<?xml version="1.0" encoding="utf-8" ?><rss version="2.0"><channel><title><![CDATA[Baoli1008的专栏]]></title><description><![CDATA[Rush]]></description><link>https://blog.youkuaiyun.com/Baoli1008</link><language>zh-cn</language><generator>https://blog.youkuaiyun.com/</generator><copyright><![CDATA[Copyright &copy; Baoli1008]]></copyright><item><title><![CDATA[TBB使用教程]]></title><link>https://blog.youkuaiyun.com/Baoli1008/article/details/62225689</link><guid>https://blog.youkuaiyun.com/Baoli1008/article/details/62225689</guid><author>Baoli1008</author><pubDate>Wed, 15 Mar 2017 15:09:33 +0800</pubDate><description><![CDATA[官方tutorial https://software.intel.com/en-us/tbb-user-guide1.parallel_for：常规方式(非lambda函数)：1.1串行版本：void SerialApplyFoo( float a[], size_t n ) {
    for( size_t i=0; i!=n; ++i )
        Foo(a[i]);
}并行版本：首]]></description><category></category></item><item><title><![CDATA[C/C++中函数调用规则（约定）__cdecl __stdcall __thiscall __vectorcall __fastcall __clrcall]]></title><link>https://blog.youkuaiyun.com/Baoli1008/article/details/54890725</link><guid>https://blog.youkuaiyun.com/Baoli1008/article/details/54890725</guid><author>Baoli1008</author><pubDate>Mon, 06 Feb 2017 10:54:20 +0800</pubDate><description><![CDATA[转自https://my.oschina.net/ray1421/blog/699540


相关文献
__stdcall
https://msdn.microsoft.com/en-us/library/zxk0tw93.aspx
C语言函数可变参数详解 - ranpanf的专栏 - 博客频道 - youkuaiyun.com
http://blog.youkuaiyun.com/ranpanf/arti]]></description><category></category></item><item><title><![CDATA[数据分析框架Pandas进阶]]></title><link>https://blog.youkuaiyun.com/Baoli1008/article/details/51382277</link><guid>https://blog.youkuaiyun.com/Baoli1008/article/details/51382277</guid><author>Baoli1008</author><pubDate>Thu, 12 May 2016 10:54:40 +0800</pubDate><description><![CDATA[广播运算多维数据和一维数据进行运算称为广播，会自动按标签匹配对每个元素执行运算。In [13]: frm
Out[13]:
   0  1  2
a  0  1  2
b  3  4  5
c  6  7  8
In [16]: sr2 = Series([3,2,4],index = range(3))In [17]: frm + sr2
Out[17]:
   0  1   2
a  3  3]]></description><category></category></item><item><title><![CDATA[数据分析框架Pandas入门]]></title><link>https://blog.youkuaiyun.com/Baoli1008/article/details/51356687</link><guid>https://blog.youkuaiyun.com/Baoli1008/article/details/51356687</guid><author>Baoli1008</author><pubDate>Mon, 09 May 2016 23:11:21 +0800</pubDate><description><![CDATA[数据结构：SeriesSeries是一种类似一维数组的数据结构，输出时会同时显示索引和值In [6]: a = Series([3,5,2])
In [7]: a
Out[7]:
0    3
1    5
2    2
dtype: int64分别得到索引和值In [10]: a.values
Out[10]: array([3, 5, 2], dtype=int64)In [11]: a.ind]]></description><category></category></item><item><title><![CDATA[Github入门教程]]></title><link>https://blog.youkuaiyun.com/Baoli1008/article/details/51089720</link><guid>https://blog.youkuaiyun.com/Baoli1008/article/details/51089720</guid><author>Baoli1008</author><pubDate>Thu, 07 Apr 2016 21:38:38 +0800</pubDate><description><![CDATA[0.安装mac和Linux默认预装git 
windows：https://git-for-windows.github.io/ 
安装过程中仔细阅读安装流程，普通用户选择默认选项即可。1.初始设置输入命令：  git config --global user.email "you@example.com"
  git config --global user.name "Your Name"字符串]]></description><category></category></item><item><title><![CDATA[Stanford 机器学习笔记 Week11 Application Example: Photo OCR]]></title><link>https://blog.youkuaiyun.com/Baoli1008/article/details/51088833</link><guid>https://blog.youkuaiyun.com/Baoli1008/article/details/51088833</guid><author>Baoli1008</author><pubDate>Thu, 07 Apr 2016 19:54:46 +0800</pubDate><description><![CDATA[Photo OCRProblem Description and PipelineOCR： Optical  Character Recognition 
步骤： 
1.识别包含文字的图片区域 
2.将每块图片中的各个文字分隔开 
3.对每个文字进行识别这种算法流程被称为pipeline，前一个模块的输出是下一个模块的输入。Sliding Windows本节分别介绍一下上述三个步骤的实现过程：1.T]]></description><category></category></item><item><title><![CDATA[Stanford 机器学习笔记 Week10 Large Scale Machine Learning]]></title><link>https://blog.youkuaiyun.com/Baoli1008/article/details/51014403</link><guid>https://blog.youkuaiyun.com/Baoli1008/article/details/51014403</guid><author>Baoli1008</author><pubDate>Wed, 30 Mar 2016 14:46:58 +0800</pubDate><description><![CDATA[Gradient Descent with Large DatasetsLearning With Large Datasets在处理海量数据时对算法会有更高的要求。比如在计算偏导数时，当m很大时对m个元素求和的开销会很大。因此在将算法应用于海量数据时最好先确定算法没有high-bias，方法就是绘制leaning curve，左图是high-varience的，增大数据量会改善算法表现，而右图hi]]></description><category></category></item><item><title><![CDATA[Stanford 机器学习笔记 Week9 Recommender Systems]]></title><link>https://blog.youkuaiyun.com/Baoli1008/article/details/50989593</link><guid>https://blog.youkuaiyun.com/Baoli1008/article/details/50989593</guid><author>Baoli1008</author><pubDate>Sat, 26 Mar 2016 23:35:33 +0800</pubDate><description><![CDATA[Predicting Movie RatingsProblem Formulation推荐系统举例： 
 
根据各个用户的打分记录，预测某用户对一没看过的电影的打分情况。Content Based Recommendations解决上一节问题的一个方法： 
对于每个电影抽象出2个属性，romance 和 action，并手动赋值。再加上bias unit x0 = 1，就构成了表示每个电影属性的向量]]></description><category></category></item><item><title><![CDATA[为什么logistic regression要使用sigmoid函数]]></title><link>https://blog.youkuaiyun.com/Baoli1008/article/details/50964715</link><guid>https://blog.youkuaiyun.com/Baoli1008/article/details/50964715</guid><author>Baoli1008</author><pubDate>Wed, 23 Mar 2016 16:58:27 +0800</pubDate><description><![CDATA[转自：JUN’S TECH BLOG 
http://leijun00.github.io/2014/08/logistic-regression/我们知道,线性回归模型输出的是一个连续值,如果我们要输出的不是连续值,该怎么做呢?假设我们的输出只有 1 和 -1. 
逻辑回归模型形式上是把线性回归模型做一个变换,让其输出是一个 0 到 1 之间的数,假设我们的变换叫做 g(z)g(z),然后在变换后]]></description><category></category></item><item><title><![CDATA[Stanford 机器学习笔记 Week9 Anomaly Detection]]></title><link>https://blog.youkuaiyun.com/Baoli1008/article/details/50956233</link><guid>https://blog.youkuaiyun.com/Baoli1008/article/details/50956233</guid><author>Baoli1008</author><pubDate>Tue, 22 Mar 2016 16:42:49 +0800</pubDate><description><![CDATA[Density EstimationProblem Motivation这个算法的目的是发现一个training set中的不规则点。给定一个training set ，如果我们能设计一个模型p，使得p(test) 等于该点是常规点的概率。那么我们就能通过判断p(test)< ε 来确定test是一个不规则点。 
显然越接近原training set的中心p(test)越大。该算法可用于质量检测等许]]></description><category></category></item><item><title><![CDATA[Stanford 机器学习 Week8 作业:K-means Clustering and Principal Component Analysis]]></title><link>https://blog.youkuaiyun.com/Baoli1008/article/details/50939596</link><guid>https://blog.youkuaiyun.com/Baoli1008/article/details/50939596</guid><author>Baoli1008</author><pubDate>Sun, 20 Mar 2016 23:22:41 +0800</pubDate><description><![CDATA[FindClosestCentroidsfor i = 1:size(X,1)
    dis = sum((centroids - X(i,:)) .^ 2, 2);
    [t, idx(i)] = min(dis);
endComputeCentroidsfor i = 1:K
    id = find(idx == i);
    tot = X(id,:);
    centroids]]></description><category></category></item><item><title><![CDATA[Stanford 机器学习笔记 Week8 Dimensionality Reduction]]></title><link>https://blog.youkuaiyun.com/Baoli1008/article/details/50933791</link><guid>https://blog.youkuaiyun.com/Baoli1008/article/details/50933791</guid><author>Baoli1008</author><pubDate>Sat, 19 Mar 2016 23:16:33 +0800</pubDate><description><![CDATA[MotivationMotivation I: Data Compression降维可以做数据压缩，减少冗余信息从而减小存储空间。2D向1D降维： 
 
cm 和 inches都表示长度，属于冗余信息，可以用z向量做新的维度，用1维就可以表示长度。3D向2D降维： 
 
在左侧的原始数据中，所有点都接近一个平面，这说明存在冗余维度。将所有点投影到该平面形成中间图。用向量z1，z2做新的维度，降到2维]]></description><category></category></item><item><title><![CDATA[Stanford 机器学习笔记 Week8 Unsupervised Learning]]></title><link>https://blog.youkuaiyun.com/Baoli1008/article/details/50932736</link><guid>https://blog.youkuaiyun.com/Baoli1008/article/details/50932736</guid><author>Baoli1008</author><pubDate>Sat, 19 Mar 2016 21:12:03 +0800</pubDate><description><![CDATA[ClusteringK-Means Algorithm一种经典的聚类算法，步骤很简单，分4步：1.首先随机选择K个聚类中心 
2.对于Training Set中的每个点i，计算离i最近的中心c(i)，将该点标记为c(i) 
3.对于每个中心k，重新计算该位置为：所有标记为k的点的平均位置 
4.如果所有中心的位置都不变，算法结束。否则回到步骤2当在第2步时发现对于某中心没有属于的点，可以直接将该中心]]></description><category></category></item><item><title><![CDATA[Stanford 机器学习笔记 Week7 Support Vector Machines]]></title><link>https://blog.youkuaiyun.com/Baoli1008/article/details/50853776</link><guid>https://blog.youkuaiyun.com/Baoli1008/article/details/50853776</guid><author>Baoli1008</author><pubDate>Fri, 11 Mar 2016 10:19:59 +0800</pubDate><description><![CDATA[Large Margin ClassificationOptimization Objective在logistic回归中，cost function使用了sigmoid函数，从而将θTx的值映射到(0 , 1)范围内。 在SVM中提出了另一种函数cost来替代sigmoid函数，如图： 
上图中曲线部分是sigmoid函数，而两段线段组成的部分就是新提出的cost函数，它也满足值域在（0，1）内并]]></description><category></category></item><item><title><![CDATA[Stanford 机器学习笔记 Week6 Machine Learning System Design]]></title><link>https://blog.youkuaiyun.com/Baoli1008/article/details/50808780</link><guid>https://blog.youkuaiyun.com/Baoli1008/article/details/50808780</guid><author>Baoli1008</author><pubDate>Sat, 05 Mar 2016 14:36:24 +0800</pubDate><description><![CDATA[Building a Spam ClassifierPrioritizing What to Work On本节将会设计一个垃圾邮件分类器，具体方法是这样的：首先设定一些经常在垃圾邮件中出现的单词，比如buy, discount, deal等等，假如有m个。然后对于一封邮件，用一个长度为m的0/1向量表示这些单词是否在这封邮件中出现过。一般来说，这些单词不是人工设定的，而是从训练集中选择出现频率最高]]></description><category></category></item><item><title><![CDATA[Stanford 机器学习 Week6 作业:Regularized Linear Regression and Bias v.s. Variance]]></title><link>https://blog.youkuaiyun.com/Baoli1008/article/details/50801691</link><guid>https://blog.youkuaiyun.com/Baoli1008/article/details/50801691</guid><author>Baoli1008</author><pubDate>Fri, 04 Mar 2016 12:28:16 +0800</pubDate><description><![CDATA[linearRegCostfunctionm = length(y); 
J = 0;
grad = zeros(size(theta));J = 1.0 / 2 / m * ( sum( (X * theta - y) .^ 2) + lambda * sum(theta(2:end) .^2) );grad = 1 / m * ((X * theta - y)' * X)';
grad(2:en]]></description><category></category></item><item><title><![CDATA[埃氏筛法和欧拉筛法的区别]]></title><link>https://blog.youkuaiyun.com/Baoli1008/article/details/50788512</link><guid>https://blog.youkuaiyun.com/Baoli1008/article/details/50788512</guid><author>Baoli1008</author><pubDate>Thu, 03 Mar 2016 14:56:19 +0800</pubDate><description><![CDATA[转自：
点击打开链接





Eratosthenes筛法（Sieve of Eratosthenes）


由于思想非常简单，故只给出实现。





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void eratosthenes_sieve(int n)
{
    totPrimes = 0;
]]></description><category></category></item><item><title><![CDATA[Stanford 机器学习笔记 Week6 Advice for Applying Machine Learning]]></title><link>https://blog.youkuaiyun.com/Baoli1008/article/details/50782945</link><guid>https://blog.youkuaiyun.com/Baoli1008/article/details/50782945</guid><author>Baoli1008</author><pubDate>Wed, 02 Mar 2016 21:14:40 +0800</pubDate><description><![CDATA[Evaluating a Learning AlgorithmDeciding What to Try Next当算法效果不好时，你可能会尝试：1. 收集更多数据 2. 增加属性 3.减少属性 4.修改regularition参数lambda,下面几节会告诉你如何选择正确的做法。Evaluating a Hypothesis如何判定是否过拟合了？一种方法是拿出原训练集的70%做新训练集，计算出θ，然]]></description><category></category></item><item><title><![CDATA[Stanford 机器学习 Week5 作业: Neural Networks: Learning]]></title><link>https://blog.youkuaiyun.com/Baoli1008/article/details/50766242</link><guid>https://blog.youkuaiyun.com/Baoli1008/article/details/50766242</guid><author>Baoli1008</author><pubDate>Mon, 29 Feb 2016 16:11:52 +0800</pubDate><description><![CDATA[randInitializeWeightepsilon_init = 0.12;
W = rand(L_out, 1 + L_in) * 2 * epsilon_init - epsilon_init;sigmoidGradientg = sigmoid(z) .* (1 - sigmoid(z));nnCostFunctionTheta1 = reshape(nn_params(1:hidden_]]></description><category></category></item><item><title><![CDATA[Stanford 机器学习笔记 Week5 Neural Networks: Learning]]></title><link>https://blog.youkuaiyun.com/Baoli1008/article/details/50735807</link><guid>https://blog.youkuaiyun.com/Baoli1008/article/details/50735807</guid><author>Baoli1008</author><pubDate>Thu, 25 Feb 2016 11:24:17 +0800</pubDate><description><![CDATA[Cost Function and BackpropagationCost Function在求取神经网络权重矩阵时，第一步也是要先写出cost function。它的cost function就是带rugularization 的logistic regression的加强版，因为在多种类分类中每个数据不再只有一个输出y(i)，而是有K个0/1输出组成一个向量（K为种类数），因此神经网络的cost]]></description><category></category></item></channel></rss>