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<th>《机器学习中的常识性问题》</th>
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<td>机器学习中的常识性问题定义：作为一名合格的机器学习从业人员必须理解和熟练掌握的机器学习领域的问题。<br> <br> 1. 网页版：<a href="https://yuanxiaosc.github.io/2019/08/16/机器学习中的常识性问题/">https://yuanxiaosc.github.io/2019/08/16/机器学习中的常识性问题/</a><br> 2. 百度网盘 链接：<a href="https://pan.baidu.com/s/1NG-xnDm0_4OYkpvMfBDKIw" target="_blank" rel="noopener">https://pan.baidu.com/s/1NG-xnDm0_4OYkpvMfBDKIw</a>, 提取码：5kra <br> 3. 《机器学习宝典》（涵盖本文及扩展内容）： <a href="https://github.com/yuanxiaosc/Machine-Learning-Book" target="_blank" rel="noopener">https://github.com/yuanxiaosc/Machine-Learning-Book</a> 欢迎Star和分享 <br> 4. 编者：袁宵</td>
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<p>反向传播（英语：Backpropagation，缩写为BP）是“误差反向传播”的简称，是一种与最优化方法（如梯度下降法）结合使用的，用来训练人工神经网络的常见方法。该方法计算对网络中所有权重计算损失函数的梯度。这个梯度会反馈给最优化方法，用来更新权值以最小化损失函数。 在神经网络上执行梯度下降法的主要算法。该算法会先按前向传播方式计算（并缓存）每个节点的输出值，然后再按反向传播遍历图的方式计算损失函数值相对于每个参数的偏导数。</p>
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<h2 id="AUC基础"><a href="#AUC基础" class="headerlink" title="AUC基础"></a>AUC基础</h2><h3 id="AUC定义"><a href="#AUC定义" class="headerlink" title="AUC定义"></a>AUC定义</h3><blockquote>
<p>百度百科 <a href="https://baike.baidu.com/item/AUC/19282953" target="_blank" rel="noopener">https://baike.baidu.com/item/AUC/19282953</a></p>
</blockquote><p>AUC（Area Under Curve）被定义为ROC曲线下与坐标轴围成的面积，显然这个面积的数值不会大于1。又由于ROC曲线一般都处于y=x这条直线的上方，所以AUC的取值范围在0.5和1之间。AUC越接近1.0，检测方法真实性越高;等于0.5时，则真实性最低，无应用价值。</p>
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<p>对于计算机是如何字符的理解难度主要来源于不同文献对字符编码概念的混淆，本文就是为了厘清字符相关概念，解决困扰程序员的字符编码、解码和乱码问题。</p>
</blockquote><p><img src="/2020/06/12/计算机是如何处理字符的/编码解码和乱码问题.png" alt=""></p><p>上图展示计算机中字符和字节的转换过程，字符在计算中有字符对应的形状、字符对应的整数值和字符对应的字节形式三种表示方法。本文讲述计算机是如何处理字符的问题，即字符这三种表示的转换过程，特别是字符的编码问题。</p>
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<p><a href="https://yuanxiaosc.github.io/2020/05/31/CPlusPlus编程语言基础/">《CPlusPlus编程语言基础》</a>又称为“C加加知识树”、“C++知识树”，用树状思维导图的形式展现C++从业人员必备的所有C++基础知识。该资源主要参考了C++编程语言巨著<a href="https://book.douban.com/subject/25708312/" target="_blank" rel="noopener">《C++ Primer》</a>、<a href="https://book.douban.com/subject/1319751/" target="_blank" rel="noopener">《C++ Primer Plus》</a>以及<a href="https://zh.cppreference.com/w/cpp" target="_blank" rel="noopener">《C++ 参考手册》</a>的内容，并且具有学习高效、格式丰富和内容权威的优点，供C++初学者入门和C++技术人员参考。</p>
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<p>HuggingFace-Transformers手册 = 官方链接 + 设计结构 + 使用教程 + 代码解析</p><p>Transformers（以前称为pytorch Transformers和pytorch pretrained bert）为自然语言理解（NLU）和自然语言生成（NLG）提供了最先进的通用架构（bert、GPT-2、RoBERTa、XLM、DistilBert、XLNet、CTRL…），其中有超过32个100多种语言的预训练模型并同时支持TensorFlow 2.0和Pythorch两大深度学习框架。</p>
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<p>本文系统性总结了学习机器学习所需的概率论和信息论基础知识。</p><p>通过使用概率论，可以计算事件$Y$在事件$X$发生时的概率，这是很多机器学习的算法的构建模型的基础，比如建模$Y=f(X)$。通过使用信息论，可以描述随机事件的信息量也可以计算两种概率分布的差异，而后者是机器学习模型通常要优化的目标，比如度量模型预测分布$\hat Y$和数据分布$Y$的差异$g(\hat Y, Y)$。</p>
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