<?xml version="1.0" encoding="utf-8" ?><rss version="2.0"><channel><title><![CDATA[m0_61368114的博客]]></title><description><![CDATA[]]></description><link>https://blog.youkuaiyun.com/m0_61368114</link><language>zh-cn</language><generator>https://blog.youkuaiyun.com/</generator><copyright><![CDATA[Copyright &copy; m0_61368114]]></copyright><item><title><![CDATA[爬虫实验。]]></title><link>https://blog.youkuaiyun.com/m0_61368114/article/details/127875960</link><guid>https://blog.youkuaiyun.com/m0_61368114/article/details/127875960</guid><author>m0_61368114</author><pubDate>Tue, 15 Nov 2022 22:52:56 +0800</pubDate><description><![CDATA[import re #正则表达式，进行文字匹配
from bs4 import BeautifulSoup#网页解析，获取数据
import urllib.request,urllib.error #制定URL，获取网页数据
import xlwt #进行excel操作

def main():
    baseurl = "https://sh.lianjia.com/zufang/"
    # 爬取网页
    datalist = getData(baseurl)
    # 保存数据
    s]]></description><category></category></item><item><title><![CDATA[马尔科夫链]]></title><link>https://blog.youkuaiyun.com/m0_61368114/article/details/127875895</link><guid>https://blog.youkuaiyun.com/m0_61368114/article/details/127875895</guid><author>m0_61368114</author><pubDate>Tue, 15 Nov 2022 22:47:09 +0800</pubDate><description><![CDATA[1.什么是马尔可夫链在机器学习算法中，马尔可夫链(Markov chain)是个很重要的概念。马尔可夫链（Markov chain），又称离散时间马尔可夫链（discrete-time Markov chain），因俄国数学家安德烈·马尔可夫（俄语：Андрей Андреевич Марков）得名，为状态空间中经过从一个状态到另一个状态的转换的随机过程。该过程要求具备“无记忆”的性质：下一状态的概率分布只能由当前状态决定，在时间序列中它前面的事件均与之无关。这种特定类型的“无记忆性”称作马尔可夫性质。马]]></description><category></category></item><item><title><![CDATA[马尔科夫链]]></title><link>https://blog.youkuaiyun.com/m0_61368114/article/details/127875738</link><guid>https://blog.youkuaiyun.com/m0_61368114/article/details/127875738</guid><author>m0_61368114</author><pubDate>Tue, 15 Nov 2022 22:42:17 +0800</pubDate><description><![CDATA[马尔可夫链（Markov chain），又称离散时间马尔可夫链（discrete-time Markov chain），因俄国数学家安德烈·马尔可夫（俄语：Андрей Андреевич Марков）得名，为状态空间中经过从一个状态到另一个状态的转换的随机过程。随机漫步中每一步的状态是在图形中的点，每一步可以移动到任何一个相邻的点，在这里移动到每一个点的概率都是相。既然某一时刻状态转移的概率只依赖前一个状态，那么只要求出系统中任意两个状态之间的转移概率，这个马尔科夫链的模型就定了。]]></description><category></category></item><item><title><![CDATA[三门问题。]]></title><link>https://blog.youkuaiyun.com/m0_61368114/article/details/127754476</link><guid>https://blog.youkuaiyun.com/m0_61368114/article/details/127754476</guid><author>m0_61368114</author><pubDate>Tue, 08 Nov 2022 17:04:28 +0800</pubDate><description><![CDATA[#换另一扇门是否会增加参赛者赢得汽车的几率？
# 如果严格按照上述条件，即主持人清楚地知道
# 自己打开的那扇门后面是羊，那么答案是会。
# 不换门的话，赢得汽车的几率是1/3,，换门的话，赢得汽车的几率是2/3。
import math;
import random;

n = 1000000
def pd(change):
  gpscar=random.randint(0,2)#汽车位置
  pdcar =random.randint(0,2)#猜测位置

  if pdcar==gpsc]]></description><category></category></item><item><title><![CDATA[卷积神经网络学习]]></title><link>https://blog.youkuaiyun.com/m0_61368114/article/details/127499427</link><guid>https://blog.youkuaiyun.com/m0_61368114/article/details/127499427</guid><author>m0_61368114</author><pubDate>Mon, 24 Oct 2022 19:45:09 +0800</pubDate><description><![CDATA[图像中物体边缘化检测。]]></description><category></category></item><item><title><![CDATA[反向传播算法推导]]></title><link>https://blog.youkuaiyun.com/m0_61368114/article/details/127351430</link><guid>https://blog.youkuaiyun.com/m0_61368114/article/details/127351430</guid><author>m0_61368114</author><pubDate>Sun, 16 Oct 2022 18:57:41 +0800</pubDate><description><![CDATA[]]></description><category></category></item><item><title><![CDATA[公式的推导]]></title><link>https://blog.youkuaiyun.com/m0_61368114/article/details/127049851</link><guid>https://blog.youkuaiyun.com/m0_61368114/article/details/127049851</guid><author>m0_61368114</author><pubDate>Mon, 26 Sep 2022 11:02:26 +0800</pubDate><description><![CDATA[]]></description><category></category></item><item><title><![CDATA[梯度下降算法]]></title><link>https://blog.youkuaiyun.com/m0_61368114/article/details/126941486</link><guid>https://blog.youkuaiyun.com/m0_61368114/article/details/126941486</guid><author>m0_61368114</author><pubDate>Mon, 19 Sep 2022 20:31:35 +0800</pubDate><description><![CDATA[]]></description><category></category></item><item><title><![CDATA[第一次实验]]></title><link>https://blog.youkuaiyun.com/m0_61368114/article/details/126715614</link><guid>https://blog.youkuaiyun.com/m0_61368114/article/details/126715614</guid><author>m0_61368114</author><pubDate>Mon, 05 Sep 2022 23:17:27 +0800</pubDate><description><![CDATA[]]></description><category></category></item><item><title><![CDATA[第二次作业，求和]]></title><link>https://blog.youkuaiyun.com/m0_61368114/article/details/126696544</link><guid>https://blog.youkuaiyun.com/m0_61368114/article/details/126696544</guid><author>m0_61368114</author><pubDate>Sun, 04 Sep 2022 23:31:09 +0800</pubDate><description><![CDATA[]]></description><category></category></item><item><title><![CDATA[python第一题:hello world]]></title><link>https://blog.youkuaiyun.com/m0_61368114/article/details/126634300</link><guid>https://blog.youkuaiyun.com/m0_61368114/article/details/126634300</guid><author>m0_61368114</author><pubDate>Wed, 31 Aug 2022 23:18:34 +0800</pubDate><description><![CDATA[]]></description><category></category></item></channel></rss>