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<p>Workflow stages <a href="https://www.kaggle.com/startupsci/titanic-data-science-solutions" target="_blank" rel="noopener">参考链接</a> <a href="https://www.kaggle.com/c/titanic" target="_blank" rel="noopener">比赛链接</a></p>
<p>The competition solution workflow goes through seven stages described in the Data Science Solutions book.</p>
<blockquote>
<p>1.Question or problem definition.<br>2.Acquire training and testing data.<br>3.Wrangle, prepare, cleanse the data.<br>4.Analyze, identify patterns, and explore the data.<br>5.Model, predict and solve the problem.<br>6.Visualize, report, and present the problem solving steps and final solution.<br>7.Supply or submit the results. </p></blockquote>
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<p>大数据应用领域：</p>
<blockquote>
<blockquote>
<ul>
<li>互联网领域：搜索引擎、推荐系统、广告系统</li>
<li>电信领域：用户画像、用户行为分析</li>
<li>医药生物领域：DNA分析</li>
<li>视频领域：视频存储、视频分析</li>
<li>金融领域：信用卡欺诈分析、用户分析</li>
<li>矿产勘探领域：矿产石油勘察预测</li>
</ul></blockquote></blockquote>
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<h4 id="Spark简介"><a href="#Spark简介" class="headerlink" title="Spark简介"></a>Spark简介</h4><p><a href="https://www.cnblogs.com/shiyanlou/p/6183389.html" target="_blank" rel="noopener">参考1</a> <a href="https://www.shiyanlou.com/try/spark" target="_blank" rel="noopener">参考2</a></p>
<blockquote>
<p>Apache Spark 是一个新兴的大数据处理通用引擎，提供了分布式的内存抽象。Spark 拥有多种语言的函数式编程 API，提供了除 map 和 reduce 之外更多的运算符，这些操作是通过一个称作弹性分布式数据集(resilient distributed datasets, RDDs)的分布式数据框架进行的。</p>
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<p>Spark 是一种与 Hadoop 相似的开源集群计算环境，但是两者之间还存在一些不同之处，这些有用的不同之处使 Spark 在某些工作负载方面表现得更加优越，换句话说，Spark 启用了内存分布数据集，除了能够提供交互式查询外，它还可以优化迭代工作负载。</p>
</blockquote>
<blockquote>
<p>Spark 是在 Scala 语言中实现的，它将 Scala 用作其应用程序框架。与 Hadoop 不同，Spark 和 Scala 能够紧密集成，其中的 Scala 可以像操作本地集合对象一样轻松地操作分布式数据集。</p>
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<p>深度学习之CNN，学习网络结构，了解代码实现。卷积神经网络的知识点包括：局部感知、参数共享、池化。 </p>
<p>用于图像分类的CNN经典网络有：LeNet5、AlexNet、VGG、GoogleNet、ResNet等，今天我们主要学一下LeNet5结构。</p>
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<p>最近实现了一个小小的神经网络预测数字类别。下面就来看看是怎么实现的吧。go…</p>
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<p>开始新的生活，希望不忘记初心<br>即是被拒绝一千次，也要有一千零一次的崛起<br>似乎从一开始走出去，就会在方向中飘荡<br>请坚持下去，做你想要的<br>爱生活，爱自己</p>
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