<?xml version="1.0" encoding="utf-8" ?><rss version="2.0"><channel><title><![CDATA[HorseRunningNoStop的博客]]></title><description><![CDATA[]]></description><link>https://blog.youkuaiyun.com/HorseRunningNoStop</link><language>zh-cn</language><generator>https://blog.youkuaiyun.com/</generator><copyright><![CDATA[Copyright &copy; HorseRunningNoStop]]></copyright><item><title><![CDATA[Jupyter 建立目录结构，便于查询]]></title><link>https://blog.youkuaiyun.com/HorseRunningNoStop/article/details/105448212</link><guid>https://blog.youkuaiyun.com/HorseRunningNoStop/article/details/105448212</guid><author>HorseRunningNoStop</author><pubDate>Sat, 11 Apr 2020 11:25:27 +0800</pubDate><description><![CDATA[1、打开Anoconda Prompt。

2、conda install -c conda-forge jupyter_contrib_nbextensions，更新选择"y"。

3、执行命令 jupyter contrib nbextensions install --user

4、打开jupyter，会多一个页签“Nbextensions”，点开，选择“Table of Content(...]]></description><category></category></item><item><title><![CDATA[转发：Octave] optimset()]]></title><link>https://blog.youkuaiyun.com/HorseRunningNoStop/article/details/104539482</link><guid>https://blog.youkuaiyun.com/HorseRunningNoStop/article/details/104539482</guid><author>HorseRunningNoStop</author><pubDate>Thu, 27 Feb 2020 16:47:35 +0800</pubDate><description><![CDATA[optimset('parameter', value, ...);
%设置所有参数及其值，未设置的为默认值

optimset(optimfun);
%设置与最优化函数有关的参数为默认

optimset(old, 'parmeter', 'value', ... );
%复制已有的设置，并修改特定项

optimset(old, new);
%两项合并



转自：https://blog...]]></description><category></category></item><item><title><![CDATA[机器学习：吴恩达老师课程的笔记资源]]></title><link>https://blog.youkuaiyun.com/HorseRunningNoStop/article/details/104420132</link><guid>https://blog.youkuaiyun.com/HorseRunningNoStop/article/details/104420132</guid><author>HorseRunningNoStop</author><pubDate>Thu, 20 Feb 2020 22:57:29 +0800</pubDate><description><![CDATA[黄海广博士的github网址，供后续学习用。

https://github.com/fengdu78
]]></description><category></category></item><item><title><![CDATA[python知识系列梳理一]]></title><link>https://blog.youkuaiyun.com/HorseRunningNoStop/article/details/104366468</link><guid>https://blog.youkuaiyun.com/HorseRunningNoStop/article/details/104366468</guid><author>HorseRunningNoStop</author><pubDate>Mon, 17 Feb 2020 22:30:06 +0800</pubDate><description><![CDATA[2019年培训了4个多月的大数据应用，几个月不应用，又生疏了很多。因此，希望能重新进行快速整理，以期达到温习的效果。


#python 基础
print('Hello World!')
Hello World!
print('*'*32)
print('你的姓名')
print('生日')
print('你最喜欢的颜色')
print('*'*32)
**********************...]]></description><category></category></item><item><title><![CDATA[学习笔记：用K值聚类预测手写体]]></title><link>https://blog.youkuaiyun.com/HorseRunningNoStop/article/details/80429733</link><guid>https://blog.youkuaiyun.com/HorseRunningNoStop/article/details/80429733</guid><author>HorseRunningNoStop</author><pubDate>Thu, 24 May 2018 09:26:10 +0800</pubDate><description><![CDATA[用ARI来评价聚类性能。代码如下：# -*- coding: utf-8 -*-
&quot;&quot;&quot;
Created on Tue May 22 14:49:02 2018

@author: eagle
&quot;&quot;&quot;
# =============================================================================
# K均值算法实现的数据聚类
# ==...]]></description><category></category></item><item><title><![CDATA[学习笔记：集成模型（回归）预测波士顿房价]]></title><link>https://blog.youkuaiyun.com/HorseRunningNoStop/article/details/80404740</link><guid>https://blog.youkuaiyun.com/HorseRunningNoStop/article/details/80404740</guid><author>HorseRunningNoStop</author><pubDate>Tue, 22 May 2018 13:54:15 +0800</pubDate><description><![CDATA[代码如下：# -*- coding: utf-8 -*-
&quot;&quot;&quot;
Created on Tue May 22 09:51:48 2018

@author: eagle
&quot;&quot;&quot;
# =============================================================================
# 集成模型预测波士顿房价：普通随机森林、极度随机森林、梯队上...]]></description><category></category></item><item><title><![CDATA[学习笔记：用回归树预测波士顿房价]]></title><link>https://blog.youkuaiyun.com/HorseRunningNoStop/article/details/80402532</link><guid>https://blog.youkuaiyun.com/HorseRunningNoStop/article/details/80402532</guid><author>HorseRunningNoStop</author><pubDate>Tue, 22 May 2018 10:45:37 +0800</pubDate><description><![CDATA[代码如下：# -*- coding: utf-8 -*-
&quot;&quot;&quot;
Created on Tue May 22 09:36:42 2018

@author: eagle
&quot;&quot;&quot;


# =============================================================================
# 回归树预测波士顿房价
# ==============...]]></description><category></category></item><item><title><![CDATA[学习笔记：不同K近邻回归预测波士顿房价]]></title><link>https://blog.youkuaiyun.com/HorseRunningNoStop/article/details/80397631</link><guid>https://blog.youkuaiyun.com/HorseRunningNoStop/article/details/80397631</guid><author>HorseRunningNoStop</author><pubDate>Mon, 21 May 2018 20:49:11 +0800</pubDate><description><![CDATA[代码如下：# -*- coding: utf-8 -*-
&quot;&quot;&quot;
Created on Mon May 21 15:52:45 2018

@author: eagle
&quot;&quot;&quot;

# =============================================================================
# 不同K近邻回归预测波士顿房价
# ===========...]]></description><category></category></item><item><title><![CDATA[学习笔记：用不同核函数的支持向量机预测波士顿房价]]></title><link>https://blog.youkuaiyun.com/HorseRunningNoStop/article/details/80394096</link><guid>https://blog.youkuaiyun.com/HorseRunningNoStop/article/details/80394096</guid><author>HorseRunningNoStop</author><pubDate>Mon, 21 May 2018 16:45:06 +0800</pubDate><description><![CDATA[代码如下：# -*- coding: utf-8 -*-
&quot;&quot;&quot;
Created on Tue May 15 17:23:42 2018

@author: eagle
&quot;&quot;&quot;
# =============================================================================
# 不同核函数的支持向量机预测波士顿房价
# ========...]]></description><category></category></item><item><title><![CDATA[学习笔记：用线性回归和梯度提升预测波士顿房价]]></title><link>https://blog.youkuaiyun.com/HorseRunningNoStop/article/details/80383149</link><guid>https://blog.youkuaiyun.com/HorseRunningNoStop/article/details/80383149</guid><author>HorseRunningNoStop</author><pubDate>Sun, 20 May 2018 16:22:11 +0800</pubDate><description><![CDATA[# -*- coding: utf-8 -*-
&quot;&quot;&quot;
Created on Tue May 15 17:23:42 2018

@author: eagle
&quot;&quot;&quot;
# =============================================================================
# 线性回归预测波士顿房价
# ====================...]]></description><category></category></item><item><title><![CDATA[学习笔记：用集成模型预测泰坦尼克号伤亡情况（与单一决策树进行对比）]]></title><link>https://blog.youkuaiyun.com/HorseRunningNoStop/article/details/80321163</link><guid>https://blog.youkuaiyun.com/HorseRunningNoStop/article/details/80321163</guid><author>HorseRunningNoStop</author><pubDate>Tue, 15 May 2018 12:32:17 +0800</pubDate><description><![CDATA[代码如下:# -*- coding: utf-8 -*-
&quot;&quot;&quot;
Created on Mon May 14 09:29:48 2018

@author: eagle
&quot;&quot;&quot;

# =============================================================================
# 单一决策树与集成模型（随机森林分类器和梯度提升决策树）对...]]></description><category></category></item><item><title><![CDATA[学习笔记：用决策树预测泰坦尼克号伤亡情况]]></title><link>https://blog.youkuaiyun.com/HorseRunningNoStop/article/details/80312108</link><guid>https://blog.youkuaiyun.com/HorseRunningNoStop/article/details/80312108</guid><author>HorseRunningNoStop</author><pubDate>Mon, 14 May 2018 17:01:07 +0800</pubDate><description><![CDATA[# -*- coding: utf-8 -*-
&quot;&quot;&quot;
Created on Mon May 14 09:29:48 2018

@author: eagle
&quot;&quot;&quot;

# =============================================================================
# 用决策树预测泰坦尼克号伤亡情况
# ===============...]]></description><category></category></item><item><title><![CDATA[学习笔记：K近邻预测Iris（鸢尾）分类]]></title><link>https://blog.youkuaiyun.com/HorseRunningNoStop/article/details/80279558</link><guid>https://blog.youkuaiyun.com/HorseRunningNoStop/article/details/80279558</guid><author>HorseRunningNoStop</author><pubDate>Fri, 11 May 2018 11:55:43 +0800</pubDate><description><![CDATA[代码如下：# -*- coding: utf-8 -*-
&quot;&quot;&quot;
Created on Fri May 11 10:26:58 2018

@author: eagle
&quot;&quot;&quot;

# =============================================================================
# 用K近邻预测Iris(鸢尾)数据集
# ========...]]></description><category></category></item><item><title><![CDATA[学习笔记：支持向量机预测数字]]></title><link>https://blog.youkuaiyun.com/HorseRunningNoStop/article/details/80264724</link><guid>https://blog.youkuaiyun.com/HorseRunningNoStop/article/details/80264724</guid><author>HorseRunningNoStop</author><pubDate>Thu, 10 May 2018 11:08:30 +0800</pubDate><description><![CDATA[# -*- coding: utf-8 -*-
&quot;&quot;&quot;
Created on Tue May  8 16:19:00 2018

@author: eagle
&quot;&quot;&quot;

from sklearn.datasets import load_digits
from sklearn.cross_validation import train_test_split
from sklearn.preproc...]]></description><category></category></item><item><title><![CDATA[学习笔记：使用Logistic回归和随机梯队下降预测良/恶性肿瘤]]></title><link>https://blog.youkuaiyun.com/HorseRunningNoStop/article/details/80241306</link><guid>https://blog.youkuaiyun.com/HorseRunningNoStop/article/details/80241306</guid><author>HorseRunningNoStop</author><pubDate>Tue, 08 May 2018 16:18:18 +0800</pubDate><description><![CDATA[# -*- coding: utf-8 -*-
&quot;&quot;&quot;
Created on Wed Apr 25 17:07:55 2018

@author: eagle
&quot;&quot;&quot;

import pandas as pd
import numpy as np
import requests
import io
from sklearn.cross_validation import train_test_sp...]]></description><category></category></item><item><title><![CDATA[学习笔记：Spyder快捷键]]></title><link>https://blog.youkuaiyun.com/HorseRunningNoStop/article/details/80117864</link><guid>https://blog.youkuaiyun.com/HorseRunningNoStop/article/details/80117864</guid><author>HorseRunningNoStop</author><pubDate>Sat, 28 Apr 2018 10:58:19 +0800</pubDate><description><![CDATA[Ctrl +1：单行注释/取消注释Ctrl+4：多行包裹注释Ctrl+5：取消多行包裹注释Ctrl+d：删除一行f12：设置断点f11：全屏f5：运行]]></description><category></category></item><item><title><![CDATA[Anconda自带第三包使用]]></title><link>https://blog.youkuaiyun.com/HorseRunningNoStop/article/details/80095948</link><guid>https://blog.youkuaiyun.com/HorseRunningNoStop/article/details/80095948</guid><author>HorseRunningNoStop</author><pubDate>Thu, 26 Apr 2018 18:50:01 +0800</pubDate><description><![CDATA[因为之前已经安装了Anaconda，并且创建了一个新的环境tensorFlow，所以在Anaconda文件夹下就多了一个Spyder(tensorflow)。见下图。在Anaconda Prompt中使用conda list，可以查看自带的第三方包。需要使用这些自带的安装包，例如需要使用numpy，需要在Anaconda Navigator下Enviroment-&amp;gt;tensorflow-&amp;g...]]></description><category></category></item><item><title><![CDATA[学习笔记一：机器学习的基本概念]]></title><link>https://blog.youkuaiyun.com/HorseRunningNoStop/article/details/80016076</link><guid>https://blog.youkuaiyun.com/HorseRunningNoStop/article/details/80016076</guid><author>HorseRunningNoStop</author><pubDate>Wed, 25 Apr 2018 17:06:16 +0800</pubDate><description><![CDATA[算法选择原则：机器学习需要预测目标变量值，选择监督学习算法；否则，选择无监督学习算法。监督学习算法中，目标变量值为离散值，选用分类算法；目标变量值为连续的数值，选用回归。无监督学习算法中，若只需将数据划分为离散的租，则采用聚类算法；若还需估计数据与各个分组的相似程度，需要使用密度估计算法。算法选择的关键是：反复试错的迭代过程。学习数据：要了解数据：特征值是离散变量还是连续变量；特征值中是否存在缺失...]]></description><category></category></item><item><title><![CDATA[开启人工智能（机器学习）之路---随笔]]></title><link>https://blog.youkuaiyun.com/HorseRunningNoStop/article/details/80003026</link><guid>https://blog.youkuaiyun.com/HorseRunningNoStop/article/details/80003026</guid><author>HorseRunningNoStop</author><pubDate>Thu, 19 Apr 2018 12:13:09 +0800</pubDate><description><![CDATA[今天双宝快4个月了，而我距离产假结束也只剩下1个多月了。为了不让自己陷入产后抑郁状态，闲暇之余开始看点书。另一半闲着无聊买了很多书，我也就顺手拿起来看喽，主要是人工智能、大数据、机器学习、深度学习、算法等方面。之前上班的时候总觉得这块很高深，真正看了之后发现，哈哈，确实很高深，但是却并没有想象中的完全不懂，因为大学期间很爱参加数学建模竞赛，所以对于一些算法，虽未深究，但起码也听说过。后来一看，很多...]]></description><category></category></item><item><title><![CDATA[Win10下用Anaconda安装TensorFlow]]></title><link>https://blog.youkuaiyun.com/HorseRunningNoStop/article/details/79506530</link><guid>https://blog.youkuaiyun.com/HorseRunningNoStop/article/details/79506530</guid><author>HorseRunningNoStop</author><pubDate>Sat, 10 Mar 2018 11:51:40 +0800</pubDate><description><![CDATA[转载自：http://blog.youkuaiyun.com/u010858605/article/details/64128466为了保存，故转载之。笔者之前在学习TensorFlow，也在自己的笔记本上完成了安装，在PyCharm中进行学习。但是最近为了使用python的科学计算环境，我把之前的环境卸载了，并用Anaconda重新安装了TensorFlow，由于自己的笔记本已经很旧了，显卡不行，所以这里介...]]></description><category></category></item></channel></rss>