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2018/08/18/notes/website/index.html

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2018/08/29/c-lang/snake_in_c/index.html

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2018/09/09/happytime/macao/index.html

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@@ -230,7 +230,7 @@ <h1 id="总结"><a href="#总结" class="headerlink" title="总结"></a>总结</
 
 
     2018-2024
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2019/07/01/deep_learning/note_neural_network1/index.html

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 <meta property="og:description" content="先落结论用 n[l] 表示第 l 层神经元的数量, w[l] 为第 l 层的权重, b[l] 为偏置z[l] 表示未激活前的矩阵(也就是wx+b的结果), a[l] 表示激活 z[l] 后(第 l 层的输出)的矩阵那么正确的神经网络各矩阵的维度应该为:w[l]: (n[l], n[l - 1])b[l]: (n[l], 1)a[l], z[l]: (n[l], 1)当输入多个训练集m, w[l]">
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@@ -233,7 +233,7 @@ <h1 id="推导过程"><a href="#推导过程" class="headerlink" title="推导
 
 
     2018-2024
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2019/07/04/deep_learning/note_neural_network2/index.html

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 <meta property="og:title" content="Neural Network 0x02- Optimization. 神经网络笔记 -- 优化手段">
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 <meta property="og:description" content="正则化(Regularization)深度学习可能存在过拟合问题，有两个解决方法，一个是正则化，另一个是准备更多的数据。 至于为什么起作用这篇笔记讲得 very good:http:&#x2F;&#x2F;www.ai-start.com&#x2F;dl2017&#x2F;html&#x2F;lesson2-week1.html#header-n119">
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@@ -230,7 +230,7 @@ <h1 id="正则化-Regularization"><a href="#正则化-Regularization" class="hea
 
 
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2020/03/29/system/pe_file/index.html

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     2018-2024
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2020/04/03/draw_stock_map/index.html

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 <meta property="og:description" content="1234567891011121314151617181920212223242526# 导入第三方库import pandas_datareaderimport datetimeimport matplotlib.pyplot as plt# 获取现在的时间# (2020, 3, 7, 10, 37, 13, 186885)end &#x3D; datetime.datetime.now()start &#x3D;">
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2020/07/22/sql_inject_mark/index.html

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 <meta property="og:description" content="SQL注入靶机:http:&#x2F;&#x2F;219.153.49.228:42101&#x2F; 第一步: 判断是否为注入点 1python sqlmap.py -u &quot;http:&#x2F;&#x2F;219.153.49.228:42101&#x2F;&quot; --forms --crawl&#x3D;2 结果出来了，果然有漏洞: 123456789101112131415---Parameter: id (GET)    Type: boo">
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-        <span class="p-name" itemprop="name">Stepfen Shawn</span>
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@@ -251,7 +251,7 @@ <h1 id="SQL注入"><a href="#SQL注入" class="headerlink" title="SQL注入"></a
 
 
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2021/01/04/how_to_eval_in_computer/index.html

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 <meta property="og:title" content="Dive into the programming language. 在编程语言中计算过程是如何求值的?">
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 <meta property="og:description" content="前言最近准备啃一本书&lt;&lt;计算机程序的构造与解释&gt;&gt;(国内简称sicp)。仅仅看了第一章前面两节，对计算过程又有了深刻的理解。书中用的语言是lisp，当我还在抱怨为什么不用当时流行的c时，读了几页就领悟了作者的用意: 人们常说”c生万物”， 没错，不过那仅仅是在语言的实现层面上说的，但在计算过程中, 我敢肯定: lisp生万物! 那么什么是计算过程呢? 在数学的角度，也许是一">
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+        <span class="p-name" itemprop="name">🍌Shawn</span>
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@@ -254,7 +254,7 @@ <h1 id="求值也有顺序之分"><a href="#求值也有顺序之分" class="hea
 
 
     2018-2024
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2021/01/23/c-lang/c_lang_read/index.html

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 <meta property="og:title" content="Dive into the C raed function. 深入c语言嘅 read 函数">
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 <meta property="og:description" content="引言我们都知道c语言是一门非常强大的语言, 这得益于佢对系统底层嘅完美封装，因此用c语言读取文件嘅效率非常高, 接下来我们来看看读取文件最基本嘅函数 – read read函数的定义我们来看一下read嘅定义:   1ssize_t read(int fd,void *buf,size_t nbyte) 调用该函数就会从fd中读取最多nbytes个字节到缓冲区buf中 分析首先我们看到第一个参数f">
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+        <span class="p-name" itemprop="name">🍌Shawn</span>
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@@ -243,7 +243,7 @@ <h1 id="模拟vim编辑器的输入模式"><a href="#模拟vim编辑器的输入
 
 
     2018-2024
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2021/03/17/derivation_program_in_lisp/index.html

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 <meta property="og:title" content="Create a derivation program in lisp. 用 Lisp 写个求导程序">
 <meta property="og:url" content="https://stepfenshawn.github.io/2021/03/17/derivation_program_in_lisp/index.html">
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 <meta property="og:description" content="前言记得之前看tensorflow, numpy等库的api中提供了自动求导的函数, 觉得其中一定是用了某些高深的数值分析算法，但在 sicp书中也给了个简单的求导程序, 本质是基于求导法则的递归性质来实现的， 感觉有点意思记录一下。 我们先来看看求导法则:   1234dc &#x2F; dx &#x3D; 0dx &#x2F; dx &#x3D; 1d(u + v) &#x2F; dx &#x3D; du&#x2F;dx + dv&#x2F;dxd(uv)&#x2F;dx &#x3D; u(">
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@@ -242,7 +242,7 @@ <h1 id="代码实现"><a href="#代码实现" class="headerlink" title="代码
 
 
     2018-2024
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