<?xml version="1.0" encoding="utf-8" ?><rss version="2.0"><channel><title><![CDATA[刘小汪的博客]]></title><description><![CDATA[机器学习在学，欢迎交流~~~]]></description><link>https://blog.youkuaiyun.com/mrliudq</link><language>zh-cn</language><generator>https://blog.youkuaiyun.com/</generator><copyright><![CDATA[Copyright &copy; mrliudq]]></copyright><item><title><![CDATA[[ML of Andrew Ng]Week 5 Neural Networks:Learning]]></title><link>https://blog.youkuaiyun.com/mrliudq/article/details/51001166</link><guid>https://blog.youkuaiyun.com/mrliudq/article/details/51001166</guid><author>mrliudq</author><pubDate>Mon, 28 Mar 2016 20:59:17 +0800</pubDate><description><![CDATA[Week 5 Neural Networks:LearningWeek 5 Neural NetworksLearning
Cost function
Classification
Forward propagation algorithm
Cost function
Backpropagation algorithm
Gradient computation
Implementation note]]></description><category></category></item><item><title><![CDATA[[ML of Andrew Ng]Week 4 Neural Networks: Representation]]></title><link>https://blog.youkuaiyun.com/mrliudq/article/details/50903352</link><guid>https://blog.youkuaiyun.com/mrliudq/article/details/50903352</guid><author>mrliudq</author><pubDate>Wed, 16 Mar 2016 11:05:47 +0800</pubDate><description><![CDATA[Week 4 Neural Networks: RepresentationWeek 4 Neural Networks Representation
Non-linear hypotheses
Neurons and the brain
Neural Networks
The brain
Model representation
Neurons in the brain
Neural Networ]]></description><category></category></item><item><title><![CDATA[[ML of Andrew Ng]Week 3 Logistic Regression and Regularization]]></title><link>https://blog.youkuaiyun.com/mrliudq/article/details/50887103</link><guid>https://blog.youkuaiyun.com/mrliudq/article/details/50887103</guid><author>mrliudq</author><pubDate>Mon, 14 Mar 2016 15:56:59 +0800</pubDate><description><![CDATA[Week 3 Logistic Regression and Re…Week 3 Logistic Regression and Re
Logistic Regression
Classification
Hypothesis Representation
Decision boundary
Cost function
Simplified cost function and gradient de]]></description><category></category></item><item><title><![CDATA[[ML of Andrew Ng]Week 2 : Linear Regression with Multiple Variables and Normal Equation]]></title><link>https://blog.youkuaiyun.com/mrliudq/article/details/50829179</link><guid>https://blog.youkuaiyun.com/mrliudq/article/details/50829179</guid><author>mrliudq</author><pubDate>Tue, 08 Mar 2016 17:43:49 +0800</pubDate><description><![CDATA[Week 2 : Linear Regression with Multiple Variables and Normal EquationWeek 2  Linear Regression with Multiple Variables and Normal Equation
Linear Regression with Multiple Variables
The Hypothesis Func]]></description><category></category></item><item><title><![CDATA[[ML of Andrew Ng]Week 1 : Linear Regression with One Variable]]></title><link>https://blog.youkuaiyun.com/mrliudq/article/details/50823130</link><guid>https://blog.youkuaiyun.com/mrliudq/article/details/50823130</guid><author>mrliudq</author><pubDate>Mon, 07 Mar 2016 22:50:17 +0800</pubDate><description><![CDATA[Week1Week1
Introduction
Two definitions of ML
Two types of ML
Supervised Learning
Unsupervised Learning
Prerequisite for this course
Linear Regression with One Variable
The Hypothesis Function
Cost Fun]]></description><category></category></item><item><title><![CDATA[Lecture 1 : the geometry of linear equations]]></title><link>https://blog.youkuaiyun.com/mrliudq/article/details/50822597</link><guid>https://blog.youkuaiyun.com/mrliudq/article/details/50822597</guid><author>mrliudq</author><pubDate>Mon, 07 Mar 2016 21:43:11 +0800</pubDate><description><![CDATA[Lecture 1 : the geometry of linear equations
  教材：Introduction to Linear Algebra, Gilbert Strang, 4th 
  网站：ocw.mit.edu \ web.mit.edu/18.06 \ math.mit.edu/linearalgebra
Preface
  The crucial operation]]></description><category></category></item></channel></rss>