDATA[信号与槽的原理]]></title><link>https://blog.youkuaiyun.com/Turinglife/article/details/49255145</link><guid>https://blog.youkuaiyun.com/Turinglife/article/details/49255145</guid><author>Turinglife</author><pubDate>Mon, 19 Oct 2015 21:07:11 +0800</pubDate><description><![CDATA[信号与槽的原理
http://doc.qt.io/qt-4.8/signalsandslots.html




信号与槽机制(signal and slots)


1. 当一个特定事件发生时，QT的对象发出一个信号(signal)。
2. 槽(slot)就是一个C++函数，被定义来对特定信号进行功能响应。




-------------------------]]></description><category></category></item><item><title><![CDATA[Dahua Lin recommended math book]]></title><link>https://blog.youkuaiyun.com/Turinglife/article/details/41289029</link><guid>https://blog.youkuaiyun.com/Turinglife/article/details/41289029</guid><author>Turinglife</author><pubDate>Wed, 19 Nov 2014 21:34:11 +0800</pubDate><description><![CDATA[1. 线性代数 (Linear Algebra)：
我想国内的大学生都会学过这门课程，但是，未必每一位老师都能贯彻它的精要。这门学科对于Learning是必备的基础，对它的透彻掌握是必不可少的。我在科大一年级的时候就学习了这门课，后来到了香港后，又重新把线性代数读了一遍，所读的是
Introduction to Linear Algebra (3rd Ed.)  by Gilbert Stra]]></description><category></category></item><item><title><![CDATA[概率模型与计算机视觉]]></title><link>https://blog.youkuaiyun.com/Turinglife/article/details/41288987</link><guid>https://blog.youkuaiyun.com/Turinglife/article/details/41288987</guid><author>Turinglife</author><pubDate>Wed, 19 Nov 2014 21:17:18 +0800</pubDate><description><![CDATA[“概率模型与计算机视觉”

林达华

美国麻省理工学院（MIT）博士

上世纪60年代, Marvin Minsky 在MIT让他的本科学生 Gerald
 Jay Sussman用一个暑假的时间完成一个有趣的Project: “link a camera to a computer and get the computer to describe what it saw”。从那时开]]></description><category></category></item><item><title><![CDATA[CNN相关资料]]></title><link>https://blog.youkuaiyun.com/Turinglife/article/details/38149787</link><guid>https://blog.youkuaiyun.com/Turinglife/article/details/38149787</guid><author>Turinglife</author><pubDate>Sat, 26 Jul 2014 20:32:59 +0800</pubDate><description><![CDATA[http://www.cnblogs.com/tornadomeet/archive/2013/05/05/3061457.html]]></description><category></category></item><item><title><![CDATA[CPP Summary]]></title><link>https://blog.youkuaiyun.com/Turinglife/article/details/30458235</link><guid>https://blog.youkuaiyun.com/Turinglife/article/details/30458235</guid><author>Turinglife</author><pubDate>Fri, 13 Jun 2014 09:39:15 +0800</pubDate><description><![CDATA[【C++ FAQ]
http://www.parashift.com/c++-faq/mentoring.html]]></description><category></category></item><item><title><![CDATA[Interview Summary]]></title><link>https://blog.youkuaiyun.com/Turinglife/article/details/30028055</link><guid>https://blog.youkuaiyun.com/Turinglife/article/details/30028055</guid><author>Turinglife</author><pubDate>Wed, 11 Jun 2014 09:26:31 +0800</pubDate><description><![CDATA[http://www.dreambigcareer.com/#!Amazon-eBay-Facebook-Google-LinkedIn-/c19re/060EF7D6-F699-4BE0-A3B8-2B403ED95CBF]]></description><category></category></item><item><title><![CDATA[JDK1.5 泛型]]></title><link>https://blog.youkuaiyun.com/Turinglife/article/details/28188079</link><guid>https://blog.youkuaiyun.com/Turinglife/article/details/28188079</guid><author>Turinglife</author><pubDate>Tue, 03 Jun 2014 01:00:18 +0800</pubDate><description><![CDATA[http://blog.youkuaiyun.com/turkeyzhou/article/details/2888774]]></description><category></category></item><item><title><![CDATA[JAVA 抽象类和接口]]></title><link>https://blog.youkuaiyun.com/Turinglife/article/details/28154449</link><guid>https://blog.youkuaiyun.com/Turinglife/article/details/28154449</guid><author>Turinglife</author><pubDate>Mon, 02 Jun 2014 22:54:19 +0800</pubDate><description><![CDATA[http://blog.youkuaiyun.com/chenssy/article/details/12858267]]></description><category></category></item><item><title><![CDATA[Spark分析]]></title><link>https://blog.youkuaiyun.com/Turinglife/article/details/27638919</link><guid>https://blog.youkuaiyun.com/Turinglife/article/details/27638919</guid><author>Turinglife</author><pubDate>Fri, 30 May 2014 08:59:57 +0800</pubDate><description><![CDATA[http://www.youkuaiyun.com/article/2014-05-29/2820013]]></description><category></category></item><item><title><![CDATA[Realtime Data Processing]]></title><link>https://blog.youkuaiyun.com/Turinglife/article/details/27497647</link><guid>https://blog.youkuaiyun.com/Turinglife/article/details/27497647</guid><author>Turinglife</author><pubDate>Thu, 29 May 2014 10:50:08 +0800</pubDate><description><![CDATA[http://highlyscalable.wordpress.com/2013/08/20/in-stream-big-data-processing/]]></description><category></category></item><item><title><![CDATA[MOOC Collection]]></title><link>https://blog.youkuaiyun.com/Turinglife/article/details/27325873</link><guid>https://blog.youkuaiyun.com/Turinglife/article/details/27325873</guid><author>Turinglife</author><pubDate>Wed, 28 May 2014 12:09:13 +0800</pubDate><description><![CDATA[[Data Structure]
http://mooc.guokr.com/course/606/%E6%95%B0%E6%8D%AE%E7%BB%93%E6%9E%84%EF%BC%88%E4%B8%8A%EF%BC%89/]]></description><category></category></item><item><title><![CDATA[Big Data Science Collection]]></title><link>https://blog.youkuaiyun.com/Turinglife/article/details/27325819</link><guid>https://blog.youkuaiyun.com/Turinglife/article/details/27325819</guid><author>Turinglife</author><pubDate>Wed, 28 May 2014 12:07:52 +0800</pubDate><description><![CDATA[http://www.52ml.net/categories/machinelearning]]></description><category></category></item><item><title><![CDATA[CPP STL]]></title><link>https://blog.youkuaiyun.com/Turinglife/article/details/26113685</link><guid>https://blog.youkuaiyun.com/Turinglife/article/details/26113685</guid><author>Turinglife</author><pubDate>Sun, 18 May 2014 01:43:12 +0800</pubDate><description><![CDATA[http://www.cs.wustl.edu/~schmidt/PDF/stl4.pdf]]></description><category></category></item><item><title><![CDATA[deep learning reading list]]></title><link>https://blog.youkuaiyun.com/Turinglife/article/details/26058631</link><guid>https://blog.youkuaiyun.com/Turinglife/article/details/26058631</guid><author>Turinglife</author><pubDate>Sat, 17 May 2014 11:32:50 +0800</pubDate><description><![CDATA[http://www.idsia.ch/~juergen/DeepLearning15May2014.pdf]]></description><category></category></item><item><title><![CDATA[google page rank 解释]]></title><link>https://blog.youkuaiyun.com/Turinglife/article/details/26058515</link><guid>https://blog.youkuaiyun.com/Turinglife/article/details/26058515</guid><author>Turinglife</author><pubDate>Sat, 17 May 2014 11:30:53 +0800</pubDate><description><![CDATA[http://www.math.cornell.edu/~mec/Winter2009/RalucaRemus/Lecture3/lecture3.html]]></description><category></category></item><item><title><![CDATA[summary of big data science terms]]></title><link>https://blog.youkuaiyun.com/Turinglife/article/details/25789771</link><guid>https://blog.youkuaiyun.com/Turinglife/article/details/25789771</guid><author>Turinglife</author><pubDate>Wed, 14 May 2014 12:36:10 +0800</pubDate><description><![CDATA[SGD: stochastic gradient descent]]></description><category></category></item><item><title><![CDATA[Improving Word Representations via Global Context and MultipleWord Prototypes]]></title><link>https://blog.youkuaiyun.com/Turinglife/article/details/25789633</link><guid>https://blog.youkuaiyun.com/Turinglife/article/details/25789633</guid><author>Turinglife</author><pubDate>Wed, 14 May 2014 12:30:23 +0800</pubDate><description><![CDATA[Andrew Y. Ng 他们在"Improving Word Representations via Global Context and MultipleWord Prototypes"训练词向量时添加了全局信息并处理了多义词。通过文档里所有词向量的加权和表示文档，通过词所在的环境对多义词进行区分和重表示。文中的提到的训练技巧：使用mini-batch L-BFGS 效果优于SGD。

]]></description><category></category></item><item><title><![CDATA[surveys on big data science]]></title><link>https://blog.youkuaiyun.com/Turinglife/article/details/25789469</link><guid>https://blog.youkuaiyun.com/Turinglife/article/details/25789469</guid><author>Turinglife</author><pubDate>Wed, 14 May 2014 12:26:40 +0800</pubDate><description><![CDATA[http://www.mlsurveys.com/]]></description><category></category></item><item><title><![CDATA[如何成为一名数据科学家？]]></title><link>https://blog.youkuaiyun.com/Turinglife/article/details/25788711</link><guid>https://blog.youkuaiyun.com/Turinglife/article/details/25788711</guid><author>Turinglife</author><pubDate>Wed, 14 May 2014 12:07:49 +0800</pubDate><description><![CDATA["Data Science = statistics who uses python and lives in San Francisco"

恰好我马上启程到Twitter的data science team，而且恰巧懂一点点统计和住在旧金山，所以冲动地没有邀请就厚脸回答了:D

我认为有几个大方面

1）学好python。
现在几乎所以公司的数据都可以api给你，而python的]]></description><category></category></item></channel></rss>