<?xml version="1.0" encoding="utf-8" ?><rss version="2.0"><channel><title><![CDATA[wangcong02345的专栏]]></title><description><![CDATA[没有经过实践的东西是不会被真正理解的]]></description><link>https://blog.youkuaiyun.com/wangcong02345</link><language>zh-cn</language><generator>https://blog.youkuaiyun.com/</generator><copyright><![CDATA[Copyright &copy; wangcong02345]]></copyright><item><title><![CDATA[Week11_1Application Photo OCR]]></title><link>https://blog.youkuaiyun.com/wangcong02345/article/details/79367552</link><guid>https://blog.youkuaiyun.com/wangcong02345/article/details/79367552</guid><author>wangcong02345</author><pubDate>Sun, 25 Feb 2018 11:03:49 +0800</pubDate><description><![CDATA[Week11_1Application Photo OCR



第 1 题

Suppose you are running a sliding window detector to find text in images. 
Your input images are 1000x1000 pixels. You will run your sliding windows detector at...]]></description><category></category></item><item><title><![CDATA[Week10_1Large Scale Machine Learning]]></title><link>https://blog.youkuaiyun.com/wangcong02345/article/details/79367536</link><guid>https://blog.youkuaiyun.com/wangcong02345/article/details/79367536</guid><author>wangcong02345</author><pubDate>Sun, 25 Feb 2018 11:01:31 +0800</pubDate><description><![CDATA[Week10_1Large Scale Machine Learning



Week10_1Large Scale Machine Learning

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Suppose you are training a logistic regression classifier using stochastic gr...]]></description><category></category></item><item><title><![CDATA[Week9_3Program_Anomaly Detection and Recommender Systems编程解析]]></title><link>https://blog.youkuaiyun.com/wangcong02345/article/details/79367529</link><guid>https://blog.youkuaiyun.com/wangcong02345/article/details/79367529</guid><author>wangcong02345</author><pubDate>Sun, 25 Feb 2018 11:00:51 +0800</pubDate><description><![CDATA[Week9_3Program_Anomaly Detection and Recommender Systems编程解析



1. Anomaly detection分析



1.1 Estimate gaussian parameters  0 / 15

计算公式: 
μi=1m∑j=1mx(j)iμi=1m∑j=1mxi(j)\mu_i = \frac{1}{m}\sum\limits_...]]></description><category></category></item><item><title><![CDATA[Week9_2Recommender Systems]]></title><link>https://blog.youkuaiyun.com/wangcong02345/article/details/79367503</link><guid>https://blog.youkuaiyun.com/wangcong02345/article/details/79367503</guid><author>wangcong02345</author><pubDate>Sun, 25 Feb 2018 10:58:27 +0800</pubDate><description><![CDATA[Week9_2Recommender Systems



Week9_2Recommender Systems

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Suppose you run a bookstore, and have ratings (1 to 5 stars) of books. Your collaborative filt...]]></description><category></category></item><item><title><![CDATA[Week9_1Anomaly Detection]]></title><link>https://blog.youkuaiyun.com/wangcong02345/article/details/79367491</link><guid>https://blog.youkuaiyun.com/wangcong02345/article/details/79367491</guid><author>wangcong02345</author><pubDate>Sun, 25 Feb 2018 10:57:19 +0800</pubDate><description><![CDATA[Week9_1Anomaly Detection



Week9_1Anomaly Detection

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第 1 题

For which of the following problems would anomaly detection be a suitable algorithm?


Given an im...]]></description><category></category></item><item><title><![CDATA[Week8_3Program_K-Means Clustering and PCA编程解析]]></title><link>https://blog.youkuaiyun.com/wangcong02345/article/details/79367478</link><guid>https://blog.youkuaiyun.com/wangcong02345/article/details/79367478</guid><author>wangcong02345</author><pubDate>Sun, 25 Feb 2018 10:55:55 +0800</pubDate><description><![CDATA[Week8_3Program_K-Means Clustering and PCA编程解析



1. ex7 流程分析



1.1  plotData

按照ex7.m中的流程走一遍








2. 编程分析



2.1 Find closest centroids  0 / 30

计算公式: ||x(i)−μj||2||x(i)−μj||2||x^{(i)}- \mu_j||^2 ...]]></description><category></category></item><item><title><![CDATA[Week8_2Principal Component Analysis]]></title><link>https://blog.youkuaiyun.com/wangcong02345/article/details/79367472</link><guid>https://blog.youkuaiyun.com/wangcong02345/article/details/79367472</guid><author>wangcong02345</author><pubDate>Sun, 25 Feb 2018 10:55:28 +0800</pubDate><description><![CDATA[Week8_2Principal Component Analysis

第 1 题

Consider the following 2D dataset: 
  
Which of the following figures correspond to possible values that PCA may return for u(1)u(1)u^{(1)} (the first eigen...]]></description><category></category></item><item><title><![CDATA[Week8_1Unsupervised Learning]]></title><link>https://blog.youkuaiyun.com/wangcong02345/article/details/79367453</link><guid>https://blog.youkuaiyun.com/wangcong02345/article/details/79367453</guid><author>wangcong02345</author><pubDate>Sun, 25 Feb 2018 10:52:58 +0800</pubDate><description><![CDATA[Week8_1Unsupervised Learning



Week8_1Unsupervised Learning

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For which of the following tasks might K-means clustering be a suitable algorithm? Select al...]]></description><category></category></item><item><title><![CDATA[Week7_2Program_Support Vector Machines编程解析]]></title><link>https://blog.youkuaiyun.com/wangcong02345/article/details/79367444</link><guid>https://blog.youkuaiyun.com/wangcong02345/article/details/79367444</guid><author>wangcong02345</author><pubDate>Sun, 25 Feb 2018 10:51:49 +0800</pubDate><description><![CDATA[Week7_2Program_Support Vector Machines编程解析



1. ex6 流程分析



1.1  plotData

按照ex6.m中的流程走一遍








2. 编程分析



2.0 计算公式

hθ(x)=minθC∑i=1m[y(i)cost1(θTx(i))+(1−y(i))cost0(θTx(i))]+12∑i=1nθ2jhθ(x)=minθC∑...]]></description><category></category></item><item><title><![CDATA[Week7_1Support Vector Machinesg]]></title><link>https://blog.youkuaiyun.com/wangcong02345/article/details/79367441</link><guid>https://blog.youkuaiyun.com/wangcong02345/article/details/79367441</guid><author>wangcong02345</author><pubDate>Sun, 25 Feb 2018 10:51:22 +0800</pubDate><description><![CDATA[Week7_1Support Vector Machinesg



第 1 题

Suppose you have trained an SVM classifier with a Gaussian kernel, and it learned the following decision boundary on the training set: 
 
You suspect that the...]]></description><category></category></item><item><title><![CDATA[Week6_3Machine Learning System Design]]></title><link>https://blog.youkuaiyun.com/wangcong02345/article/details/79367419</link><guid>https://blog.youkuaiyun.com/wangcong02345/article/details/79367419</guid><author>wangcong02345</author><pubDate>Sun, 25 Feb 2018 10:49:09 +0800</pubDate><description><![CDATA[Week6_3Machine Learning System Design



第 1 题

You are working on a spam classification system using regularized logistic regression. “Spam” is a positive class (y = 1) and “not spam” is the negative...]]></description><category></category></item><item><title><![CDATA[w6_2Regularized Linear Regression and BiasVariance编程解析]]></title><link>https://blog.youkuaiyun.com/wangcong02345/article/details/79367417</link><guid>https://blog.youkuaiyun.com/wangcong02345/article/details/79367417</guid><author>wangcong02345</author><pubDate>Sun, 25 Feb 2018 10:48:36 +0800</pubDate><description><![CDATA[w6_2Regularized Linear Regression and BiasVariance编程解析



1. ex5 流程分析



1.1  plotData

按照ex5.m中的流程走一遍








2. 编程分析



2.0 计算公式

代价函数： 
hθ(x)=12m∑i=1m(hθ(x(i)−y(i))2+λ2m(∑j=1nθ2j)hθ(x)=12m∑i=1m(hθ(...]]></description><category></category></item><item><title><![CDATA[Week6_1Advice for Applying Machine Learning]]></title><link>https://blog.youkuaiyun.com/wangcong02345/article/details/79367407</link><guid>https://blog.youkuaiyun.com/wangcong02345/article/details/79367407</guid><author>wangcong02345</author><pubDate>Sun, 25 Feb 2018 10:47:58 +0800</pubDate><description><![CDATA[Week6_1Advice for Applying Machine Learning



Week6_1Advice for Applying Machine Learning

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You train a learning algorithm, and find that it has unaccepta...]]></description><category></category></item><item><title><![CDATA[w5_2Neural Network Learning编程解析]]></title><link>https://blog.youkuaiyun.com/wangcong02345/article/details/79367393</link><guid>https://blog.youkuaiyun.com/wangcong02345/article/details/79367393</guid><author>wangcong02345</author><pubDate>Sun, 25 Feb 2018 10:46:33 +0800</pubDate><description><![CDATA[w5_2Neural Network Learning编程解析



1. ex4 流程分析



1.1  plotData

按照ex4.m中的流程走一遍



%% Setup the parameters you will use for this exercise
input_layer_size  = 400;  % 20x20 Input Images of Digits
hidde...]]></description><category></category></item><item><title><![CDATA[Week5_1Neural Networks Learning]]></title><link>https://blog.youkuaiyun.com/wangcong02345/article/details/79367388</link><guid>https://blog.youkuaiyun.com/wangcong02345/article/details/79367388</guid><author>wangcong02345</author><pubDate>Sun, 25 Feb 2018 10:45:43 +0800</pubDate><description><![CDATA[Week5_1Neural Networks Learning



Week5_1Neural Networks Learning

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You are training a three layer neural network and would like to use backpropagation t...]]></description><category></category></item><item><title><![CDATA[w4_2Multi-class Classification and Neural Networks编程解析]]></title><link>https://blog.youkuaiyun.com/wangcong02345/article/details/79367383</link><guid>https://blog.youkuaiyun.com/wangcong02345/article/details/79367383</guid><author>wangcong02345</author><pubDate>Sun, 25 Feb 2018 10:44:42 +0800</pubDate><description><![CDATA[w4_2Multi-class Classification and Neural Networks编程解析



w4_2Multi-class Classification and Neural Networks编程解析

ex3m
1  ex3m框架分析
2  Regularized logistic regression 0  30
21 logistic regression Cost ...]]></description><category></category></item><item><title><![CDATA[Week4_1Neural Networks Representation]]></title><link>https://blog.youkuaiyun.com/wangcong02345/article/details/79367377</link><guid>https://blog.youkuaiyun.com/wangcong02345/article/details/79367377</guid><author>wangcong02345</author><pubDate>Sun, 25 Feb 2018 10:43:52 +0800</pubDate><description><![CDATA[Week4_1Neural Networks Representation



Week4_1Neural Networks Representation

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Which of the following statements are true? Check all that apply.


The ac...]]></description><category></category></item><item><title><![CDATA[机器学习测试Week3_3Program_Logistic Regression编程解析]]></title><link>https://blog.youkuaiyun.com/wangcong02345/article/details/79028055</link><guid>https://blog.youkuaiyun.com/wangcong02345/article/details/79028055</guid><author>wangcong02345</author><pubDate>Wed, 10 Jan 2018 20:39:41 +0800</pubDate><description><![CDATA[w3_3Program_Logistic Regression编程解析



1. Logistic Regression



1.1  plotData

把数据可视化, 在plotData.m中添加:



% Find Indices of Positive and Negative Examples
pos = find(y==1); neg = find(y == 0);
% Plot]]></description><category></category></item><item><title><![CDATA[机器学习测试Week3_2Regularization]]></title><link>https://blog.youkuaiyun.com/wangcong02345/article/details/79028050</link><guid>https://blog.youkuaiyun.com/wangcong02345/article/details/79028050</guid><author>wangcong02345</author><pubDate>Wed, 10 Jan 2018 20:39:01 +0800</pubDate><description><![CDATA[Week3_2Regularization



第 1 题

You are training a classification model with logistic  
regression. Which of the following statements are true? Check 
all that apply.


Introducing regularization to t]]></description><category></category></item><item><title><![CDATA[机器学习测试Week3_1Logistic Regression]]></title><link>https://blog.youkuaiyun.com/wangcong02345/article/details/79028036</link><guid>https://blog.youkuaiyun.com/wangcong02345/article/details/79028036</guid><author>wangcong02345</author><pubDate>Wed, 10 Jan 2018 20:37:44 +0800</pubDate><description><![CDATA[Week3_1Logistic Regression



第 1 题

Suppose that you have trained a logistic regression classifier, and it outputs on a new example x a prediction hθ(x)h_\theta(x) = 0.4. This means (check all that a]]></description><category></category></item></channel></rss>