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<!DOCTYPE html><html lang="zh-CN" data-theme="light"><head><meta charset="UTF-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width,initial-scale=1"><title>HLY-Blog - 保持少年，精神富足</title><meta name="author" content="HLY"><meta name="copyright" content="HLY"><meta name="format-detection" content="telephone=no"><meta name="theme-color" content="#ffffff"><meta http-equiv="Cache-Control" content="no-transform"><meta http-equiv="Cache-Control" content="no-siteapp"><meta name="description" content="人生未完成...">
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href="/2022/01/14/machine-learning%EF%BC%88%E4%B8%89%EF%BC%89/" title="machine-learning（三）"> <img class="post_bg" data-lazy-src="https://img0.baidu.com/it/u=728045523,3008166041&fm=26&fmt=auto" onerror="this.onerror=null;this.src='/img/404.jpg'" alt="machine-learning（三）"></a></div><div class="recent-post-info"><a class="article-title" href="/2022/01/14/machine-learning%EF%BC%88%E4%B8%89%EF%BC%89/" title="machine-learning（三）">machine-learning（三）</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">发表于</span><time datetime="2022-01-14T08:47:39.000Z" title="发表于 2022-01-14 16:47:39">2022-01-14</time></span><span class="article-meta"><span class="article-meta__separator">|</span><i class="fas fa-inbox article-meta__icon"></i><a class="article-meta__categories" href="/categories/AI/">AI</a></span><span class="article-meta tags"><span class="article-meta__separator">|</span><i class="fas fa-tag article-meta__icon"></i><a class="article-meta__tags" href="/tags/Neural-Network/">Neural Network</a></span></div><div class="content">神经网络1.1模型表示神经网络模型建立在很多神经元之上，每一个神经元又是一个个学习模型。这些神经元（也叫激活单元，activation unit）采纳一些特征作为输出，并且根据本身的模型提供一个输出。下图是一个以逻辑回归模型作为自身学习模型的神经元示例，在神经网络中，参数又可被成为权重（weight）。
x_0 是偏置单元，一般每一层都会加入一个偏置单元（也称作偏置项）
（以下解释参见：https://www.zhihu.com/question/305340182/answer/721739423）
在神经网络中，以sigmoid函数为例，加入偏置后也是增加了函数的灵活性，提高了神经元的拟合能力。
在神经元中，output = sum (weights * inputs) + bias。偏置实际上是对神经元激活状态的控制。比如在上图中，当偏置为20时，x较小时y的值就可以很大，就可以较快的将该神经元激活。
以此设计出类似神经元的神经网络：
神经网络模型是许多逻辑单元按照不同层级组织起来的网络，每一层的输出变量都是下一层的输入变量。下图为一个3层的神经网络，第一层成为输入 ...</div></div></div><div class="recent-post-item"><div class="post_cover right_radius"><a href="/2021/12/31/%E5%B9%B4%E7%BB%88%E6%80%BB%E7%BB%93/" title="年终总结"> <img class="post_bg" data-lazy-src="https://pica.zhimg.com/80/v2-1b71cad1e84d680d42d5d253aaa2719f_720w.jpg?source=1940ef5c" onerror="this.onerror=null;this.src='/img/404.jpg'" alt="年终总结"></a></div><div class="recent-post-info"><a class="article-title" href="/2021/12/31/%E5%B9%B4%E7%BB%88%E6%80%BB%E7%BB%93/" title="年终总结">年终总结</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">发表于</span><time datetime="2021-12-31T10:23:30.000Z" title="发表于 2021-12-31 18:23:30">2021-12-31</time></span><span class="article-meta tags"><span class="article-meta__separator">|</span><i class="fas fa-tag article-meta__icon"></i><a class="article-meta__tags" href="/tags/%E6%80%BB%E7%BB%93/">总结</a></span></div><div class="content">Hi，这是我2021年的年终总结在2021年的尾巴处，回顾一下这平凡但又不平静的一年吧。
2021 年初，应该我还是大二上的小萌新吧，那时还沉溺于后端开发，一心想着大二下能去找个厂上班，回顾这一年，算是把不去上课，期末补课这一宗旨贯彻到底了哈哈哈哈（虽然现在有点后悔），每天去打卡都是逆人流而行嗷，还挺尴尬的，大伙都是去上课，就只有我往回走去大活。
我准备把我的2021年终总结分为两个方面：生活 + 学业
生活生活方面最记忆犹新的应该就是终于算是比较有计划的健身了吧，经过一年自己乱练摸索以后，算是比较系统的有了一个健身计划，并且坚持了下来（因为西安疫情原因，都一个月没健身了，小痛苦）。自己体重也有了变化，长胖了不少，身材也没那么看上去一吹就倒。
然后呢，就是居然有幸在西安见了两次雪景（一次是2021年初，一次是2021年末），对于一个南方人来说，挺幸福的哈哈哈，附几张图：
2021年咱没去啥地方，基本是家里蹲了，寒假记不太清了，好像在家里呆的浑浑噩噩的，暑假的话有在忙事情，唯一出去玩的时候是国庆节去长沙找朋友耍了几天，长沙热是真的热啊啊！
总的来说，生活上并无太大变化，唯一 ...</div></div></div><div class="recent-post-item"><div class="post_cover left_radius"><a href="/2021/12/29/machine-learning%EF%BC%88%E4%BA%8C%EF%BC%89/" title="machine-learning（二）"> <img class="post_bg" data-lazy-src="https://img0.baidu.com/it/u=728045523,3008166041&fm=26&fmt=auto" onerror="this.onerror=null;this.src='/img/404.jpg'" alt="machine-learning（二）"></a></div><div class="recent-post-info"><a class="article-title" href="/2021/12/29/machine-learning%EF%BC%88%E4%BA%8C%EF%BC%89/" title="machine-learning（二）">machine-learning（二）</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">发表于</span><time datetime="2021-12-29T14:47:39.000Z" title="发表于 2021-12-29 22:47:39">2021-12-29</time></span><span class="article-meta"><span class="article-meta__separator">|</span><i class="fas fa-inbox article-meta__icon"></i><a class="article-meta__categories" href="/categories/AI/">AI</a></span><span class="article-meta tags"><span class="article-meta__separator">|</span><i class="fas fa-tag article-meta__icon"></i><a class="article-meta__tags" href="/tags/logistic-regression/">logistic regression</a></span></div><div class="content">一、逻辑回归（logistic regression）1.1 假说表示根据线性回归模型我们只能预测连续的值，然而对于分类问题，我们需要输出0或1，我们可以预测：
当${h_\theta}\left( x \right)>=0.5$时，预测 $y=1$。
当${h_\theta}\left( x \right)<0.5$时，预测 $y=0$ 。
我们引入一个新的模型，逻辑回归，该模型的输出变量范围始终在0和1之间。
逻辑回归模型的假设是： $h_\theta \left( x \right)=g\left(\theta^{T}X \right)$
其中：
$X$ 代表特征向量
$g$ 代表逻辑函数（logistic function)是一个常用的逻辑函数为S形函数（Sigmoid function），公式为： $g\left( z \right)=\frac{1}{1+{{e}^{-z}}}$。
123import numpy as npdef sigmod(z): return 1 / (1 + np.exp(-z))
函数图像为：
$h_\theta \left ...</div></div></div><div class="recent-post-item"><div class="post_cover right_radius"><a href="/2021/12/21/machine-learning/" title="machine-learning（一）"> <img class="post_bg" data-lazy-src="https://img0.baidu.com/it/u=728045523,3008166041&fm=26&fmt=auto" onerror="this.onerror=null;this.src='/img/404.jpg'" alt="machine-learning（一）"></a></div><div class="recent-post-info"><a class="article-title" href="/2021/12/21/machine-learning/" title="machine-learning（一）">machine-learning（一）</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">发表于</span><time datetime="2021-12-21T14:47:39.000Z" title="发表于 2021-12-21 22:47:39">2021-12-21</time></span><span class="article-meta"><span class="article-meta__separator">|</span><i class="fas fa-inbox article-meta__icon"></i><a class="article-meta__categories" href="/categories/AI/">AI</a></span><span class="article-meta tags"><span class="article-meta__separator">|</span><i class="fas fa-tag article-meta__icon"></i><a class="article-meta__tags" href="/tags/linear-regression/">linear regression</a></span></div><div class="content">一、简介1.2 机器学习是什么？近一点的定义，由Tom Mitchell提出，来自卡内基梅隆大学，Tom定义的机器学习是，一个好的学习问题定义如下，他说，一个程序被认为能从经验E中学习，解决任务T，达到性能度量值P，当且仅当，有了经验E后，经过P评判，程序在处理T时的性能有所提升。我认为经验E 就是程序上万次的自我练习的经验而任务T 就是下棋。性能度量值P呢，就是它在与一些新的对手比赛时，赢得比赛的概率。
1.3 监督学习横轴表示房子的面积，单位是平方英尺，纵轴表示房价，单位是千美元。那基于这组数据，假如你有一个朋友，他有一套750平方英尺房子，现在他希望把房子卖掉，他想知道这房子能卖多少钱。
我们应用学习算法，可以在这组数据中画一条直线，或者换句话说，拟合一条直线，根据这条线我们可以推测出，这套房子可能卖$150,000，当然这不是唯一的算法。可能还有更好的，比如我们不用直线拟合这些数据，用二次方程去拟合可能效果会更好。根据二次方程的曲线，我们可以从这个点推测出，这套房子能卖接近$200,000。
可以看出，监督学习指的就是我们给学习算法一个数据集。这个数据集由“正确答案”组成。在 ...</div></div></div><div class="recent-post-item"><div class="post_cover left_radius"><a href="/2021/09/06/%E8%AE%BE%E8%AE%A1%E6%A8%A1%E5%BC%8F/" title="设计模式"> <img class="post_bg" data-lazy-src="https://pic4.zhimg.com/80/v2-3c93265c69c1e73580fcaaa6ea83d8db_720w.jpg" onerror="this.onerror=null;this.src='/img/404.jpg'" alt="设计模式"></a></div><div class="recent-post-info"><a class="article-title" href="/2021/09/06/%E8%AE%BE%E8%AE%A1%E6%A8%A1%E5%BC%8F/" title="设计模式">设计模式</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">发表于</span><time datetime="2021-09-06T02:20:15.000Z" title="发表于 2021-09-06 10:20:15">2021-09-06</time></span><span class="article-meta"><span class="article-meta__separator">|</span><i class="fas fa-inbox article-meta__icon"></i><a class="article-meta__categories" href="/categories/%E9%9D%A2%E8%AF%95%E5%9F%BA%E7%A1%80/">面试基础</a></span><span class="article-meta tags"><span class="article-meta__separator">|</span><i class="fas fa-tag article-meta__icon"></i><a class="article-meta__tags" href="/tags/%E8%AE%BE%E8%AE%A1%E6%A8%A1%E5%BC%8F/">设计模式</a></span></div><div class="content">设计模式单例模式（Singletion）
Intent确保一个类只有一个实例，并提供该实例的全局访问点
Class Diagram使用一个私有构造函数、一个私有静态变量以及一个公有静态函数来实现。
私有构造函数保证了不能通过构造函数来创建对象实例，只能通过公有静态函数返回唯一的私有静态变量。
双重校验锁-线程安全123456789101112131415161718public class Singleton{ private volatile static Singleton uniqueInstance; private Singletion(){ } public static Singleton getUniqurInstance(){ if(uniqueInstance == null){ synchronized(Singleton.class){ if(uniqueInstance == n ...</div></div></div><div class="recent-post-item"><div class="post_cover right_radius"><a href="/2021/05/31/%E7%AE%97%E6%B3%95%E6%80%BB%E7%BB%93/" title="算法总结"> <img class="post_bg" data-lazy-src="https://pic1.zhimg.com/80/v2-39991d03db9215ec8781bc774341034c_720w.jpg" onerror="this.onerror=null;this.src='/img/404.jpg'" alt="算法总结"></a></div><div class="recent-post-info"><a class="article-title" href="/2021/05/31/%E7%AE%97%E6%B3%95%E6%80%BB%E7%BB%93/" title="算法总结">算法总结</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">发表于</span><time datetime="2021-05-31T02:53:05.000Z" title="发表于 2021-05-31 10:53:05">2021-05-31</time></span><span class="article-meta tags"><span class="article-meta__separator">|</span><i class="fas fa-tag article-meta__icon"></i><a class="article-meta__tags" href="/tags/%E7%AE%97%E6%B3%95/">算法</a></span></div><div class="content">一、快速排序算法（leetcode复盘： 8）==主要思想==
从序列中，任选一个记录k作为轴值pivot
选择策略
第一个元素
最后一个元素
中间元素
将数组元素，分割成左子序列L和右子序列R
L 中所有元素都 < k， R 中所有元素都 > k
对 L 和 R递归进行快排，直到子序列中有 0 个 或者 1 个元素，退出
通过画图理解算法
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869/** * @author hly * @Description: 分治思想：快速排序算法实现 * @create 2021-05-31 11:13 */public class 快速排序 { public static void main(String[] args) { int[] n = {1,5,6,3,2,8,9&# ...</div></div></div><div class="recent-post-item"><div class="post_cover left_radius"><a href="/2021/04/22/Leetcode%E5%A4%8D%E7%9B%98/" title="Leetcode复盘"> <img class="post_bg" data-lazy-src="https://pic4.zhimg.com/80/v2-2d53a7b90e55544f9b59dd46768052d1_720w.jpg?source=1940f5c" onerror="this.onerror=null;this.src='/img/404.jpg'" alt="Leetcode复盘"></a></div><div class="recent-post-info"><a class="article-title" href="/2021/04/22/Leetcode%E5%A4%8D%E7%9B%98/" title="Leetcode复盘">Leetcode复盘</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">发表于</span><time datetime="2021-04-22T01:58:33.000Z" title="发表于 2021-04-22 09:58:33">2021-04-22</time></span><span class="article-meta"><span class="article-meta__separator">|</span><i class="fas fa-inbox article-meta__icon"></i><a class="article-meta__categories" href="/categories/%E7%AE%97%E6%B3%95/">算法</a></span><span class="article-meta tags"><span class="article-meta__separator">|</span><i class="fas fa-tag article-meta__icon"></i><a class="article-meta__tags" href="/tags/leetcode/">leetcode</a></span></div><div class="content">DP动态规划9.给定一个整数数组 nums ，找到一个具有最大和的连续子数组（子数组最少包含一个元素），返回其最大和。要点：
连续子数组
最大和
思路：
整数数组有正有负
sum 保存动态最大和，又可能出现负数情况
动态规划，ans 保存最后结果
12345678910111213141516171819202122232425package src.day4;/** * 给定一个整数数组 nums ，找到一个具有最大和的连续子数组（子数组最少包含一个元素），返回其最大和。 * @author han long yi * @create 2021-04-04 10:43 */public class max { public int maxSubArray(int[] nums) { int sum = 0; // 保存结果 int ans = nums[0]; for(int num : nums){ // 如果sum为负数，直接赋值num i ...</div></div></div><div class="recent-post-item"><div class="post_cover right_radius"><a href="/2021/03/10/%E7%BB%83%E6%89%8B%E8%AE%B0%E5%BD%95/" title="练手记录"> <img class="post_bg" data-lazy-src="https://pic1.zhimg.com/80/v2-39991d03db9215ec8781bc774341034c_720w.jpg" onerror="this.onerror=null;this.src='/img/404.jpg'" alt="练手记录"></a></div><div class="recent-post-info"><a class="article-title" href="/2021/03/10/%E7%BB%83%E6%89%8B%E8%AE%B0%E5%BD%95/" title="练手记录">练手记录</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">发表于</span><time datetime="2021-03-10T07:06:11.000Z" title="发表于 2021-03-10 15:06:11">2021-03-10</time></span><span class="article-meta"><span class="article-meta__separator">|</span><i class="fas fa-inbox article-meta__icon"></i><a class="article-meta__categories" href="/categories/%E6%97%A5%E5%B8%B8/">日常</a></span><span class="article-meta tags"><span class="article-meta__separator">|</span><i class="fas fa-tag article-meta__icon"></i><a class="article-meta__tags" href="/tags/%E5%B0%8F%E9%A1%B9%E7%9B%AE/">小项目</a></span></div><div class="content">写在前言：主要记录练手小项目，当作一个mad文档，也同时记录一些遇到的问题和总结经验。
1.项目准备 需求分析 模块 功能
库表设计 数据库
详细设计 流程图伪代码的方式
编码环节：
a.环境准备
b.正式进入编码环节
测试 部署上线
2.技术选型 前端：vue + axios
后端：spring boot + mybatis + mysql + tomcat +Redis
3.需求分析用户模块 a.用户登录
b.用户注册
c.验证码实现
d.欢迎xx用户展示
e.安全退出
f.员工列表展示
员工模块 g.员工添加
h.员工删除
i.员工修改
j.员工列表加入Redis缓存实现
4 ...</div></div></div><div class="recent-post-item"><div class="post_cover left_radius"><a href="/2020/12/01/token%E3%80%81session%E3%80%81cookie/" title="token、session、cookie那些事"> <img class="post_bg" data-lazy-src="https://pic1.zhimg.com/80/v2-e9553dfe572e71ba82811a3dad2def52_720w.jpg?source=1940ef5c" onerror="this.onerror=null;this.src='/img/404.jpg'" alt="token、session、cookie那些事"></a></div><div class="recent-post-info"><a class="article-title" href="/2020/12/01/token%E3%80%81session%E3%80%81cookie/" title="token、session、cookie那些事">token、session、cookie那些事</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">发表于</span><time datetime="2020-12-01T02:55:08.000Z" title="发表于 2020-12-01 10:55:08">2020-12-01</time></span><span class="article-meta"><span class="article-meta__separator">|</span><i class="fas fa-inbox article-meta__icon"></i><a class="article-meta__categories" href="/categories/JavaWeb/">JavaWeb</a></span><span class="article-meta tags"><span class="article-meta__separator">|</span><i class="fas fa-tag article-meta__icon"></i><a class="article-meta__tags" href="/tags/token/">token</a></span></div><div class="content">1、美好的旧时光我经常怀念三十年前那美好的旧时光， 工作很轻松， 生活很悠闲。
上班的时候偶尔有些HTTP的请求发到我这里， 我简单的看一下， 取出相对应的html文档，图片，发回去就可以了， 然后就可以继续喝茶聊天。
我的创造者们对我很严格， 他们制定的一个简单HTTP协议， 就是请求加响应， 尤其是我不用记住是谁刚刚发了HTTP请求， 每个请求对我来说都是全新的！
邮件服务器很羡慕我， 他说：老弟，你的生活太惬意了， 哪像我， 每次有人从客户端访问邮箱， 我都得专门给他建立一个会话， 来处理他发的消息， 你倒好， 完全不用管理会话。
这是由应用的特性决定的， 如果邮件服务器不管理会话， 那多个人之间的邮件消息就会完全混到一起了， 乱作一团了。
而30年前的Web 基本上就是文档的浏览而已， 既然是浏览，我作为一个服务器， 为什么要记住谁在一段时间里都浏览了什么文档呢？
2、Session但是好日子没持续多久， 很快大家就不满足于静态的Html 文档了， 交互式的Web应用开始兴起， 尤其是论坛， 在线购物等网站。
我马上就遇到了和邮件服务器一样的问题， 那就是必须管理会话，必 ...</div></div></div><div class="recent-post-item"><div class="post_cover right_radius"><a href="/2020/11/21/SCP/" title="SCP"> <img class="post_bg" data-lazy-src="https://pic1.zhimg.com/80/v2-f5b2680b072c7b5c62751d13832b7c87_720w.jpg?source=1940ef5c" onerror="this.onerror=null;this.src='/img/404.jpg'" alt="SCP"></a></div><div class="recent-post-info"><a class="article-title" href="/2020/11/21/SCP/" title="SCP">SCP</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">发表于</span><time datetime="2020-11-21T03:50:33.000Z" title="发表于 2020-11-21 11:50:33">2020-11-21</time></span><span class="article-meta"><span class="article-meta__separator">|</span><i class="fas fa-inbox article-meta__icon"></i><a class="article-meta__categories" href="/categories/scp/">scp</a></span><span class="article-meta tags"><span class="article-meta__separator">|</span><i class="fas fa-tag article-meta__icon"></i><a class="article-meta__tags" href="/tags/%E7%8B%82%E6%83%B3/">狂想</a></span></div><div class="content">回味无穷的SCP基金会系列记录一些很有趣的scp基金会文章。
scp基金会中国分部官网
scp基金会：
基金会宣言:
人类到如今已经繁衍了近百万年，只有最近的4000年是有意义的。
所以，我们在将近25000年中在干嘛？我们躲在山洞中，围坐在小小的篝火边，畏惧那些我们不懂得的事物——那些关于太阳如何升起的解释，那些人头鸟身的怪物，那些有生命的石头。所以我们称他们为“神”和“恶魔”，并向他们祈求宽恕和祈祷拯救。
之后，他们的数量在减少，我们的数量在增加。当我们恐惧的事物越来越少，我们开始更理智的看待这个世界。然而，不能解释的事物并没有消失，好像宇宙故意要表现出荒谬与不可思议一样。
人类不能再生活在恐惧中。 没有东西能保护我们，我们必须保护我们自己。
当其他人在阳光下生活时，我们必须在阴影中和它们战斗，并防止它们暴露在大众眼中，这样其他人才能生活在一个理智的，普通的世界中。
我们控制，我们收容，我们保护。
— The Administrator
那么scp基金会真的存在吗？？
项目编号：SCP-1733
项目等级：Safe
特殊收容措施：收录有SCP-1733的硬盘录像机被收容于Sit ...</div></div></div><nav id="pagination"><div class="pagination"><span class="page-number current">1</span><a class="page-number" href="/page/2/">2</a><a class="extend next" rel="next" href="/page/2/"><i class="fas fa-chevron-right fa-fw"></i></a></div></nav></div><div class="aside_content" id="aside_content"><div class="card-widget card-info"><div class="card-content"><div class="card-info-avatar is-center"><img class="avatar-img" data-lazy-src="/img/tubiao.jpg" onerror="this.onerror=null;this.src='/img/friend_404.gif'" alt="avatar"/><div class="author-info__name">HLY</div><div class="author-info__description">人生未完成...</div></div><div class="card-info-data"><div class="card-info-data-item is-center"><a href="/archives/"><div class="headline">文章</div><div class="length-num">16</div></a></div><div class="card-info-data-item is-center"><a href="/tags/"><div class="headline">标签</div><div class="length-num">14</div></a></div><div class="card-info-data-item 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href="/2021/12/29/machine-learning%EF%BC%88%E4%BA%8C%EF%BC%89/" title="machine-learning（二）">machine-learning（二）</a><time datetime="2021-12-29T14:47:39.000Z" title="发表于 2021-12-29 22:47:39">2021-12-29</time></div></div><div class="aside-list-item"><a class="thumbnail" href="/2021/12/21/machine-learning/" title="machine-learning（一）"><img data-lazy-src="https://img0.baidu.com/it/u=728045523,3008166041&fm=26&fmt=auto" onerror="this.onerror=null;this.src='/img/404.jpg'" alt="machine-learning（一）"/></a><div class="content"><a class="title" href="/2021/12/21/machine-learning/" title="machine-learning（一）">machine-learning（一）</a><time datetime="2021-12-21T14:47:39.000Z" title="发表于 2021-12-21 22:47:39">2021-12-21</time></div></div><div class="aside-list-item"><a class="thumbnail" href="/2021/09/06/%E8%AE%BE%E8%AE%A1%E6%A8%A1%E5%BC%8F/" title="设计模式"><img data-lazy-src="https://pic4.zhimg.com/80/v2-3c93265c69c1e73580fcaaa6ea83d8db_720w.jpg" onerror="this.onerror=null;this.src='/img/404.jpg'" alt="设计模式"/></a><div class="content"><a class="title" href="/2021/09/06/%E8%AE%BE%E8%AE%A1%E6%A8%A1%E5%BC%8F/" title="设计模式">设计模式</a><time datetime="2021-09-06T02:20:15.000Z" title="发表于 2021-09-06 10:20:15">2021-09-06</time></div></div></div></div></div><div class="card-widget card-categories"><div class="card-content"><div class="item-headline"><i class="fas fa-folder-open"></i><span>分类</span></div><ul class="card-category-list" id="aside-cat-list">
<li class="card-category-list-item "><a class="card-category-list-link" href="/categories/AI/"><span class="card-category-list-name">AI</span><span class="card-category-list-count">3</span></a></li><li class="card-category-list-item "><a class="card-category-list-link" href="/categories/Java/"><span class="card-category-list-name">Java</span><span class="card-category-list-count">1</span></a></li><li class="card-category-list-item "><a class="card-category-list-link" href="/categories/JavaWeb/"><span class="card-category-list-name">JavaWeb</span><span class="card-category-list-count">1</span></a></li><li class="card-category-list-item "><a class="card-category-list-link" href="/categories/Redis/"><span class="card-category-list-name">Redis</span><span class="card-category-list-count">1</span></a></li><li class="card-category-list-item "><a class="card-category-list-link" href="/categories/scp/"><span class="card-category-list-name">scp</span><span class="card-category-list-count">1</span></a></li><li class="card-category-list-item "><a class="card-category-list-link" href="/categories/%E6%97%A5%E5%B8%B8/"><span class="card-category-list-name">日常</span><span class="card-category-list-count">3</span></a></li><li class="card-category-list-item "><a class="card-category-list-link" href="/categories/%E7%AE%97%E6%B3%95/"><span class="card-category-list-name">算法</span><span class="card-category-list-count">1</span></a></li><li class="card-category-list-item "><a class="card-category-list-link" href="/categories/%E9%9A%8F%E7%AC%94/"><span class="card-category-list-name">随笔</span><span class="card-category-list-count">1</span></a></li>
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</ul></div></div><div class="card-widget card-tags"><div class="card-content"><div class="item-headline"><i class="fas fa-tags"></i><span>标签</span></div><div class="card-tag-cloud"><a href="/tags/%E6%80%BB%E7%BB%93/" style="font-size: 1.5em; color: rgb(96, 7, 87)">总结</a><a href="/tags/Neural-Network/" style="font-size: 1.1em; color: rgb(22, 86, 173)">Neural Network</a><a href="/tags/logistic-regression/" style="font-size: 1.1em; color: rgb(88, 135, 43)">logistic regression</a><a href="/tags/%E5%B0%91%E5%B9%B4/" style="font-size: 1.1em; color: rgb(27, 1, 51)">少年</a><a href="/tags/token/" style="font-size: 1.1em; color: rgb(100, 39, 179)">token</a><a href="/tags/%E7%AE%97%E6%B3%95/" style="font-size: 1.1em; color: rgb(172, 63, 163)">算法</a><a href="/tags/%E5%B0%8F%E9%A1%B9%E7%9B%AE/" style="font-size: 1.1em; color: rgb(5, 48, 108)">小项目</a><a href="/tags/%E5%BB%B6%E6%97%B6%E9%98%9F%E5%88%97/" style="font-size: 1.1em; color: rgb(176, 85, 63)">延时队列</a><a href="/tags/%E8%AE%BE%E8%AE%A1%E6%A8%A1%E5%BC%8F/" style="font-size: 1.1em; color: rgb(108, 18, 175)">设计模式</a><a href="/tags/leetcode/" style="font-size: 1.1em; color: rgb(0, 169, 154)">leetcode</a><a href="/tags/%E7%8B%82%E6%83%B3/" style="font-size: 1.1em; color: rgb(30, 11, 152)">狂想</a><a href="/tags/linear-regression/" style="font-size: 1.1em; color: rgb(160, 194, 11)">linear regression</a><a href="/tags/%E5%8A%B1%E5%BF%97/" style="font-size: 1.1em; color: rgb(169, 148, 83)">励志</a><a href="/tags/%E6%95%B0%E6%8D%AE%E5%BA%93/" style="font-size: 1.1em; color: rgb(125, 155, 27)">数据库</a></div></div></div><div class="card-widget card-archives"><div class="card-content"><div class="item-headline"><i class="fas fa-archive"></i><span>归档</span></div><ul class="card-archive-list"><li class="card-archive-list-item"><a class="card-archive-list-link" href="/archives/2022/01/"><span class="card-archive-list-date">一月 2022</span><span class="card-archive-list-count">1</span></a></li><li class="card-archive-list-item"><a class="card-archive-list-link" href="/archives/2021/12/"><span class="card-archive-list-date">十二月 2021</span><span class="card-archive-list-count">3</span></a></li><li class="card-archive-list-item"><a class="card-archive-list-link" href="/archives/2021/09/"><span class="card-archive-list-date">九月 2021</span><span class="card-archive-list-count">1</span></a></li><li class="card-archive-list-item"><a class="card-archive-list-link" href="/archives/2021/05/"><span class="card-archive-list-date">五月 2021</span><span class="card-archive-list-count">1</span></a></li><li class="card-archive-list-item"><a class="card-archive-list-link" href="/archives/2021/04/"><span class="card-archive-list-date">四月 2021</span><span class="card-archive-list-count">1</span></a></li><li class="card-archive-list-item"><a class="card-archive-list-link" href="/archives/2021/03/"><span class="card-archive-list-date">三月 2021</span><span class="card-archive-list-count">1</span></a></li><li class="card-archive-list-item"><a class="card-archive-list-link" href="/archives/2020/12/"><span class="card-archive-list-date">十二月 2020</span><span class="card-archive-list-count">1</span></a></li><li class="card-archive-list-item"><a class="card-archive-list-link" href="/archives/2020/11/"><span class="card-archive-list-date">十一月 2020</span><span class="card-archive-list-count">6</span></a></li><li class="card-archive-list-item more is-center"><a class="card-archive-list-link-more" href="/archives/">
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