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atom.xml
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<?xml version="1.0" encoding="utf-8"?>
<feed xmlns="http://www.w3.org/2005/Atom">
<title>Cape of Good Hope</title>
<subtitle>I miss you</subtitle>
<link href="/atom.xml" rel="self"/>
<link href="https://imbowei.com/"/>
<updated>2019-09-10T06:57:03.465Z</updated>
<id>https://imbowei.com/</id>
<author>
<name>Christmas</name>
</author>
<generator uri="http://hexo.io/">Hexo</generator>
<entry>
<title>【阅读笔记】项亮前辈的《推荐系统实战》</title>
<link href="https://imbowei.com/2019/05/11/Recommended-system/"/>
<id>https://imbowei.com/2019/05/11/Recommended-system/</id>
<published>2019-05-11T02:15:41.000Z</published>
<updated>2019-09-10T06:57:03.465Z</updated>
<summary type="html">
<p>推荐系统是个有意思的方向。项亮前辈的《推荐系统实战》来当作入门的第一本书还是很合适的，这段时间断断续续的抽空阅读了一遍。本书写的浅显易懂，很好的勾勒出了推荐引擎十年前的主流算法，以及工业推荐系统是如何打磨的。从这个角度讲，项亮前辈很好地完成了”让学生了解如何将自己了解的算法实现到一个工业系统中去“这一写作目标。</p>
<p>看书的过程中简单记录了一些内容，夹杂着自己突然产生的一些想法。用以过段时间后的来重新复习消化本书。</p>
</summary>
<category term="Recommend-system" scheme="https://imbowei.com/categories/Recommend-system/"/>
<category term="UserCF" scheme="https://imbowei.com/tags/UserCF/"/>
<category term="ItemCF" scheme="https://imbowei.com/tags/ItemCF/"/>
<category term="Collaborative-filtering" scheme="https://imbowei.com/tags/Collaborative-filtering/"/>
<category term="Social-recommendation" scheme="https://imbowei.com/tags/Social-recommendation/"/>
</entry>
<entry>
<title>Python3复习总结</title>
<link href="https://imbowei.com/2019/01/19/python3-tips/"/>
<id>https://imbowei.com/2019/01/19/python3-tips/</id>
<published>2019-01-19T11:59:45.000Z</published>
<updated>2019-09-02T14:55:41.587Z</updated>
<summary type="html">
<p>抽空复习了一下python的语言特性，其中容易忘记、混淆的点特地记录如下。</p>
</summary>
<category term="Programming_language" scheme="https://imbowei.com/categories/Programming-language/"/>
<category term="Python3" scheme="https://imbowei.com/categories/Programming-language/Python3/"/>
<category term="File_operations" scheme="https://imbowei.com/tags/File-operations/"/>
<category term="Python3" scheme="https://imbowei.com/tags/Python3/"/>
<category term="Deepcopy" scheme="https://imbowei.com/tags/Deepcopy/"/>
<category term="Copy" scheme="https://imbowei.com/tags/Copy/"/>
<category term="Generator" scheme="https://imbowei.com/tags/Generator/"/>
<category term="Iterator" scheme="https://imbowei.com/tags/Iterator/"/>
</entry>
<entry>
<title>无名之辈不无名（A Cool Fish）</title>
<link href="https://imbowei.com/2018/11/16/an-unknown-person-is-not-unknown/"/>
<id>https://imbowei.com/2018/11/16/an-unknown-person-is-not-unknown/</id>
<published>2018-11-16T12:41:16.000Z</published>
<updated>2018-11-17T12:26:16.000Z</updated>
<summary type="html">
<p>感谢影片Cast阵容中的一个个“无名之辈”， 让我们置身于一个西南小城中，陪着一个个无名之辈度过了生命中的<strong>荒诞</strong>一天。 </p>
</summary>
<category term="Movie" scheme="https://imbowei.com/categories/Movie/"/>
<category term="Absurd-comedy" scheme="https://imbowei.com/categories/Movie/Absurd-comedy/"/>
<category term="Movie" scheme="https://imbowei.com/tags/Movie/"/>
<category term="Absurd" scheme="https://imbowei.com/tags/Absurd/"/>
<category term="Comedy" scheme="https://imbowei.com/tags/Comedy/"/>
<category term="Multithreading" scheme="https://imbowei.com/tags/Multithreading/"/>
<category term="A-Cool-Fish" scheme="https://imbowei.com/tags/A-Cool-Fish/"/>
</entry>
<entry>
<title>BERT：From Transformer Architecture to Transfer Learning</title>
<link href="https://imbowei.com/2018/10/27/From-transformer-architecture-to-transfer-learning/"/>
<id>https://imbowei.com/2018/10/27/From-transformer-architecture-to-transfer-learning/</id>
<published>2018-10-27T12:07:40.000Z</published>
<updated>2019-04-15T01:22:50.538Z</updated>
<summary type="html">
<p>半个月前BERT横空出世，在数十个数据集上屠榜，一时风头无两。外加国内一些科技自媒体的“UC式”标题推波助澜，也给这篇文章博得了更多的关注。为了更好的理解BERT，我们需要先理解Attention和Transformer结构。然后可以集中精力从Transfer Learning的角度来比较ELMo，GPT，BERT这三篇文章的优劣异同。 </p>
</summary>
<category term="Deep-learning" scheme="https://imbowei.com/categories/Deep-learning/"/>
<category term="Pre-training" scheme="https://imbowei.com/categories/Deep-learning/Pre-training/"/>
<category term="Deep-learning" scheme="https://imbowei.com/tags/Deep-learning/"/>
<category term="Transformer" scheme="https://imbowei.com/tags/Transformer/"/>
<category term="Attention" scheme="https://imbowei.com/tags/Attention/"/>
<category term="Transfer-learning" scheme="https://imbowei.com/tags/Transfer-learning/"/>
<category term="BERT" scheme="https://imbowei.com/tags/BERT/"/>
<category term="GNMT" scheme="https://imbowei.com/tags/GNMT/"/>
<category term="Self-attention" scheme="https://imbowei.com/tags/Self-attention/"/>
<category term="Multi-head-attention" scheme="https://imbowei.com/tags/Multi-head-attention/"/>
<category term="Positional-encoding" scheme="https://imbowei.com/tags/Positional-encoding/"/>
<category term="ELMo" scheme="https://imbowei.com/tags/ELMo/"/>
<category term="Semi-supervised-learning" scheme="https://imbowei.com/tags/Semi-supervised-learning/"/>
<category term="Pre-training" scheme="https://imbowei.com/tags/Pre-training/"/>
</entry>
<entry>
<title>如何写好学术论文？</title>
<link href="https://imbowei.com/2018/09/29/How-to-write-a-great-paper/"/>
<id>https://imbowei.com/2018/09/29/How-to-write-a-great-paper/</id>
<published>2018-09-29T12:09:59.000Z</published>
<updated>2018-10-14T07:12:16.000Z</updated>
<summary type="html">
<p>Since the last submission was rejected, I studied how to write English papers. The details are summarized as follows.</p>
</summary>
<category term="Academic" scheme="https://imbowei.com/categories/Academic/"/>
<category term="Writing" scheme="https://imbowei.com/categories/Academic/Writing/"/>
<category term="Write-a-paper" scheme="https://imbowei.com/tags/Write-a-paper/"/>
<category term="Conference-papers" scheme="https://imbowei.com/tags/Conference-papers/"/>
</entry>
<entry>
<title>配置便捷的开发环境（PyCharm & Jupyter）</title>
<link href="https://imbowei.com/2018/09/17/Configuring-the-best-development-environment-with-pycharm-and-jupyter-notebook/"/>
<id>https://imbowei.com/2018/09/17/Configuring-the-best-development-environment-with-pycharm-and-jupyter-notebook/</id>
<published>2018-09-17T11:39:43.000Z</published>
<updated>2018-09-20T14:51:20.000Z</updated>
<summary type="html">
<p>由于在PyCharm中进行统计整理数据、画图等操作有诸多的不便。在本地的jupyter notebook进行处理又显得步骤繁琐（传输文件），故萌生在服务器搭建jupyter notebook的想法。两种工具优势互补，从而最大程度上集中注意力在项目本身，提升开发效率。</p>
</summary>
<category term="Tool" scheme="https://imbowei.com/categories/Tool/"/>
<category term="Server" scheme="https://imbowei.com/categories/Tool/Server/"/>
<category term="PyCharm" scheme="https://imbowei.com/tags/PyCharm/"/>
<category term="Jupyter" scheme="https://imbowei.com/tags/Jupyter/"/>
</entry>
<entry>
<title>Win10平台下的常用软件</title>
<link href="https://imbowei.com/2018/09/09/win10-software/"/>
<id>https://imbowei.com/2018/09/09/win10-software/</id>
<published>2018-09-09T07:56:09.000Z</published>
<updated>2018-10-14T03:14:16.000Z</updated>
<summary type="html">
<p>由于电脑系统最近崩溃两次，让我有了备份常用软件清单的想法。工欲善其事必先利其器，不断追求效率的提升是一件很幸福的事情。清单不定期更新。</p>
</summary>
<category term="Operating-system" scheme="https://imbowei.com/categories/Operating-system/"/>
<category term="Win10" scheme="https://imbowei.com/categories/Operating-system/Win10/"/>
<category term="Flux" scheme="https://imbowei.com/tags/Flux/"/>
<category term="Shadowsocks" scheme="https://imbowei.com/tags/Shadowsocks/"/>
<category term="Pycharm" scheme="https://imbowei.com/tags/Pycharm/"/>
<category term="Teamviwer" scheme="https://imbowei.com/tags/Teamviwer/"/>
<category term="Tpyora" scheme="https://imbowei.com/tags/Tpyora/"/>
</entry>
<entry>
<title>双网卡配置：同时使用公司（学校/实验室）内网和外网</title>
<link href="https://imbowei.com/2018/09/07/Dual-NIC-configuration/"/>
<id>https://imbowei.com/2018/09/07/Dual-NIC-configuration/</id>
<published>2018-09-07T13:37:09.000Z</published>
<updated>2019-09-25T06:19:57.349Z</updated>
<summary type="html">
<p>没有一个好用的网络根本是没有办法好好工作的呀。最近通过改变路由表、调整网卡优先级，实现了内外网络的同时使用，终于可以愉快的上网了。</p>
</summary>
<category term="Tool" scheme="https://imbowei.com/categories/Tool/"/>
<category term="Computer-network" scheme="https://imbowei.com/categories/Tool/Computer-network/"/>
<category term="Hop-number" scheme="https://imbowei.com/tags/Hop-number/"/>
<category term="Routing-table" scheme="https://imbowei.com/tags/Routing-table/"/>
</entry>
<entry>
<title>算法的时间复杂度和空间复杂度</title>
<link href="https://imbowei.com/2018/08/17/Summary-of-time-complexity-and-space-complexity/"/>
<id>https://imbowei.com/2018/08/17/Summary-of-time-complexity-and-space-complexity/</id>
<published>2018-08-17T10:37:34.000Z</published>
<updated>2019-04-22T08:30:39.163Z</updated>
<summary type="html">
<p>趁假期复习了算法基础的时间复杂度和空间复杂度，整理一遍。</p>
</summary>
<category term="Algorithm" scheme="https://imbowei.com/categories/Algorithm/"/>
<category term="Time-complexity" scheme="https://imbowei.com/tags/Time-complexity/"/>
<category term="Space-complexity" scheme="https://imbowei.com/tags/Space-complexity/"/>
</entry>
<entry>
<title>AI发电厂——数据标注公司（国内数据标注公司服务调研）</title>
<link href="https://imbowei.com/2018/08/07/Investigate-domestic-data-labeling-companies/"/>
<id>https://imbowei.com/2018/08/07/Investigate-domestic-data-labeling-companies/</id>
<published>2018-08-07T11:07:28.000Z</published>
<updated>2018-09-04T10:19:20.000Z</updated>
<summary type="html">
<p>众所周知，深度学习需要大量的标记数据和高效的运算来做支撑。<br>计算资源只要从黄老板的公司订购就可以了，但大规模的高质量有标记数据却不是那么容易获得，让科研人员头疼不已。<br>应用时代而生的就是一大批数据众包公司和平台。正好借着一个数据众包任务，对于国内的数据标注公司服务有了更深一步的了解。</p>
</summary>
<category term="Machine-learning" scheme="https://imbowei.com/categories/Machine-learning/"/>
<category term="Data-labeling" scheme="https://imbowei.com/categories/Machine-learning/Data-labeling/"/>
<category term="Machine-learning" scheme="https://imbowei.com/tags/Machine-learning/"/>
<category term="Data-labeling" scheme="https://imbowei.com/tags/Data-labeling/"/>
<category term="Crowdsourcing" scheme="https://imbowei.com/tags/Crowdsourcing/"/>
</entry>
<entry>
<title>用Github和Coding双线绑定自定义域名</title>
<link href="https://imbowei.com/2018/07/29/Github-and-Coding-bulid-blog/"/>
<id>https://imbowei.com/2018/07/29/Github-and-Coding-bulid-blog/</id>
<published>2018-07-29T09:26:57.000Z</published>
<updated>2018-10-29T02:21:36.000Z</updated>
<summary type="html">
<p>本来博客已经搭建好了，可是总觉得用Github一个二级域名怪怪的，非要折腾自己来绑定自己的域名，并申请SSL（就是喜欢看那个小绿锁！）前前后后花了不少时间，走了不少弯路，将踩过的坑记录下来。</p>
</summary>
<category term="blog" scheme="https://imbowei.com/categories/blog/"/>
<category term="Github-pages" scheme="https://imbowei.com/tags/Github-pages/"/>
<category term="Coding-pages" scheme="https://imbowei.com/tags/Coding-pages/"/>
<category term="DNSpod" scheme="https://imbowei.com/tags/DNSpod/"/>
<category term="SSL" scheme="https://imbowei.com/tags/SSL/"/>
<category term="Domain" scheme="https://imbowei.com/tags/Domain/"/>
</entry>
<entry>
<title>Linux服务器命令行上传数据到百度云盘</title>
<link href="https://imbowei.com/2018/07/26/linux-uploading-baiduyunpan/"/>
<id>https://imbowei.com/2018/07/26/linux-uploading-baiduyunpan/</id>
<published>2018-07-25T23:25:35.000Z</published>
<updated>2018-09-04T10:19:50.000Z</updated>
<summary type="html">
<p>每次想把服务器上的数据上传到百度云盘都要先下载到本地，然后再上传百度云。<br>这一点都不优雅，既浪费时间，还占用电脑本来的带宽。<br>如果Linux命令行能够直接上传百度云盘岂不美哉？说干就干，磨刀不误砍柴工！</p>
</summary>
<category term="Linux" scheme="https://imbowei.com/categories/Linux/"/>
<category term="Shell" scheme="https://imbowei.com/tags/Shell/"/>
<category term="Bypy" scheme="https://imbowei.com/tags/Bypy/"/>
</entry>
<entry>
<title>wikipedia 训练繁（简）体中文 embedding(word2vec)模型</title>
<link href="https://imbowei.com/2018/07/22/wikipedia-train-traditional-chinese-embedding%EF%BC%88word2vec%EF%BC%89model/"/>
<id>https://imbowei.com/2018/07/22/wikipedia-train-traditional-chinese-embedding（word2vec）model/</id>
<published>2018-07-22T14:52:39.000Z</published>
<updated>2018-09-25T08:53:00.000Z</updated>
<summary type="html">
<p>由于课题任务需要一个繁体中文的word3vec, 折腾经过记录在此。希望以后少掉几个坑。<br>训练好的embedding放在<a href="https://pan.baidu.com/s/1DB_Sft8N9XMyDP9cpVMpBw" target="_blank" rel="noopener">网盘</a>中， 密码：<code>2um0</code><br>后来又按照这个方法训练了简体中文维度分别为50、100、200、300的embedding，一并放出来<a href="https://pan.baidu.com/s/1JgjRBWwwrcJSy4taPFtLhA" target="_blank" rel="noopener">网盘链接</a> 密码：<code>751d</code></p>
</summary>
<category term="NLP" scheme="https://imbowei.com/categories/NLP/"/>
<category term="Word2vex" scheme="https://imbowei.com/categories/NLP/Word2vex/"/>
<category term="Wikipedia" scheme="https://imbowei.com/tags/Wikipedia/"/>
<category term="Gensim" scheme="https://imbowei.com/tags/Gensim/"/>
<category term="Embedding" scheme="https://imbowei.com/tags/Embedding/"/>
<category term="Opencc" scheme="https://imbowei.com/tags/Opencc/"/>
</entry>
<entry>
<title>linux 常用命令备忘</title>
<link href="https://imbowei.com/2018/07/20/Use-linux-well/"/>
<id>https://imbowei.com/2018/07/20/Use-linux-well/</id>
<published>2018-07-20T11:56:19.000Z</published>
<updated>2019-05-07T11:26:42.502Z</updated>
<summary type="html">
<p>经常会有一些的Linux命令记不牢，持续整理更新，以便查找。</p>
</summary>
<category term="Linux" scheme="https://imbowei.com/categories/Linux/"/>
<category term="Linux" scheme="https://imbowei.com/tags/Linux/"/>
</entry>
<entry>
<title>Hexo_Next_博客搭建记</title>
<link href="https://imbowei.com/2018/07/14/Hexo_Next_%E5%8D%9A%E5%AE%A2%E6%90%AD%E5%BB%BA%E8%AE%B0/"/>
<id>https://imbowei.com/2018/07/14/Hexo_Next_博客搭建记/</id>
<published>2018-07-14T04:43:07.000Z</published>
<updated>2019-05-01T09:15:13.411Z</updated>
<summary type="html">
<p>与我而言，建立个人博客存在的意义有两个。<br>一方面，当作自己的备忘录，记录零散的知识点，避免重复的搜索工作；<br>另一方面，可以更好的分享一些自己的心得，方便与大家交流。<br>选择<code>GitHub Hexo Next</code>的组合的主要原因就是方便、便宜、简单，为从来没有接触过前端的自己降低难度。<br>为了能让博客漂亮一点，这几天来的折腾过程记录整理在此，以备遗忘。</p>
</summary>
<category term="blog" scheme="https://imbowei.com/categories/blog/"/>
<category term="Github-pages" scheme="https://imbowei.com/tags/Github-pages/"/>
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<category term="Web-design" scheme="https://imbowei.com/tags/Web-design/"/>
</entry>
<entry>
<title>【NLP competition】中文信息学会 文本溯源技术评测（SMP ETST）Ranking First</title>
<link href="https://imbowei.com/2018/07/09/%E3%80%90NLP%E6%AF%94%E8%B5%9B%E3%80%91%E4%B8%AD%E5%9B%BD%E4%B8%AD%E6%96%87%E4%BF%A1%E6%81%AF%E5%AD%A6%E4%BC%9A%E6%96%87%E6%9C%AC%E6%BA%AF%E6%BA%90%E6%8A%80%E6%9C%AF%E8%AF%84%E6%B5%8B%EF%BC%88SMP-ETST%EF%BC%89Ranking-First/"/>
<id>https://imbowei.com/2018/07/09/【NLP比赛】中国中文信息学会文本溯源技术评测（SMP-ETST）Ranking-First/</id>
<published>2018-07-09T00:10:43.000Z</published>
<updated>2018-09-30T01:36:16.000Z</updated>
<summary type="html">
<p>此次的文本溯源项目我们以n-gram为核心思想，构建候选句子对的评测标准。<br>用TF-IDF和词袋模型的的思想来预筛选候选句子对，大大提升算法效率。<br>最后用了两种切词方式的模型融合和规则后处理（提升很小）。<br><a href="https://biendata.com/competition/smpetst2018/final-leaderboard/" target="_blank" rel="noopener">Final-Leaderboard Ranking First</a></p>
</summary>
<category term="NLP" scheme="https://imbowei.com/categories/NLP/"/>
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<category term="Paraphrase" scheme="https://imbowei.com/tags/Paraphrase/"/>
<category term="N-gram" scheme="https://imbowei.com/tags/N-gram/"/>
<category term="Segment" scheme="https://imbowei.com/tags/Segment/"/>
<category term="Information-retrieval" scheme="https://imbowei.com/tags/Information-retrieval/"/>
<category term="BoW" scheme="https://imbowei.com/tags/BoW/"/>
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</entry>
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