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class="recent-post-item"><div class="post_cover left"><a href="/2024/07/08/2024-1-19-%E7%B9%81%E8%8A%B1/" title="繁花"><img class="post-bg" src= "data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7" data-lazy-src="/../img/cover.jpg" onerror="this.onerror=null;this.src='/img/404.jpg'" alt="繁花"></a></div><div class="recent-post-info"><a class="article-title" href="/2024/07/08/2024-1-19-%E7%B9%81%E8%8A%B1/" title="繁花">繁花</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 class="post-meta-date-created" datetime="2024-07-07T16:04:22.148Z" title="发表于 2024-07-08 00:04:22">2024-07-08</time><span class="article-meta-separator">|</span><i class="fas fa-history"></i><span class="article-meta-label">更新于</span><time class="post-meta-date-updated" datetime="2024-07-07T16:49:35.932Z" title="更新于 2024-07-08 00:49:35">2024-07-08</time></span><span class="article-meta"><span class="article-meta-separator">|</span><i class="fas fa-inbox"></i><a class="article-meta__categories" href="/categories/%E5%BD%B1%E8%AF%84/">影评</a></span></div><div class="content">繁花 一开始是奔着精彩商战,各方博弈去看的,没想到带给我的却是一层又一层的惊喜和震撼。不管是剧情内容,拍摄手法,镜头语言,角色刻画,演员张力,bgm选曲,我认为繁花都做的极佳,也让我对八九十年代的上海有了更深入的了解。
八九十年代,迎着改革开放的春风,股票,外贸是时代的风口,人们穿行在上海滩的滚滚红尘中,都希望从中分一杯羹,扶摇而上。散户,舰队,麒麟会,再到后面南国投,每一次的新玩法,都象征着市场的迭代升级,也一次次刺激着经济发展。和外资合作,敢拼,敢做,敢摸着石头过河,上的了像至真园般的饭店谈笑风生,下的了深圳一个小县城的工厂里放下身段。饭店吃饭,吃的是合作;一只龙虾,就是一次机会;饭店里的食客,不是在应酬,就是在求人。一切的一切,像极了盖茨比中西方”咆哮的二十年代“。
30年前如此,30年后又有什么变化呢?十几二十年前的房地产,当下的互联网,电商,自媒体,AI…一个又一个时代风口接踵而至。或许你机智聪慧,有创业头脑,又有贵人身旁,成功如宝总;或许你顽强不屈,为争一口气,拼搏如汪小姐;或许你运筹帷幄,有勇有谋,闯荡如李李;或许你后来独立自强,有主张、搞得定、摆得平,输得起如 ...</div></div></div><div class="recent-post-item"><div class="post_cover right"><a href="/2024/07/07/2024-7-7-%E7%88%B1%E5%9C%A8%E9%BB%8E%E6%98%8E%E7%A0%B4%E6%99%93%E5%89%8D%EF%BC%88Before%20Sunrise%EF%BC%89%2075c6bd9af1644fde9c0c1a5c645abb10/" title="爱在黎明破晓前(Before Sunrise)"><img class="post-bg" src= "data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7" data-lazy-src="/../img/before%20sunrise.png" onerror="this.onerror=null;this.src='/img/404.jpg'" alt="爱在黎明破晓前(Before Sunrise)"></a></div><div class="recent-post-info"><a class="article-title" href="/2024/07/07/2024-7-7-%E7%88%B1%E5%9C%A8%E9%BB%8E%E6%98%8E%E7%A0%B4%E6%99%93%E5%89%8D%EF%BC%88Before%20Sunrise%EF%BC%89%2075c6bd9af1644fde9c0c1a5c645abb10/" title="爱在黎明破晓前(Before Sunrise)">爱在黎明破晓前(Before Sunrise)</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 class="post-meta-date-created" datetime="2024-07-07T15:38:36.540Z" title="发表于 2024-07-07 23:38:36">2024-07-07</time><span class="article-meta-separator">|</span><i class="fas fa-history"></i><span class="article-meta-label">更新于</span><time class="post-meta-date-updated" datetime="2024-07-07T16:51:40.398Z" title="更新于 2024-07-08 00:51:40">2024-07-08</time></span><span class="article-meta"><span class="article-meta-separator">|</span><i class="fas fa-inbox"></i><a class="article-meta__categories" href="/categories/%E5%BD%B1%E8%AF%84/">影评</a></span></div><div class="content">爱在黎明破晓前(Before Sunrise) 偶然一次逛b站看到的剪辑,翻看评论发现叫爱在三部曲,这是第一部,诞生于1995年,导演是理查德·林克莱特,据说是根据自己在费城的经历拍出的艺术。
影片基本就围绕男女主Jesse and Seline进行,讲述了二人彼此一见钟情,并crazy般的在维也纳度过了afternoon,midnight,sunrise的美丽故事。对于我来说,不得不佩服 Jesse 的勇敢,敢邀请 Seline 打破原计划而和他一起hang out,或许很多人会认为这是因为男主的帅气外表,但我更愿意相信是其幽默健谈、勇敢不羁的人格魅力打动了Seline。
这部影片的拍摄成本非常低,不仅是相较于2024年来说,对比1995年的其它电影亦是如此,但这种简单,纯粹的拍摄手法却更能聚焦观众的注意在二人天马行空的对话中,从小时候的经历到彼此的人格(little boy versus old woman);从对待手相奶奶的不同态度到彼此的ex-boy/girl friend;从在bus上的的quick talk到唱片店无处安放的情愫;从街边的促膝长谈到饭店幼 ...</div></div></div><div class="recent-post-item"><div class="post_cover left"><a href="/2023/10/28/2023-10-28-%E5%83%8F%E6%A0%91%E4%B8%80%E6%A0%B7%E7%94%9F%E9%95%BF/" title="像树一样生长"><img class="post-bg" src= "data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7" data-lazy-src="/../img/%E5%83%8F%E6%A0%91%E4%B8%80%E6%A0%B7%E7%94%9F%E9%95%BF.jpg" onerror="this.onerror=null;this.src='/img/404.jpg'" alt="像树一样生长"></a></div><div class="recent-post-info"><a class="article-title" href="/2023/10/28/2023-10-28-%E5%83%8F%E6%A0%91%E4%B8%80%E6%A0%B7%E7%94%9F%E9%95%BF/" title="像树一样生长">像树一样生长</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 class="post-meta-date-created" datetime="2023-10-28T07:03:39.288Z" title="发表于 2023-10-28 15:03:39">2023-10-28</time><span class="article-meta-separator">|</span><i class="fas fa-history"></i><span class="article-meta-label">更新于</span><time class="post-meta-date-updated" datetime="2024-07-07T16:20:15.734Z" title="更新于 2024-07-08 00:20:15">2024-07-08</time></span><span class="article-meta"><span class="article-meta-separator">|</span><i class="fas fa-inbox"></i><a class="article-meta__categories" href="/categories/%E7%94%9F%E6%B4%BB%E5%B0%8F%E6%84%9F%E6%82%9F/">生活小感悟</a></span></div><div class="content">像树一样生长序 作为一个文学水平巅峰停留在初高中的人来说能想要放下手中的ddl来写一些文字可谓极其稀有了,即便如此,我的确是有些东西和感悟要写,算是记录,算是回忆……
一些思考 大学,确实是有些魅力的,它令人着迷的点不仅是你摄入的知识(虽然你科的学生大多数时间都在干这个),还在你接触的人,看到的事。因为贵专业的缘故,其实我并没有接触到什么人,每天做的事也远不如进入大学前幻想的精彩,不过好在,我还是有了些思考。
学业 首先自然是讲讲学业,不知道其它学校或者华科其它专业的本科生教学水平怎么样,但是口口(华科计院)的教学实在不敢恭维,学的东西有用吗,肯定是有用的,但是内容确实也没有紧跟时代,再加上奇怪的时间安排,一门课学下来最后毕业了脑子里剩下的东西并不多。
大多数人只是为了保研出国或者奖学金卷一个高分(羡慕会考试的,我想卷高分都不会),毕业了工作是不是计算机方向都不一定,就算是,大多数技能都是自学,即便你保研了,最终目的只要是就业,无非就是多读3-5年书,拿个研究生博士生的学历,让你在大学的 “温床” 多停留3-5年罢了。那如果留校执教呢,做科研确实是辛 ...</div></div></div><div class="recent-post-item"><div class="post_cover right"><a href="/2023/05/31/2023-5-31-%E7%BB%8F%E5%85%B8%E7%AE%97%E6%B3%95/" title="经典算法"><img class="post-bg" src= "data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7" data-lazy-src="/../img/cube.jpg" onerror="this.onerror=null;this.src='/img/404.jpg'" alt="经典算法"></a></div><div class="recent-post-info"><a class="article-title" href="/2023/05/31/2023-5-31-%E7%BB%8F%E5%85%B8%E7%AE%97%E6%B3%95/" title="经典算法">经典算法</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 class="post-meta-date-created" datetime="2023-05-31T08:35:05.349Z" title="发表于 2023-05-31 16:35:05">2023-05-31</time><span class="article-meta-separator">|</span><i class="fas fa-history"></i><span class="article-meta-label">更新于</span><time class="post-meta-date-updated" datetime="2024-07-07T16:22:01.984Z" title="更新于 2024-07-08 00:22:01">2024-07-08</time></span></div><div class="content">经典算法插入
直接插入
1234567891011void insertion_sort(int arr[], int len){ for(int i=1; i<len; i++){ int key = arr[i]; //存入当前元素 int j = i-1; while(j>=0 && key<arr[j]){ //后面比前面小 arr[j+1] = arr[j]; j--; } arr[j+1]=key; //否则直接插在后面 }}
折半插入
12345678910111213void BInsertSort(SqList &L){ for(i=2; i<=L.length; ++i){ L.r[0] = L.r[i]; //L.r[i]暂存到L.r[0] low = 1; high = i-1; while(low < high){ m = (high+low) / 2; if(L.r[0].key ...</div></div></div><div class="recent-post-item"><div class="post_cover left"><a href="/2023/05/04/2023-5-4-logistics/" title="logistics"><img class="post-bg" src= "data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7" data-lazy-src="/../img/beauty.jpg" onerror="this.onerror=null;this.src='/img/404.jpg'" alt="logistics"></a></div><div class="recent-post-info"><a class="article-title" href="/2023/05/04/2023-5-4-logistics/" title="logistics">logistics</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 class="post-meta-date-created" datetime="2023-05-04T07:12:52.270Z" title="发表于 2023-05-04 15:12:52">2023-05-04</time><span class="article-meta-separator">|</span><i class="fas fa-history"></i><span class="article-meta-label">更新于</span><time class="post-meta-date-updated" datetime="2024-07-07T15:56:17.209Z" title="更新于 2024-07-07 23:56:17">2024-07-07</time></span><span class="article-meta"><span class="article-meta-separator">|</span><i class="fas fa-inbox"></i><a class="article-meta__categories" href="/categories/AI/">AI</a></span></div><div class="content">==二元分类==输出:标签1/0
img在计算机中保存–>三个矩阵(假设64*64,那么就有3 * 64 * 64个像素,变成特征向量,总维度,也就是矩阵的行为n=3 * 64 * 64,如果有m组训练集,那么矩阵最终为m列,n行)
==logistics回归== y^ 表示 y 等于1的一种可能性或者是机会
w 表示逻辑回归的参数,这也是一个 n 维向量(因为 w 实际上是特征权重,维度与特征向量相同),参数里面还有 b,这是一个实数(表示偏差)
选择使用sigmoid函数原因:如果单纯线性,无法把y^值控制在0-1之间,故取sigmoid(z)
使得z很大,sigmoid(z)趋近1, z 变成一个绝对值很大的负数,sigmoid(z)趋近0
==损失函数==$L(\hat{y},y) = -y\log(\hat{y})-(1-y)\log(1-\hat{y})$ 单次
成本(代价)函数:$J(w,b) = ...</div></div></div><div class="recent-post-item"><div class="post_cover right"><a href="/2023/05/03/2023-5-3-intro%20to%20Deep%20learning/" title="Intro to deep learning"><img class="post-bg" src= "data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7" data-lazy-src="https://tse3-mm.cn.bing.net/th/id/OIP-C.713XQPQ7LAdyYLOXPI5VcQHaEo?pid=ImgDet&rs=1" onerror="this.onerror=null;this.src='/img/404.jpg'" alt="Intro to deep learning"></a></div><div class="recent-post-info"><a class="article-title" href="/2023/05/03/2023-5-3-intro%20to%20Deep%20learning/" title="Intro to deep learning">Intro to deep learning</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 class="post-meta-date-created" datetime="2023-05-03T11:45:45.673Z" title="发表于 2023-05-03 19:45:45">2023-05-03</time><span class="article-meta-separator">|</span><i class="fas fa-history"></i><span class="article-meta-label">更新于</span><time class="post-meta-date-updated" datetime="2024-07-07T15:57:12.514Z" title="更新于 2024-07-07 23:57:12">2024-07-07</time></span><span class="article-meta"><span class="article-meta-separator">|</span><i class="fas fa-inbox"></i><a class="article-meta__categories" href="/categories/AI/">AI</a></span></div><div class="content">==Deep learning==神经网络RELU 修正线性单元
独立神经元叠在一起 –> 神经网络 –> 只需要input x –> output y(中间部分自己完成)
supervised learning 监督学习 ==NN->neural net==
predict房价,ad投放 –> standard NN;
image –> CNN(卷积神经网络)
sequence data==(eg. audio, language)== –> RNN (循环神经网络)
无人驾驶 –> custom, hybrid(复杂)
结构化数据——对数据有很清晰的定义
非结构化数据——相对难解释给计算机
要么神经网络规模大,要么数据量多
data 早期
computation 当下
algorithms 创新算法为了加快计算速度,服务于规模更大的网络
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最大连续子数组和:函数名称是MaxSubArray(L); 初始条件是线性表L已存在且非空,请找出一个具有最大和的连续子数组(子数组最少包含一个元素),操作结果是其最大和;
1234567891011int maxSubArray(int* nums, int numsSize) { int maxSum = nums[0]; int curSum = nums[0]; for (int i = 1; i < numsSize; i++) { curSum = curSum > 0 ? curSum + nums[i] : nums[i]; if (curSum > maxSum) { maxSum = curSum; } } return maxSum;}
链表类似
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