<?xml version="1.0" encoding="utf-8" ?><rss version="2.0"><channel><title><![CDATA[杨老师的小跟班(*∩_∩*)]]></title><description><![CDATA[]]></description><link>https://blog.youkuaiyun.com/m0_72662900</link><language>zh-cn</language><generator>https://blog.youkuaiyun.com/</generator><copyright><![CDATA[Copyright &copy; m0_72662900]]></copyright><item><title><![CDATA[【刷力扣算法题】滑动窗口最大值]]></title><link>https://blog.youkuaiyun.com/m0_72662900/article/details/150578077</link><guid>https://blog.youkuaiyun.com/m0_72662900/article/details/150578077</guid><author>m0_72662900</author><pubDate>Thu, 21 Aug 2025 11:03:05 +0800</pubDate><description><![CDATA[【代码】【刷力扣算法题】滑动窗口最大值。]]></description><category></category></item><item><title><![CDATA[【刷力扣算法1题】两个列表的最小索引总和]]></title><link>https://blog.youkuaiyun.com/m0_72662900/article/details/150500966</link><guid>https://blog.youkuaiyun.com/m0_72662900/article/details/150500966</guid><author>m0_72662900</author><pubDate>Tue, 19 Aug 2025 08:55:11 +0800</pubDate><description><![CDATA[3.对于相加最小索引：我的想法是先存个无穷大然后和其进行对比，在保存进而返回元素。2.对于是公共字符串可以利用两个for循环去判断相等。1.类型：数组【字符串】]]></description><category></category></item><item><title><![CDATA[【阅读文献】利用多任务驱动从高分辨率遥感图像中提取建筑物]]></title><link>https://blog.youkuaiyun.com/m0_72662900/article/details/145844251</link><guid>https://blog.youkuaiyun.com/m0_72662900/article/details/145844251</guid><author>m0_72662900</author><pubDate>Tue, 25 Feb 2025 10:06:56 +0800</pubDate><description><![CDATA[利用深度学习方法从高分辨率遥感图像中提取建筑物这一研究已得到广泛开展，以辅助诸如地理数据库更新和灾害目标监测等任务。目前该领域的文献主要聚焦于以下两个方面：1）设计适用于遥感图像的有效网络；2）通过添加后处理步骤，利用特定特征（例如形状、边界和结构）来改进预测结果。]]></description><category></category></item><item><title><![CDATA[【python】类创建、实例化和调用类方法、子类、继承、私有属性、静态方法]]></title><link>https://blog.youkuaiyun.com/m0_72662900/article/details/136118572</link><guid>https://blog.youkuaiyun.com/m0_72662900/article/details/136118572</guid><author>m0_72662900</author><pubDate>Thu, 15 Feb 2024 10:13:18 +0800</pubDate><description><![CDATA[【python】类创建、实例化和调用类方法、子类、继承、私有属性、静态方法]]></description><category></category></item><item><title><![CDATA[【python】object() takes no parameters报错原因]]></title><link>https://blog.youkuaiyun.com/m0_72662900/article/details/136114057</link><guid>https://blog.youkuaiyun.com/m0_72662900/article/details/136114057</guid><author>m0_72662900</author><pubDate>Wed, 14 Feb 2024 16:30:53 +0800</pubDate><description><![CDATA[【python】object() takes no parameters报错原因]]></description><category></category></item><item><title><![CDATA[Labelme——的安装，使用及json文件转换成数据文件]]></title><link>https://blog.youkuaiyun.com/m0_72662900/article/details/131005400</link><guid>https://blog.youkuaiyun.com/m0_72662900/article/details/131005400</guid><author>m0_72662900</author><pubDate>Fri, 02 Jun 2023 14:09:43 +0800</pubDate><description><![CDATA[Labelme——的安装，使用及json文件转换成数据文件]]></description><category></category></item><item><title><![CDATA[机器学习——集成学习]]></title><link>https://blog.youkuaiyun.com/m0_72662900/article/details/129239409</link><guid>https://blog.youkuaiyun.com/m0_72662900/article/details/129239409</guid><author>m0_72662900</author><pubDate>Mon, 27 Feb 2023 12:32:33 +0800</pubDate><description><![CDATA[集成学习：让机器学习效果更好，单个不行，群殴走起。]]></description><category></category></item><item><title><![CDATA[【深度学习】——循环神经网络RNN及实例气温预测、单层lstm股票预测]]></title><link>https://blog.youkuaiyun.com/m0_72662900/article/details/128767703</link><guid>https://blog.youkuaiyun.com/m0_72662900/article/details/128767703</guid><author>m0_72662900</author><pubDate>Sun, 29 Jan 2023 22:35:06 +0800</pubDate><description><![CDATA[密集连接网络和卷积神经网络都有主要的特点，那就是它们没有记忆。它们单独处理每个输入，在输入和输入之间没有保存任何状态。举个例子：当你在阅读一个句子的时候，你需要记住之前的内容，我们才能动态的了解这个句子想表达的含义。生物智能已渐进的方式处理信息，同时保存一个关于所处理内容的内部模型，此模型是根据过去额信息构建的，并随着新的信息进入不断更新。比如股票预测、气温预测等等。]]></description><category></category></item><item><title><![CDATA[拉格朗日插值法——python代码实现]]></title><link>https://blog.youkuaiyun.com/m0_72662900/article/details/128278422</link><guid>https://blog.youkuaiyun.com/m0_72662900/article/details/128278422</guid><author>m0_72662900</author><pubDate>Sun, 11 Dec 2022 20:43:55 +0800</pubDate><description><![CDATA[拉格朗日插值法——python代码实现
万能函数，直接调用]]></description><category></category></item><item><title><![CDATA[报错：bar() missing 1 required positional argument: ‘x‘]]></title><link>https://blog.youkuaiyun.com/m0_72662900/article/details/128011821</link><guid>https://blog.youkuaiyun.com/m0_72662900/article/details/128011821</guid><author>m0_72662900</author><pubDate>Thu, 24 Nov 2022 09:30:50 +0800</pubDate><description><![CDATA[报错：bar() missing 1 required positional argument: 'x']]></description><category></category></item><item><title><![CDATA[常微分方程数值解——差商、欧拉公式详细推导及代码实现]]></title><link>https://blog.youkuaiyun.com/m0_72662900/article/details/127931119</link><guid>https://blog.youkuaiyun.com/m0_72662900/article/details/127931119</guid><author>m0_72662900</author><pubDate>Sat, 19 Nov 2022 19:05:37 +0800</pubDate><description><![CDATA[常微分方程数值解——差商、欧拉公式详细推导、纯手工代码实现]]></description><category></category></item><item><title><![CDATA[数据挖掘——RFM客户价值模型及航空公司客户分析实例]]></title><link>https://blog.youkuaiyun.com/m0_72662900/article/details/127912673</link><guid>https://blog.youkuaiyun.com/m0_72662900/article/details/127912673</guid><author>m0_72662900</author><pubDate>Thu, 17 Nov 2022 22:08:27 +0800</pubDate><description><![CDATA[数据挖掘——RFM客户价值模型及航空公司客户分析实例]]></description><category></category></item><item><title><![CDATA[Opencv——直方图、掩膜、直方图均衡化详细介绍及代码实现]]></title><link>https://blog.youkuaiyun.com/m0_72662900/article/details/127819746</link><guid>https://blog.youkuaiyun.com/m0_72662900/article/details/127819746</guid><author>m0_72662900</author><pubDate>Wed, 16 Nov 2022 19:08:02 +0800</pubDate><description><![CDATA[Opencv——直方图、掩膜、直方图均衡化详细介绍及代码实现]]></description><category></category></item><item><title><![CDATA[module ‘cv2‘ has no attribute ‘gapi_wip_gst_GStreamerPipeline‘]]></title><link>https://blog.youkuaiyun.com/m0_72662900/article/details/127828745</link><guid>https://blog.youkuaiyun.com/m0_72662900/article/details/127828745</guid><author>m0_72662900</author><pubDate>Sun, 13 Nov 2022 09:49:13 +0800</pubDate><description><![CDATA[module 'cv2' has no attribute 'gapi_wip_gst_GStreamerPipeline']]></description><category></category></item><item><title><![CDATA[相关分析——斯皮尔曼等级相关Python实现]]></title><link>https://blog.youkuaiyun.com/m0_72662900/article/details/127658531</link><guid>https://blog.youkuaiyun.com/m0_72662900/article/details/127658531</guid><author>m0_72662900</author><pubDate>Wed, 02 Nov 2022 21:15:51 +0800</pubDate><description><![CDATA[当测量得到的数据不是等距或等比数据，而是具有等级顺序的数据；或者得到的数据是等距或等比数据，但其所来自的总体分布不是正态分布，不满足皮尔森相关系数（积差相关）的要求。这时就要运用等级相关系数。]]></description><category></category></item><item><title><![CDATA[相关分析——皮尔逊相关系数、t显著性检验及Python实现]]></title><link>https://blog.youkuaiyun.com/m0_72662900/article/details/127651823</link><guid>https://blog.youkuaiyun.com/m0_72662900/article/details/127651823</guid><author>m0_72662900</author><pubDate>Wed, 02 Nov 2022 16:42:02 +0800</pubDate><description><![CDATA[相关分析——皮尔逊相关系数、t显著性检验及Python实现]]></description><category></category></item><item><title><![CDATA[模拟退火算法详细说明]]></title><link>https://blog.youkuaiyun.com/m0_72662900/article/details/127511148</link><guid>https://blog.youkuaiyun.com/m0_72662900/article/details/127511148</guid><author>m0_72662900</author><pubDate>Tue, 25 Oct 2022 13:42:27 +0800</pubDate><description><![CDATA[模拟退火算法详细说明
模拟退火机制在一定程度上避免陷入局部最优解，与爬山算法不同那么它是如何改变的呢？在局部搜索即使找到一个比当前解较差的解，也会以一定的概率跳到这个解上。

那就是蒙特卡洛判断准则（Metropolis)]]></description><category></category></item><item><title><![CDATA[非线性方程求根——牛顿迭代法]]></title><link>https://blog.youkuaiyun.com/m0_72662900/article/details/127500814</link><guid>https://blog.youkuaiyun.com/m0_72662900/article/details/127500814</guid><author>m0_72662900</author><pubDate>Mon, 24 Oct 2022 22:29:10 +0800</pubDate><description><![CDATA[非线性方程求根——牛顿迭代法
1.实质：牛顿法实质上是一种线性方法，其基本思想是将非线性方程f(x)=0逐步归结为某种线性方程来解。]]></description><category></category></item><item><title><![CDATA[机器学习——BP神经网络详细介绍及案例Python代码实现]]></title><link>https://blog.youkuaiyun.com/m0_72662900/article/details/127451962</link><guid>https://blog.youkuaiyun.com/m0_72662900/article/details/127451962</guid><author>m0_72662900</author><pubDate>Fri, 21 Oct 2022 20:22:52 +0800</pubDate><description><![CDATA[机器学习——BP神经网络详细介绍及案例Python代码实现
BP（Back Propagation)网络 也称误差反向传播，1985年由Rumelhart 和 McCelland提出]]></description><category></category></item><item><title><![CDATA[特征工程——缺失值显示和填充（集中趋势（众数、平均数、中位数）、缺失值矩阵图、条形图、集中趋势填充）]]></title><link>https://blog.youkuaiyun.com/m0_72662900/article/details/127427564</link><guid>https://blog.youkuaiyun.com/m0_72662900/article/details/127427564</guid><author>m0_72662900</author><pubDate>Thu, 20 Oct 2022 15:48:42 +0800</pubDate><description><![CDATA[特征工程——缺失值显示和填充（集中趋势（众数、平均数、中位数）、缺失值矩阵图、条形图、集中趋势填充）
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