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        <p>字数统计:</p>
        <p>5.8k字</p>
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        <p>19分</p>
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      <p>我们就会产生一个想法：能不能有一个机器人来回答这些重复的问题，它能 24 小时的工作不用休息，降低客户成本，还能挖掘聊天记录里面的一些有价值的知识点。这就是智能客服产生的背景。</p>
      
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      【NLP】XLNet
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        <p>字数统计:</p>
        <p>4.7k字</p>
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        <p>16分</p>
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      <p>XLNet 是一个类似 BERT 的模型，而不是完全不同的模型。总之，XLNET是一种通用的自回归预训练方法。它是CMU和Google Brain团队在2019年6月份发布的模型，最终，XLNet 在 20 个任务上超过了 BERT 的表现，并在 18 个任务上取得了当前最佳效果（state-of…</p>
      
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        <p>5.7k字</p>
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        <p>20分</p>
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      <p>BERT的全称是BIDIRECTIONAL ENCODER REPRESENTATION FROM TRANSFORMERS，是Google2018年提出的预训练模型，即双向Transformer的Encoder，因为decoder是不能获要预测的信息的。模型的主要创新点都在pre-train方法…</p>
      
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    <a href="/2019/09/26/NLP/Transformer/">
      【NLP】Transformer
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    <p>2019-09-26</p>
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        <p>13分</p>
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      <p>什么是Transformer 《Attention Is All You Need》是一篇Google提出的将Attention思想发挥到极致的论文。这篇论文中提出一个全新的模型，叫 Transformer，抛弃了以往深度学习任务里面使用到的 CNN 和 RNN。目前大热…</p>
      
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      推荐系统
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        <p>字数统计:</p>
        <p>8.5k字</p>
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        <p>29分</p>
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      <p>推荐系统是利用电子商务网站向客户提供商品信息和建议，帮助用户决定应该购买什么产品，模拟销售人员帮助客户完成购买过程。个性化推荐是根据用户的兴趣特点和购买行为，向用户推荐用户感兴趣的信息和商品。 随着电子商务规模的不断扩大，商品个数和种类快速增长，顾客需要花费大量的时间才能找到自己想买的商品。这种浏…</p>
      
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    <a href="/2019/08/31/NLP/Attention/">
      【NLP】注意力机制(Attention)
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        <p>字数统计:</p>
        <p>4.1k字</p>
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      <p>什么是Attention机制 在“编码器—解码器（seq2seq）”⼀节⾥，解码器在各个时间步依赖相同的背景变量来获取输⼊序列信息。当编码器为循环神经⽹络时，背景变量来⾃它最终时间步的隐藏状态。 现在，让我们再次思考那⼀节提到的翻译例⼦：输⼊为英语序列“They”“are…</p>
      
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      <p>在⾃然语⾔处理的很多应⽤中，输⼊和输出都可以是不定⻓序列。以机器翻译为例，输⼊可以是⼀段不定⻓的英语⽂本序列，输出可以是⼀段不定⻓的法语⽂本序列，例如： 英语输⼊：“They”、“are”、“watching”、“.” 法语输出：“Ils”、“regardent”、“.” 当输⼊和输出都是不定⻓序…</p>
      
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      <p>TEXTRNN指的是利用RNN循环神经网络解决文本分类问题，文本分类是自然语言处理的一个基本任务，试图推断出给定文本(句子、文档等)的标签或标签集合。 在一些自然语言处理任务中，当对序列进行处理时，我们一般会采用循环神经网络RNN，尤其是它的一些变种，如LSTM(更常用)，GRU。当然我们也可以把…</p>
      
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      <p>正如GloVe论文的标题而言，<strong>GloVe的全称叫Global Vectors for Word Representation，它是一个基于全局词频统计（count-based &amp; overall statistics）的词表征（word representation）工具，它可以把一个单词表达…</strong></p>
      
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      <p>英语单词通常有其内部结构和形成⽅式。例如，我们可以从“dog”“dogs”和“dogcatcher”的字⾯上推测它们的关系。这些词都有同⼀个词根“dog”，但使⽤不同的后缀来改变词的含义。而且，这个关联可以推⼴⾄其他词汇。 在word2vec中，我们并没有直接利⽤构词学中的信息。⽆论是在跳字模型还…</p>
      
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  /*页面载入完成后，创建复制按钮*/
  !function (e, t, a) {
    /* code */
    var initCopyCode = function(){
      var copyHtml = '';
      copyHtml += '<button class="btn-copy" data-clipboard-snippet="">';
      copyHtml += '  <i class="fa fa-copy"></i><span>复制</span>';
      copyHtml += '</button>';
      $(".highlight .code pre").before(copyHtml);
      var clipboard = new ClipboardJS('.btn-copy', {
        target: function(trigger) {
          return trigger.nextElementSibling;
        }
      });

      clipboard.on('success', function(e) {
        //您可以加入成功提示
        console.info('Action:', e.action);
        console.info('Text:', e.text);
        console.info('Trigger:', e.trigger);
        success_prompt(COPY_SUCCESS);
        e.clearSelection();
      });
      clipboard.on('error', function(e) {
        //您可以加入失败提示
        console.error('Action:', e.action);
        console.error('Trigger:', e.trigger);
        fail_prompt(COPY_FAILURE);
      });
    }
    initCopyCode();

  }(window, document);

  /**
   * 弹出式提示框，默认1.5秒自动消失
   * @param message 提示信息
   * @param style 提示样式，有alert-success、alert-danger、alert-warning、alert-info
   * @param time 消失时间
   */
  var prompt = function (message, style, time)
  {
      style = (style === undefined) ? 'alert-success' : style;
      time = (time === undefined) ? 1500 : time*1000;
      $('<div>')
          .appendTo('body')
          .addClass('alert ' + style)
          .html(message)
          .show()
          .delay(time)
          .fadeOut();
  };

  // 成功提示
  var success_prompt = function(message, time)
  {
      prompt(message, 'alert-success', time);
  };

  // 失败提示
  var fail_prompt = function(message, time)
  {
      prompt(message, 'alert-danger', time);
  };

  // 提醒
  var warning_prompt = function(message, time)
  {
      prompt(message, 'alert-warning', time);
  };

  // 信息提示
  var info_prompt = function(message, time)
  {
      prompt(message, 'alert-info', time);
  };

</script>


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<script src="https://cdn.jsdelivr.net/gh/fancyapps/fancybox@3.5.7/dist/jquery.fancybox.min.js"></script>
<script>
  let LAZY_LOAD_IMAGE = "";
  $(".article-entry").find("fancybox").find("img").each(function () {
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      $(element).attr("data-fancybox", "gallery");
      $(element).attr("href", $(this).attr("src"));
      /* 图片采用懒加载处理时,
       * 一般图片标签内会有个属性名来存放图片的真实地址，比如 data-original,
       * 那么此处将原本的属性名src替换为对应属性名data-original,
       * 修改如下
       */
       if (LAZY_LOAD_IMAGE) {
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      $(this).wrap(element);
  });
</script>


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