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<!DOCTYPE html>
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<article id="post-实战篇——达观杯比赛，信息抽取" class="post-实战篇——达观杯比赛，信息抽取 post type-post status-publish format-standard hentry">
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<a class="article-title" href="/2019/08/26/实战篇——达观杯比赛，信息抽取/">实战篇——达观杯比赛，信息抽取</a>
</h1>
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<p>“达观杯”文本智能信息抽取挑战赛 <a href="https://www.biendata.com/competition/datagrand/" target="_blank" rel="noopener">https://www.biendata.com/competition/datagrand/</a><br>
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2019-08-26
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<article id="post-主动学习" class="post-主动学习 post type-post status-publish format-standard hentry">
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<a class="article-title" href="/2019/08/26/主动学习/">主动学习</a>
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<p>快速了解主动学习<br><a href="https://zhuanlan.zhihu.com/p/39367595" target="_blank" rel="noopener">https://zhuanlan.zhihu.com/p/39367595</a><br>
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2019-08-26
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<article id="post-论文-BERT-翻译" class="post-论文-BERT-翻译 post type-post status-publish format-standard hentry">
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<a class="article-title" href="/2019/08/26/论文-BERT-翻译/">论文:BERT 翻译</a>
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<p>BERT：预训练的深度双向Transformer语言模型<br>
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2019-08-26
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<article id="post-NLP自然语言处理" class="post-NLP自然语言处理 post type-post status-publish format-standard hentry">
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<a class="article-title" href="/2019/08/21/NLP自然语言处理/">NLP自然语言处理</a>
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<p>最近一直在做nlp的东西，但都还是了解的太表面了。东西有点多，杂，现在的心情正好适合整理一下。<br>
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2019-08-21
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<article id="post-好久不见之BERTRT问题篇" class="post-好久不见之BERTRT问题篇 post type-post status-publish format-standard hentry">
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<a class="article-title" href="/2018/12/15/好久不见之BERTRT问题篇/">好久不见之BERT问题篇</a>
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<p>整理的看BERT论文中的一些问题……有待更新<br>
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2018-12-15
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<article id="post-好久不见之BERT——NLP" class="post-好久不见之BERT——NLP post type-post status-publish format-standard hentry">
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<a class="article-title" href="/2018/12/15/好久不见之BERT——NLP/">好久不见之BERT——NLP</a>
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<p>不知不觉已经很久不写了……看了看之前的，还真觉得有的知识真是得记下来啊（因为脑子装不下……哈哈哈）回归主题，今天整理了一下nlp最新的BERT模型，涉及太多不懂的了，仍在studying……<br>
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2018-12-15
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<article id="post-数据结构之最小生成树" class="post-数据结构之最小生成树 post type-post status-publish format-standard hentry">
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<a class="article-title" href="/2018/07/19/数据结构之最小生成树/">数据结构之最小生成树</a>
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<p>最小生成树是一副连通加权无向图中一棵权值最小的生成树。</p>
<p><img src="/upload_image/Minimum_spanning_tree.png" alt=""></p>
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<article id="post-机器学习之K近邻" class="post-机器学习之K近邻 post type-post status-publish format-standard hentry">
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<a class="article-title" href="/2018/07/19/机器学习之K近邻/">机器学习之K近邻</a>
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<p> K近邻法(k-nearest neighbors,KNN)是一种很基本的机器学习方法了，在我们平常的生活中也会不自主的应用。比如，我们判断一个人的人品，只需要观察他来往最密切的几个人的人品好坏就可以得出了。这里就运用了KNN的思想。KNN方法既可以做分类，也可以做回归，这点和决策树算法相同。<a href="https://zhuanlan.zhihu.com/p/25994179" target="_blank" rel="noopener">参考文章</a></p>
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<p>主要是对操作系统的一些回顾复习！ <a href="https://github.com/CyC2018/Interview-Notebook/blob/master/notes/%E8%AE%A1%E7%AE%97%E6%9C%BA%E6%93%8D%E4%BD%9C%E7%B3%BB%E7%BB%9F.md" target="_blank" rel="noopener">参考</a><br>
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<p>今天学习的是K-means聚类算法<br>
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