<?xml version="1.0" encoding="utf-8" ?><rss version="2.0"><channel><title><![CDATA[Boiler UP!]]></title><description><![CDATA[Boiler UP!]]></description><link>https://blog.youkuaiyun.com/jasonwoolf</link><language>zh-cn</language><generator>https://blog.youkuaiyun.com/</generator><copyright><![CDATA[Copyright &copy; jasonwoolf]]></copyright><item><title><![CDATA[Linux下 和 Windows 下 Oracle Instant Client 的安装.]]></title><link>https://blog.youkuaiyun.com/jasonwoolf/article/details/47373251</link><guid>https://blog.youkuaiyun.com/jasonwoolf/article/details/47373251</guid><author>jasonwoolf</author><pubDate>Fri, 01 Jun 2018 01:57:14 +0800</pubDate><description><![CDATA[Oracle数据库软件十分庞大，数据库引擎有好几个G。通常情况下，我们的使用方式是安装一个Oracle数据库在服务器机器上，在客户端通过PL/SQL Developer、sqlplus等工具操作服务器上的数据，当然Oracle 10g后，OEM(企业管理器)已经是WEB版了，在浏览器中也可以同样操作数据库。

但是Windows下客户端工具 PL/SQL Developer 使用得最多，Lin]]></description><category></category></item><item><title><![CDATA[Change Jupyter Notebook Default Directory]]></title><link>https://blog.youkuaiyun.com/jasonwoolf/article/details/79191148</link><guid>https://blog.youkuaiyun.com/jasonwoolf/article/details/79191148</guid><author>jasonwoolf</author><pubDate>Mon, 29 Jan 2018 04:54:12 +0800</pubDate><description><![CDATA[Change Jupyter Notebook Default Directory


  There are three way to change the default (i.e., start-up) directory of jupyter notebook.




Solution #1

1.Use command line (aka, cmd), run the followin]]></description><category></category></item><item><title><![CDATA[How to use MathJax in Markdown]]></title><link>https://blog.youkuaiyun.com/jasonwoolf/article/details/78778292</link><guid>https://blog.youkuaiyun.com/jasonwoolf/article/details/78778292</guid><author>jasonwoolf</author><pubDate>Tue, 12 Dec 2017 01:38:11 +0800</pubDate><description><![CDATA[How to use MathJax in MarkdownWhen to Use MathJax?When using markdown to write blog, especially using Github Page to do it. You may have trouble to display formula. There are several ways[1] to do that]]></description><category></category></item><item><title><![CDATA[How to Render the Hyperlink with Braces]]></title><link>https://blog.youkuaiyun.com/jasonwoolf/article/details/78778291</link><guid>https://blog.youkuaiyun.com/jasonwoolf/article/details/78778291</guid><author>jasonwoolf</author><pubDate>Tue, 12 Dec 2017 01:37:51 +0800</pubDate><description><![CDATA[How to Render the Hyperlink with BracesHere is the code![Reorganized Dichotomies of B(4,3) - 1][11][11]:https://raw.githubusercontent.com/zhichengML/MarkdownPhoto/master/MachineLearning/Machine%20Learn]]></description><category></category></item><item><title><![CDATA[Mini-Batch Gradient Descent]]></title><link>https://blog.youkuaiyun.com/jasonwoolf/article/details/78778290</link><guid>https://blog.youkuaiyun.com/jasonwoolf/article/details/78778290</guid><author>jasonwoolf</author><pubDate>Tue, 12 Dec 2017 01:37:16 +0800</pubDate><description><![CDATA[Mini-Batch Gradient Descent1. What is Mini-Batch Gradient Descent?Mini-Batch Gradient Descent is an algorithm between the Batch Gradient Descent and Stochastic Gradient Descent. Concretly, this use som]]></description><category></category></item><item><title><![CDATA[Stochastic Gradient Descent]]></title><link>https://blog.youkuaiyun.com/jasonwoolf/article/details/78778289</link><guid>https://blog.youkuaiyun.com/jasonwoolf/article/details/78778289</guid><author>jasonwoolf</author><pubDate>Tue, 12 Dec 2017 01:36:48 +0800</pubDate><description><![CDATA[Stochastic Gradient Descent1. What is Stochastic Gradient DescentStochastic Gradient Descent(SGD) is similiar with Batch Gradient Desent, but it used only 1 example for each iteration. So that it makes]]></description><category></category></item><item><title><![CDATA[Latex 常见错误整理]]></title><link>https://blog.youkuaiyun.com/jasonwoolf/article/details/78719382</link><guid>https://blog.youkuaiyun.com/jasonwoolf/article/details/78719382</guid><author>jasonwoolf</author><pubDate>Tue, 05 Dec 2017 13:26:36 +0800</pubDate><description><![CDATA[Latex 常见错误整理1. 求导\sideset{^*}{'}\sum_{1\le i\le 100} A(i)
\qquad
\sum_{1\le i\le 100}\vphantom{\sum}^{'} A(i)
\qquad
\mathop{{\sum}'}_{1\le i\le 100} A(i)∑∗∑′1≤i≤100A(i)∑1≤i≤100∑′A(i)∑′1≤i≤100A(i)
\sid]]></description><category></category></item><item><title><![CDATA[Concave and Convex Function]]></title><link>https://blog.youkuaiyun.com/jasonwoolf/article/details/78681273</link><guid>https://blog.youkuaiyun.com/jasonwoolf/article/details/78681273</guid><author>jasonwoolf</author><pubDate>Thu, 30 Nov 2017 21:33:47 +0800</pubDate><description><![CDATA[Concave and Convex FunctionWhat is Concave Function?Concave function is a function where the line segement between any two points of the function lies below or on the graph.[1]Mathematically, as for co]]></description><category></category></item><item><title><![CDATA[Batch Gradient Descent]]></title><link>https://blog.youkuaiyun.com/jasonwoolf/article/details/78681260</link><guid>https://blog.youkuaiyun.com/jasonwoolf/article/details/78681260</guid><author>jasonwoolf</author><pubDate>Thu, 30 Nov 2017 21:32:34 +0800</pubDate><description><![CDATA[Batch Gradient Descent
  We use linear regression as example to explain this optimization algorithm.
1. Formula1.1. Cost Function
  We prefer residual sum of squared to evaluate linear regression.
J(θ)]]></description><category></category></item><item><title><![CDATA[Get More Data]]></title><link>https://blog.youkuaiyun.com/jasonwoolf/article/details/78681230</link><guid>https://blog.youkuaiyun.com/jasonwoolf/article/details/78681230</guid><author>jasonwoolf</author><pubDate>Thu, 30 Nov 2017 21:29:33 +0800</pubDate><description><![CDATA[Get More Data1. Why We Need More Data?In many situations (low bias learning model), more data usually means better performance of the model.2. When We Need More Data?Usually, we should plot the learnin]]></description><category></category></item><item><title><![CDATA[Tylor Expansion Example]]></title><link>https://blog.youkuaiyun.com/jasonwoolf/article/details/78458581</link><guid>https://blog.youkuaiyun.com/jasonwoolf/article/details/78458581</guid><author>jasonwoolf</author><pubDate>Mon, 06 Nov 2017 15:57:51 +0800</pubDate><description><![CDATA[Tylor Expansion Example
  Tylor Expansion is a powerful tool to deal with limits. Some examples are showed below.
PrerequisiteSee more about how to calculate derivative at this link and differential ru]]></description><category></category></item><item><title><![CDATA[Practical Derivatives]]></title><link>https://blog.youkuaiyun.com/jasonwoolf/article/details/78458568</link><guid>https://blog.youkuaiyun.com/jasonwoolf/article/details/78458568</guid><author>jasonwoolf</author><pubDate>Mon, 06 Nov 2017 15:57:18 +0800</pubDate><description><![CDATA[Practical Derivatives1. Power FunctionGiven : f(x)=xa(a∈Q)f \left( x \right) = x^a \text(a \in Q)Proofs : f′(x)=a⋅xa−1f'\left( x \right) = a \cdot x^{a-1}
  Deduction
f′(x)=limΔx→0f(x+Δx)−f(x)Δx=limΔx→]]></description><category></category></item><item><title><![CDATA[What is a one-sided limits?]]></title><link>https://blog.youkuaiyun.com/jasonwoolf/article/details/78458560</link><guid>https://blog.youkuaiyun.com/jasonwoolf/article/details/78458560</guid><author>jasonwoolf</author><pubDate>Mon, 06 Nov 2017 15:56:34 +0800</pubDate><description><![CDATA[What is a one-sided limits?IntroductionOne-sided Limits and Two-sided LimitsPeople are familiar with two sided limits, shown below.limx−>af(x)=L(1)
\lim\limits_{x->a} f(x) = L
\tag{$1$}
But here, we ar]]></description><category></category></item><item><title><![CDATA[Function and Limit]]></title><link>https://blog.youkuaiyun.com/jasonwoolf/article/details/78458554</link><guid>https://blog.youkuaiyun.com/jasonwoolf/article/details/78458554</guid><author>jasonwoolf</author><pubDate>Mon, 06 Nov 2017 15:55:53 +0800</pubDate><description><![CDATA[Function and Limit1. Function1) What is a Function?In mathematics, a function is a relation between a set of inputs and a set of permissible outputs with the property that each input is related to exac]]></description><category></category></item><item><title><![CDATA[Differentiation Rules]]></title><link>https://blog.youkuaiyun.com/jasonwoolf/article/details/78458549</link><guid>https://blog.youkuaiyun.com/jasonwoolf/article/details/78458549</guid><author>jasonwoolf</author><pubDate>Mon, 06 Nov 2017 15:55:16 +0800</pubDate><description><![CDATA[Differentiation Rules1. The Sum RuleIn calculus, the sum rule in differentiation is a method of finding the derivative of a function that is the sum of two other functions for which derivatives exist.[]]></description><category></category></item><item><title><![CDATA[tanh Function]]></title><link>https://blog.youkuaiyun.com/jasonwoolf/article/details/78398607</link><guid>https://blog.youkuaiyun.com/jasonwoolf/article/details/78398607</guid><author>jasonwoolf</author><pubDate>Mon, 30 Oct 2017 23:15:11 +0800</pubDate><description><![CDATA[tanh Function1. Introduction
  To limit all data within the range of -1 to 1. Comparing to Sigmoid Function which output range is [0,1]
2. FormulaThe formula and derivative of tanh is: 
f(z)f′(z)=tanh(]]></description><category></category></item><item><title><![CDATA[Mathematics - Matrix and Vector Transformation]]></title><link>https://blog.youkuaiyun.com/jasonwoolf/article/details/78398604</link><guid>https://blog.youkuaiyun.com/jasonwoolf/article/details/78398604</guid><author>jasonwoolf</author><pubDate>Mon, 30 Oct 2017 23:14:52 +0800</pubDate><description><![CDATA[Mathematics - Matrix and Vector Transformation
Mathematics - Matrix and Vector Transformation 
1. When to Transform?
2. How to Transform? 
1) Octave Implement
2) Python Implement
 
1. When to Transform]]></description><category></category></item><item><title><![CDATA[Sigmoid Function]]></title><link>https://blog.youkuaiyun.com/jasonwoolf/article/details/78398601</link><guid>https://blog.youkuaiyun.com/jasonwoolf/article/details/78398601</guid><author>jasonwoolf</author><pubDate>Mon, 30 Oct 2017 23:14:13 +0800</pubDate><description><![CDATA[Sigmoid Function1. Introduction
  To limit all data within the range of 0 to 1.
2. Formulay=11+e−x
y = \frac{1}{1+e^{-x}}
3. Implementation3.1 Octave x = linspace(-10, 10 ,10000);
 y = zeros( size(x, 1]]></description><category></category></item><item><title><![CDATA[How to use MathJax in Markdown]]></title><link>https://blog.youkuaiyun.com/jasonwoolf/article/details/78398582</link><guid>https://blog.youkuaiyun.com/jasonwoolf/article/details/78398582</guid><author>jasonwoolf</author><pubDate>Mon, 30 Oct 2017 23:10:30 +0800</pubDate><description><![CDATA[How to use MathJax in MarkdownWhen to Use MathJax?When using markdown to write blog, especially using Github Page to do it. You may have trouble to display formula. There are several ways[1] to do that]]></description><category></category></item><item><title><![CDATA[0. 机器学习基石 - Table of Contents]]></title><link>https://blog.youkuaiyun.com/jasonwoolf/article/details/78223162</link><guid>https://blog.youkuaiyun.com/jasonwoolf/article/details/78223162</guid><author>jasonwoolf</author><pubDate>Fri, 13 Oct 2017 10:41:02 +0800</pubDate><description><![CDATA[机器学习基石(Machine learning Foundation) - Table of Contents
  这系列博客是基于台湾大学的林轩田教授的机器学习基石，自己加以整理的学习笔记。
为什么选这个教程?
  网上大部分人都是推荐Andrew Ng教授的机器学习，但是对比之后我觉得各有优劣，Ng的课程更适合没有任何基础的学生，算法没有讲的很深，相比之下，Hsuan-Tien Lin的教程遵循]]></description><category></category></item></channel></rss>