diff --git a/quiz-app/src/assets/translations/zh-cn.json b/quiz-app/src/assets/translations/zh-cn.json index 8ad50bc5..5ae1ea9e 100644 --- a/quiz-app/src/assets/translations/zh-cn.json +++ b/quiz-app/src/assets/translations/zh-cn.json @@ -1,18 +1,18 @@ [{ "title": "初学者机器学习：测验", - "complete": "恭喜你，你完成了测验！", - "error": "对不起，再试一次", + "complete": "恭喜，您完成了测验！", + "error": "抱歉，再试一次", "quizzes": [{ "id": 1, - "title": "机器学习简介：讲座前测验", + "title": "机器学习简介：课前测验", "quiz": [{ - "questionText": "机器学习的应用都在我们身边", + "questionText": "机器学习的应用就在我们身边", "answerOptions": [{ - "answerText": "是", + "answerText": "True", "isCorrect": "true" }, { - "answerText": "否", + "answerText": "虚假", "isCorrect": "false" } ] @@ -20,7 +20,7 @@ { "questionText": "经典 ML 和深度学习之间的技术区别是什么？", "answerOptions": [{ - "answerText": "古典 ML 是先发明的", + "answerText": "经典机器学习最早被发明", "isCorrect": "false" }, { @@ -28,23 +28,23 @@ "isCorrect": "true" }, { - "answerText": "深度学习用于机器人", + "answerText": "深度学习在机器人使用", "isCorrect": "false" } ] }, { - "questionText": "为什么企业要使用 ML 策略？", + "questionText": "為什麼企業可能想要使用 ML 策略？", "answerOptions": [{ - "answerText": "自动化解决多维问题", + "answerText": "自動解決多維問題", "isCorrect": "false" }, { - "answerText": "根据客户类型定制购物体验", + "answerText": "定制根據客戶的類型的購物體驗", "isCorrect": "false" }, { - "answerText": "以上两者", + "answerText": "以上兩者", "isCorrect": "true" } ] @@ -53,15 +53,15 @@ }, { "id": 2, - "title": "机器学习简介：讲座后测验", + "title": "機器學習簡介：課後測驗", "quiz": [{ - "questionText": "机器学习算法旨在模拟", + "questionText": "機器學習算法是為了模擬", "answerOptions": [{ - "answerText": "智能机器", + "answerText": "智能機器", "isCorrect": "false" }, { - "answerText": "人脑", + "answerText": "人腦", "isCorrect": "true" }, { @@ -71,33 +71,33 @@ ] }, { - "questionText": "什么是经典 ML 技术的例子？", + "questionText": "什麼是經典 ML 技術的示例？", "answerOptions": [{ - "answerText": "自然语言处理", + "answerText": "自然語言處理", "isCorrect": "true" }, { - "answerText": "深度学习", + "answerText": "深度學習", "isCorrect": "false" }, { - "answerText": "神经网络", + "answerText": "神經網絡", "isCorrect": "false" } ] }, { - "questionText": "为什么每个人都要学习 ML 的基本知识？", + "questionText": "為什麼每個人都應該學習機器學習的基礎知識？", "answerOptions": [{ - "answerText": "学习 Ml 是有趣和访问的每个人", + "answerText": "學習ML的樂趣和方便大家", "isCorrect": "false" }, { - "answerText": "ML 策略正在许多行业和领域使用", + "answerText": "ML策略在許多行業和領域被使用", "isCorrect": "false" }, { - "answerText": "以上两者", + "answerText": "以上兩者", "isCorrect": "true" } ] @@ -106,43 +106,43 @@ }, { "id": 3, - "title": "机器学习史：课前测验", + "title": "預講座測驗：機器學習的歷史", "quiz": [{ - "questionText": "大约是'人工智能'一词的创造时间？", + "questionText": "“人工智能”這個詞是什麼時候創造的？", "answerOptions": [{ - "answerText": "20 世纪 80 年代", + "answerText": "1980年代", "isCorrect": "false" }, { - "answerText": "20 世纪 50 年代", + "answerText": "1950年代", "isCorrect": "true" }, { - "answerText": "20 世纪 30 年代", + "answerText": "1930年代", "isCorrect": "false" } ] }, { - "questionText": "谁是机器学习的早期先驱之一？", + "questionText": "誰是機器學習的早期先驅之一？", "answerOptions": [{ - "answerText": "艾伦·图灵", + "answerText": "阿蘭·圖靈", "isCorrect": "true" }, { - "answerText": "比尔·盖茨", + "answerText": "比爾蓋茨", "isCorrect": "false" }, { - "answerText": "摇动机器人", + "answerText": "摇晃机器人", "isCorrect": "false" } ] }, { - "questionText": "20 世纪 70 年代人工智能发展放缓的原因之一是什么？", + "questionText": "什么是该进步在AI在20世纪70年代放缓的原因之一？", "answerOptions": [{ - "answerText": "计算能力有限", + "answerText": "有限的计算能力", "isCorrect": "true" }, { @@ -150,7 +150,7 @@ "isCorrect": "false" }, { - "answerText": "国家间的冲突", + "answerText": "国家之间的冲突", "isCorrect": "false" } ] @@ -159,15 +159,15 @@ }, { "id": 4, - "title": "机器学习史：讲座后测验", + "title": "后期讲座测验：机器学习的历史", "quiz": [{ - "questionText": "什么是'粗糙'AI 系统的例子？", + "questionText": "什么是“肮脏的” AI系统的例子吗？", "answerOptions": [{ "answerText": "伊丽莎", "isCorrect": "true" }, { - "answerText": "哈克姆", + "answerText": "HACKML", "isCorrect": "false" }, { @@ -177,23 +177,23 @@ ] }, { - "questionText": "在'黄金年'期间开发的技术的例子是什么？", + "questionText": "在“黄金年”期间开发的技术的例子是什么？", "answerOptions": [{ "answerText": "块世界", "isCorrect": "true" }, { - "answerText": "吉波", + "answerText": "吉博", "isCorrect": "false" }, { - "answerText": "机器人狗", + "answerText": "机器狗", "isCorrect": "false" } ] }, { - "questionText": "人工智能领域的创建和扩展是基础性的？", + "questionText": "哪个事件是人工智能领域创建和扩展的基础？", "answerOptions": [{ "answerText": "图灵测试", "isCorrect": "false" @@ -203,7 +203,7 @@ "isCorrect": "true" }, { - "answerText": "AI 冬季", + "answerText": "AI冬季", "isCorrect": "false" } ] @@ -212,15 +212,15 @@ }, { "id": 5, - "title": "公平与机器学习：演讲前测验", + "title": "公平与机器学习：课前测验", "quiz": [{ - "questionText": "机器学习中的不公平可能发生", + "questionText": "机器学习中的不公平可能会发生", "answerOptions": [{ - "answerText": "有意", + "answerText": "故意地", "isCorrect": "false" }, { - "answerText": "不经意间", + "answerText": "无意中", "isCorrect": "false" }, { @@ -230,33 +230,33 @@ ] }, { - "questionText": "ML 中的'不公平'一词表示：", + "questionText": "ML 中的术语“不公平”意味着：", "answerOptions": [{ "answerText": "对一群人的伤害", "isCorrect": "true" }, { - "answerText": "伤害一个人", + "answerText": "对一个人的伤害", "isCorrect": "false" }, { - "answerText": "对大多数人的伤害", + "answerText": "对大多数人的危害", "isCorrect": "false" } ] }, { - "questionText": "五种主要伤害类型包括", + "questionText": "五种主要的伤害类型包括", "answerOptions": [{ - "answerText": "分配、服务质量、陈规定型观念、诋毁以及过度或不足代表", + "answerText": "分配，服务，定型，贬低，和过度或不足表示的质量", "isCorrect": "true" }, { - "answerText": "定位、服务质量、成见、诋毁以及过度或不足代表 ", + "answerText": "elocation，服务，定型，贬低，和过度或不足表示的质量 ", "isCorrect": "false" }, { - "answerText": "分配、服务质量、立体声、诋毁以及过度或不足代表 ", + "answerText": "分配，服务，Stereophonics的，弄黑，和过度或不足表示的质量 ", "isCorrect": "false" } ] @@ -265,9 +265,9 @@ }, { "id": 6, - "title": "公平与机器学习：讲座后测验", + "title": "公平与机器学习：课后测验", "quiz": [{ - "questionText": "模型中的不公平可能由", + "questionText": "模型中的不公平可能是由以下原引的", "answerOptions": [{ "answerText": "过度依赖历史数据", "isCorrect": "true" @@ -277,19 +277,19 @@ "isCorrect": "false" }, { - "answerText": "与历史数据过于紧密一致", + "answerText": "过于紧密对准历史数据", "isCorrect": "false" } ] }, { - "questionText": "为了减轻不公平，你可以", + "questionText": "为了减轻不公平，您可以", "answerOptions": [{ - "answerText": "识别伤害和受影响的群体", + "answerText": "确定危害和受影响的群体", "isCorrect": "false" }, { - "answerText": "定义公平指标", + "answerText": "确定公平度量", "isCorrect": "false" }, { @@ -299,17 +299,17 @@ ] }, { - "questionText": "公平学习是一个包， 可以", + "questionText": "Fairlearn是一个包，可以", "answerOptions": [{ "answerText": "使用公平性和性能指标比较多个模型", "isCorrect": "true" }, { - "answerText": "根据您的需求选择最佳型号", + "answerText": "选择最适合您需求的最佳模式", "isCorrect": "false" }, { - "answerText": "帮助您决定什么是公平的，什么是不公平的", + "answerText": "帮助你决定什么是公平，什么是不", "isCorrect": "false" } ] @@ -318,31 +318,31 @@ }, { "id": 7, - "title": "工具和技术：讲座前测验", + "title": "工具和技术：课前测验", "quiz": [{ - "questionText": "构建模型时，应：", + "questionText": "在构建模型时，您应该：", "answerOptions": [{ - "answerText": "准备数据，然后训练您的模型", + "answerText": "准备数据，然后训练模型", "isCorrect": "true" }, { - "answerText": "选择培训方法，然后准备数据", + "answerText": "选择训练方法，然后准备数据", "isCorrect": "false" }, { - "answerText": "调整参数，然后训练您的模型", + "answerText": "调整参数，然后训练你的模型", "isCorrect": "false" } ] }, { - "questionText": "您的数据的 ___将影响您的 ML 模型的质量", + "questionText": "您的数据的 ___ 将影响您的 ML 模型的质量", "answerOptions": [{ "answerText": "数量", "isCorrect": "false" }, { - "answerText": "样", + "answerText": "形状", "isCorrect": "false" }, { @@ -352,17 +352,17 @@ ] }, { - "questionText": "功能变量是：", + "questionText": "一个特征变量是：", "answerOptions": [{ - "answerText": "数据质量", + "answerText": "您的数据质量", "isCorrect": "false" }, { - "answerText": "数据的可测量属性", + "answerText": "您的数据的可测量特性", "isCorrect": "true" }, { - "answerText": "一排数据", + "answerText": "一行数据", "isCorrect": "false" } ] @@ -371,31 +371,31 @@ }, { "id": 8, - "title": "工具和技术：讲座后测验", + "title": "工具和技术：课后测验", "quiz": [{ "questionText": "您应该可视化您的数据，因为", "answerOptions": [{ - "answerText": "您可以发现离群值", + "answerText": "你可以发现异常值", "isCorrect": "false" }, { - "answerText": "您可以发现偏见的潜在原因", + "answerText": "你可以发现偏见的潜在原因", "isCorrect": "false" }, { - "answerText": "两者都是", + "answerText": "这两个", "isCorrect": "true" } ] }, { - "questionText": "将数据拆分为：", + "questionText": "将您的数据拆分为：", "answerOptions": [{ - "answerText": "训练和图灵集", + "answerText": "培训和图灵套", "isCorrect": "false" }, { - "answerText": "培训和测试集", + "answerText": "训练和测试集", "isCorrect": "true" }, { @@ -405,17 +405,17 @@ ] }, { - "questionText": "在各种 ML 库中启动培训过程的常见命令是：", + "questionText": "一个常见的命令来启动训练过程中各种ML库是：", "answerOptions": [{ - "answerText": "model.travel", + "answerText": "", "isCorrect": "false" }, { - "answerText": "模型.火车", + "answerText": "model.train", "isCorrect": "false" }, { - "answerText": "模型.适合", + "answerText": "model.fit", "isCorrect": "true" } ] @@ -424,11 +424,11 @@ }, { "id": 9, - "title": "回归简介：演讲前测验", + "title": "回归简介：课前测验", "quiz": [{ - "questionText": "这些变量中哪一个是数字变量？", + "questionText": "以下哪个变量是数字变量？", "answerOptions": [{ - "answerText": "高度", + "answerText": "身高", "isCorrect": "true" }, { @@ -436,13 +436,13 @@ "isCorrect": "false" }, { - "answerText": "毛色", + "answerText": "头发颜色", "isCorrect": "false" } ] }, { - "questionText": "这些变量中哪一个是绝对变量？", + "questionText": "以下哪个变量是分类变量？", "answerOptions": [{ "answerText": "心率", "isCorrect": "false" @@ -458,7 +458,7 @@ ] }, { - "questionText": "这些问题中哪一个是基于回归分析的问题？", + "questionText": "以下哪个问题是基于回归分析的问题？", "answerOptions": [{ "answerText": "预测学生的期末考试成绩", "isCorrect": "true" @@ -468,7 +468,7 @@ "isCorrect": "false" }, { - "answerText": "预测电子邮件是否是垃圾邮件", + "answerText": "预测电子邮件是否为垃圾邮件", "isCorrect": "false" } ] @@ -477,41 +477,41 @@ }, { "id": 10, - "title": "回归简介：演讲后测验", + "title": "回归简介：课后测验", "quiz": [{ - "questionText": "如果您的机器学习模型的训练精度为 95%，测试精度为 30%，那么它被称为什么类型的条件？", + "questionText": "如果你的机器学习模型的训练准确率为 95%，测试准确率为 30%，那么它被称为什么类型的条件？", "answerOptions": [{ - "answerText": "过度拟合", + "answerText": "过拟合", "isCorrect": "true" }, { - "answerText": "不合身", + "answerText": "欠拟合", "isCorrect": "false" }, { - "answerText": "双合身", + "answerText": "双接头", "isCorrect": "false" } ] }, { - "questionText": "从一组功能中识别重要特征的过程称为：", + "questionText": "从一组特征中识别重要特征的过程称为：", "answerOptions": [{ - "answerText": "功能提取", + "answerText": "特征提取", "isCorrect": "false" }, { - "answerText": "功能尺寸降低", + "answerText": "特征降维", "isCorrect": "false" }, { - "answerText": "功能选择", + "answerText": "特征选择", "isCorrect": "true" } ] }, { - "questionText": "使用 Scikit Learn 的'train_test_split（）'方法/功能将数据集拆分为一定比例的训练和测试数据集的过程称为：", + "questionText": "使用 Scikit Learn 的“train_test_split()”方法/函数将数据集拆分为一定比例的训练和测试数据集的过程称为：", "answerOptions": [{ "answerText": "交叉验证", "isCorrect": "false" @@ -521,7 +521,7 @@ "isCorrect": "true" }, { - "answerText": "将一个排除在验证外", + "answerText": "留一个验证", "isCorrect": "false" } ] @@ -530,25 +530,25 @@ }, { "id": 11, - "title": "准备和可视化回归数据：讲座前测验", + "title": "准备和可视化回归数据：课前测验", "quiz": [{ - "questionText": "这些 Python 模块中哪一个用于绘制数据的可视化图？", + "questionText": "以下哪个 Python 模块用于绘制数据的可视化？", "answerOptions": [{ - "answerText": "努皮", + "answerText": "NumPy的", "isCorrect": "false" }, { - "answerText": "科学学习", + "answerText": "Scikit-学习", "isCorrect": "false" }, { - "answerText": "马特普洛特利布", + "answerText": "Matplotlib", "isCorrect": "true" } ] }, { - "questionText": "如果您想要了解数据集数据点的分布或其他特征，则执行以下工作：", + "questionText": "如果你想了解的扩散或数据集的数据点的其他特征，然后执行：", "answerOptions": [{ "answerText": "数据可视化", "isCorrect": "true" @@ -558,23 +558,23 @@ "isCorrect": "false" }, { - "answerText": "列车测试拆分", + "answerText": "训练测试拆分", "isCorrect": "false" } ] }, { - "questionText": "哪些是机器学习项目中数据可视化步骤的一部分？", + "questionText": "以下哪一项是机器学习项目中数据可视化步骤的一部分？", "answerOptions": [{ - "answerText": "纳入特定的机器学习算法", + "answerText": "结合一定的机器学习算法", "isCorrect": "false" }, { - "answerText": "使用不同的绘图方法创建数据的图片表示", + "answerText": "使用不同的绘制方法创建数据的图示", "isCorrect": "true" }, { - "answerText": "使数据集值正常化", + "answerText": "规范化数据集的值", "isCorrect": "false" } ] @@ -583,27 +583,27 @@ }, { "id": 12, - "title": "准备和可视化回归数据：讲座后测验", + "title": "准备和可视化回归数据：课后测验", "quiz": [{ - "questionText": "如果您想要检查数据集中是否存在缺失值，则根据此课程，这些代码片段中哪一个是正确的？假设数据集存储在名为'数据集'的变量中，该变量是熊猫数据帧对象。", + "questionText": "如果您想检查数据集中是否存在缺失值，根据本课程，以下哪些代码片段是正确的？ 假设数据集存储在名为“dataset”的变量中，该变量是 Pandas DataFrame 对象。", "answerOptions": [{ - "answerText": "数据集. isnull （. 和 （）", + "answerText": "数据集.isnull().sum()", "isCorrect": "true" }, { - "answerText": "查找错误（数据集）", + "answerText": "findMissing（数据集）", "isCorrect": "false" }, { - "answerText": "和（空（数据集））", + "answerText": "总和（空（数据集））", "isCorrect": "false" } ] }, { - "questionText": "当您想了解数据集中不同数据点组的分布时，这些绘图方法中哪一个有用？", + "questionText": "当您想了解数据集中不同数据点组的分布时，以下哪种绘图方法有用？", "answerOptions": [{ - "answerText": "散布图", + "answerText": "散点图", "isCorrect": "false" }, { @@ -611,7 +611,7 @@ "isCorrect": "false" }, { - "answerText": "酒吧情节", + "answerText": "条形图", "isCorrect": "true" } ] @@ -627,7 +627,7 @@ "isCorrect": "true" }, { - "answerText": "在数据集中查找离群值的存在", + "answerText": "查找数据集中是否存在异常值", "isCorrect": "false" } ] @@ -636,9 +636,9 @@ }, { "id": 13, - "title": "线性和多面回归：演讲前测验", + "title": "线性和多项式回归：课前测验", "quiz": [{ - "questionText": "马特普洛特利布是一个", + "questionText": "Matplotlib 是一个", "answerOptions": [{ "answerText": "绘图库", "isCorrect": "false" @@ -648,15 +648,15 @@ "isCorrect": "true" }, { - "answerText": "借阅库", + "answerText": "借阅图书馆", "isCorrect": "false" } ] }, { - "questionText": "线性回归使用以下图来绘制变量之间的关系", + "questionText": "线性回归使用以下内容绘制变量之间的关系", "answerOptions": [{ - "answerText": "直线", + "answerText": "一条直线", "isCorrect": "true" }, { @@ -670,17 +670,17 @@ ] }, { - "questionText": "一个好的线性回归模型具有 ___ 相关系数", + "questionText": "一个好的线性回归模型有一个 ___ 相关系数", "answerOptions": [{ - "answerText": "低", + "answerText": "低的", "isCorrect": "false" }, { - "answerText": "轩", + "answerText": "高的", "isCorrect": "true" }, { - "answerText": "平", + "answerText": "平坦的", "isCorrect": "false" } ] @@ -689,9 +689,9 @@ }, { "id": 14, - "title": "线性和多面回归：讲座后测验", + "title": "线性和多项式回归：课后测验", "quiz": [{ - "questionText": "如果您的数据是非线性的，请尝试 '回归'类型", + "questionText": "如果您的数据是非线性的，请尝试 ___ 类型的回归", "answerOptions": [{ "answerText": "线性", "isCorrect": "false" @@ -707,33 +707,33 @@ ] }, { - "questionText": "这些都是类型的回归方法", + "questionText": "这些是所有类型的回归方法", "answerOptions": [{ - "answerText": "假步、岭、拉索和弹性网", + "answerText": "Falsestep、Ridge、Lasso 和 Elasticnet", "isCorrect": "false" }, { - "answerText": "步进， 岭， 拉索和弹性网", + "answerText": "Stepwise、Ridge、Lasso 和 Elasticnet", "isCorrect": "true" }, { - "answerText": "步进， 岭， 拉里亚特和弹性网", + "answerText": "Stepwise、Ridge、Lariat 和 Elasticnet", "isCorrect": "false" } ] }, { - "questionText": "最小方块回归意味着回归线周围的所有数据点都是：", + "questionText": "最小二乘回归意味着回归线周围的所有数据点是：", "answerOptions": [{ "answerText": "平方，然后减去", "isCorrect": "false" }, { - "answerText": "乘以", + "answerText": "相乘", "isCorrect": "false" }, { - "answerText": "平方，然后加起来", + "answerText": "平方然后加起来", "isCorrect": "true" } ] @@ -742,41 +742,41 @@ }, { "id": 15, - "title": "后勤回归：课前测验", + "title": "逻辑回归：课前测验", "quiz": [{ - "questionText": "使用物流回归来预测", + "questionText": "采用Logistic回归预测", "answerOptions": [{ - "answerText": "苹果是否成熟", + "answerText": "苹果是否成熟与否", "isCorrect": "true" }, { - "answerText": "一个月内能卖出多少张票", + "answerText": "一个月能卖多少票", "isCorrect": "false" }, { - "answerText": "天空明天下午 6 点会转动什么颜色", + "answerText": "明天下午 6 点天空会变成什么颜色", "isCorrect": "false" } ] }, { - "questionText": "后勤回归类型包括", + "questionText": "逻辑回归的类型包括", "answerOptions": [{ - "answerText": "多名和枢机主教", + "answerText": "多项式和基数", "isCorrect": "false" }, { - "answerText": "多名和序", + "answerText": "多项式和序数", "isCorrect": "true" }, { - "answerText": "校长和序人", + "answerText": "本金和序", "isCorrect": "false" } ] }, { - "questionText": "您的数据相关性较弱。最佳类型的回归使用是：", + "questionText": "您的数据具有弱相关性。 最好使用的回归类型是：", "answerOptions": [{ "answerText": "物流", "isCorrect": "true" @@ -795,31 +795,31 @@ }, { "id": 16, - "title": "后勤回归：课后测验", + "title": "逻辑回归：课后测验", "quiz": [{ - "questionText": "海出生是一种类型", + "questionText": "Seaborn是一种类型的", "answerOptions": [{ "answerText": "数据可视化库", "isCorrect": "true" }, { - "answerText": "制图库", + "answerText": "映射库", "isCorrect": "false" }, { - "answerText": "数学库", + "answerText": "数学图书馆", "isCorrect": "false" } ] }, { - "questionText": "混淆矩阵也称为：", + "questionText": "混淆矩阵也被称为一个:", "answerOptions": [{ - "answerText": "错误矩阵", + "answerText": "误差矩阵", "isCorrect": "true" }, { - "answerText": "真相矩阵", + "answerText": "真值矩阵", "isCorrect": "false" }, { @@ -829,17 +829,17 @@ ] }, { - "questionText": "一个好的模型将有：", + "questionText": "一个好的模型将具有：", "answerOptions": [{ - "answerText": "大量的误报和真底片在其混乱矩阵", + "answerText": "其混淆矩阵中存在大量误报和真负", "isCorrect": "false" }, { - "answerText": "大量的真正的积极和真正的负面在其混乱矩阵", + "answerText": "其混淆矩阵中有大量真阳性和真阴性", "isCorrect": "true" }, { - "answerText": "大量的真正反误矩阵", + "answerText": "其混淆矩阵中有大量真阳性和假阴性", "isCorrect": "false" } ] @@ -848,25 +848,25 @@ }, { "id": 17, - "title": "构建 Web 应用程序：讲座前测验", + "title": "构建 Web 应用程序：课前测验", "quiz": [{ "questionText": "ONNX 代表什么？", "answerOptions": [{ - "answerText": "通过神经网络交换", + "answerText": "Over Neural Network Exchange", "isCorrect": "false" }, { - "answerText": "开放神经网络交换", + "answerText": "Open Neural Network Exchange", "isCorrect": "true" }, { - "answerText": "输出神经网络交换", + "answerText": "Output Neural Network Exchange", "isCorrect": "false" } ] }, { - "questionText": "弗拉斯克是如何由它的创造者定义的？", + "questionText": "Flask 的创建者是如何定义的？", "answerOptions": [{ "answerText": "迷你框架", "isCorrect": "false" @@ -876,23 +876,23 @@ "isCorrect": "false" }, { - "answerText": "微型框架", + "answerText": "微框架", "isCorrect": "true" } ] }, { - "questionText": "Python 的泡菜模块是做什么的", + "questionText": "Python 的 Pickle 模块有什么作用", "answerOptions": [{ "answerText": "序列化 Python 对象", "isCorrect": "false" }, { - "answerText": "去序列化 Python 对象", + "answerText": "反序列化 Python 对象", "isCorrect": "false" }, { - "answerText": "序列化和去序列化 Python 对象", + "answerText": "序列化和反序列化 Python 对象", "isCorrect": "true" } ] @@ -901,27 +901,27 @@ }, { "id": 18, - "title": "构建 Web 应用程序：讲座后测验", + "title": "构建 Web 应用程序：课后测验", "quiz": [{ - "questionText": "我们可以使用哪些工具使用 Python 在网络上托管预先训练的模型？", + "questionText": "我们可以使用哪些工具来使用 Python 在 Web 上托管预训练模型？", "answerOptions": [{ - "answerText": "瓶", + "answerText": "Flask", "isCorrect": "true" }, { - "answerText": "滕索弗.js", + "answerText": "TensorFlow.js", "isCorrect": "false" }, { - "answerText": ".js", + "answerText": "onnx.js", "isCorrect": "false" } ] }, { - "questionText": "萨斯代表什么？", + "questionText": "SaaS代表什么？", "answerOptions": [{ - "answerText": "系统作为服务", + "answerText": "系统即服务", "isCorrect": "false" }, { @@ -929,19 +929,19 @@ "isCorrect": "true" }, { - "answerText": "安全作为一种服务", + "answerText": "安全即服务", "isCorrect": "false" } ] }, { - "questionText": "科学学习的标签编码器库是做什么的？", + "questionText": "Scikit-learn 的 LabelEncoder 库有什么作用？", "answerOptions": [{ - "answerText": "按字母顺序编码数据", + "answerText": "按字母顺序对数据进行编码", "isCorrect": "true" }, { - "answerText": "以数字编码数据", + "answerText": "数字编码数据", "isCorrect": "false" }, { @@ -954,11 +954,11 @@ }, { "id": 19, - "title": "分类1：课前测验", + "title": "分类 1：课前测验", "quiz": [{ - "questionText": "分类是一种监督学习的形式，有很多共同之处", + "questionText": "分类是监督学习的一种形式，有很多共同点", "answerOptions": [{ - "answerText": "时间系列", + "answerText": "时间序列", "isCorrect": "false" }, { @@ -974,7 +974,7 @@ { "questionText": "分类可以帮助回答什么问题？", "answerOptions": [{ - "answerText": "这封邮件是不是垃圾邮件？", + "answerText": "这是垃圾邮件或没有？", "isCorrect": "true" }, { @@ -982,7 +982,7 @@ "isCorrect": "false" }, { - "answerText": "生命的意义何在？", + "answerText": "什么是生命的意义？", "isCorrect": "false" } ] @@ -994,7 +994,7 @@ "isCorrect": "false" }, { - "answerText": "清洁和平衡您的数据", + "answerText": "清理和平衡您的数据", "isCorrect": "true" }, { @@ -1007,7 +1007,7 @@ }, { "id": 20, - "title": "分类1：课后测验", + "title": "分类 1：课后测验", "quiz": [{ "questionText": "什么是多类问题？", "answerOptions": [{ @@ -1015,7 +1015,7 @@ "isCorrect": "false" }, { - "answerText": "将数据点分类为几个类之一的任务", + "answerText": "将数据点分类为几个类别之一的任务", "isCorrect": "true" }, { @@ -1025,13 +1025,13 @@ ] }, { - "questionText": "清理经常性或无益的数据以帮助分类器解决您的问题非常重要。", + "questionText": "清理复发或无用的数据，以帮助您分类解决你的问题很重要。", "answerOptions": [{ - "answerText": "真", + "answerText": "真的", "isCorrect": "true" }, { - "answerText": "错误", + "answerText": "虚假", "isCorrect": "false" } ] @@ -1039,15 +1039,15 @@ { "questionText": "平衡数据的最佳理由是什么？", "answerOptions": [{ - "answerText": "不平衡的数据在可视化方面看起来很糟糕", + "answerText": "不平衡的数据在可视化中看起来很糟糕", "isCorrect": "false" }, { - "answerText": "平衡数据会产生更好的结果，因为 ML 模型不会偏向一个类", + "answerText": "平衡您的数据会产生更好的结果，因为 ML 模型不会偏向某一类", "isCorrect": "true" }, { - "answerText": "平衡数据为您提供了更多的数据点", + "answerText": "平衡数据可为您提供更多数据点", "isCorrect": "false" } ] @@ -1056,27 +1056,27 @@ }, { "id": 21, - "title": "分类2：课前测验", + "title": "分类 2：课前测验", "quiz": [{ - "questionText": "平衡、干净的数据产生最佳的分类结果", + "questionText": "平衡、干净的数据产生最好的分类结果", "answerOptions": [{ - "answerText": "真", + "answerText": "真的", "isCorrect": "true" }, { - "answerText": "错误", + "answerText": "false", "isCorrect": "false" } ] }, { - "questionText": "如何选择正确的分类器？", + "questionText": "如何选择合适的分类器？", "answerOptions": [{ "answerText": "了解哪些分类器最适合哪些场景", "isCorrect": "false" }, { - "answerText": "受过教育的猜测和检查", + "answerText": "有根据的猜测和检查", "isCorrect": "false" }, { @@ -1086,7 +1086,7 @@ ] }, { - "questionText": "分类是一种类型", + "questionText": "分类是一种类型的", "answerOptions": [{ "answerText": "NLP", "isCorrect": "false" @@ -1096,7 +1096,7 @@ "isCorrect": "true" }, { - "answerText": "程序设计语言", + "answerText": "编程语言", "isCorrect": "false" } ] @@ -1105,27 +1105,27 @@ }, { "id": 22, - "title": "分类2：课后测验", + "title": "分类 2：课后测验", "quiz": [{ - "questionText": "什么是'解算器'？", + "questionText": "什么是“求解器”？", "answerOptions": [{ - "answerText": "仔细检查您工作的人", + "answerText": "谁的人双重检查你的工作", "isCorrect": "false" }, { - "answerText": "优化问题中使用的算法", + "answerText": "用于优化问题的算法", "isCorrect": "true" }, { - "answerText": "机器学习技术", + "answerText": "一种机器学习技术", "isCorrect": "false" } ] }, { - "questionText": "我们在这节课中使用了哪个分类器？", + "questionText": "我们在本课中使用了哪个分类器？", "answerOptions": [{ - "answerText": "物流回归", + "answerText": "逻辑回归", "isCorrect": "true" }, { @@ -1133,7 +1133,7 @@ "isCorrect": "false" }, { - "answerText": "一对全多类", + "answerText": "一VS-所有多类", "isCorrect": "false" } ] @@ -1149,7 +1149,7 @@ "isCorrect": "false" }, { - "answerText": "通过查看历史数据，了解该算法在解决类似问题时有多好", + "answerText": "通过查看历史数据，了解该算法在解决类似问题方面的表现", "isCorrect": "false" } ] @@ -1158,15 +1158,15 @@ }, { "id": 23, - "title": "分类3：课前测验", + "title": "分类 3：课前测验", "quiz": [{ - "questionText": "要尝试的一个好的初始分类器是：", + "questionText": "良好的初始分类是尝试：", "answerOptions": [{ "answerText": "线性 SVC", "isCorrect": "true" }, { - "answerText": "K-手段", + "answerText": "K均值", "isCorrect": "false" }, { @@ -1176,7 +1176,7 @@ ] }, { - "questionText": "正规化控制：", + "questionText": "正则化控制：", "answerOptions": [{ "answerText": "参数的影响", "isCorrect": "true" @@ -1186,23 +1186,23 @@ "isCorrect": "false" }, { - "answerText": "离群值的影响", + "answerText": "异常值的影响", "isCorrect": "false" } ] }, { - "questionText": "K-邻居分类器可用于：", + "questionText": "K-Neighbors 分类器可用于：", "answerOptions": [{ "answerText": "监督学习", "isCorrect": "false" }, { - "answerText": "无人监督的学习", + "answerText": "无监督学习", "isCorrect": "false" }, { - "answerText": "两者都是", + "answerText": "这两个", "isCorrect": "true" } ] @@ -1211,9 +1211,9 @@ }, { "id": 24, - "title": "分类3：课后测验", + "title": "分类 3：课后测验", "quiz": [{ - "questionText": "支持矢量分类器可用于", + "questionText": "支持向量分类器可用于", "answerOptions": [{ "answerText": "分类", "isCorrect": "false" @@ -1223,7 +1223,7 @@ "isCorrect": "false" }, { - "answerText": "两者都是", + "answerText": "这两个", "isCorrect": "true" } ] @@ -1231,27 +1231,27 @@ { "questionText": "随机森林是一种___类型的分类器", "answerOptions": [{ - "answerText": "整体", + "answerText": "合奏", "isCorrect": "true" }, { - "answerText": "掩饰", + "answerText": "拆解", "isCorrect": "false" }, { - "answerText": "聚集", + "answerText": "集合", "isCorrect": "false" } ] }, { - "questionText": "阿达布斯特以：", + "questionText": "Adaboost 以：", "answerOptions": [{ - "answerText": "关注分类错误项目的权重", + "answerText": "注重分类错误项的权重", "isCorrect": "true" }, { - "answerText": "关注离群值", + "answerText": "关注异常值", "isCorrect": "false" }, { @@ -1264,47 +1264,47 @@ }, { "id": 25, - "title": "分类4：课前测验", + "title": "分类 4：课前测验", "quiz": [{ - "questionText": "建议系统可用于", + "questionText": "推荐系统可用于", "answerOptions": [{ - "answerText": "推荐一家好餐厅", + "answerText": "推荐一家不错的餐厅", "isCorrect": "false" }, { - "answerText": "推荐时尚尝试", + "answerText": "推荐时装尝试", "isCorrect": "false" }, { - "answerText": "两者都是", + "answerText": "这两个", "isCorrect": "true" } ] }, { - "questionText": "将模型嵌入 Web 应用有助于它具有离线能力", + "questionText": "在 Web 应用程序中嵌入模型有助于使其具有离线功能", "answerOptions": [{ - "answerText": "真", + "answerText": "真的", "isCorrect": "true" }, { - "answerText": "错误", + "answerText": "虚假", "isCorrect": "false" } ] }, { - "questionText": "Onnx 运行时间可用于", + "questionText": "Onnx 运行时可用于", "answerOptions": [{ - "answerText": "在 Web 应用中运行模型", + "answerText": "在 Web 应用程序中运行模型", "isCorrect": "true" }, { - "answerText": "培训模式", + "answerText": "训练模型", "isCorrect": "false" }, { - "answerText": "超参数调谐", + "answerText": "超参数调优", "isCorrect": "false" } ] @@ -1313,7 +1313,7 @@ }, { "id": 26, - "title": "分类4：课后测验", + "title": "分类 4：课后测验", "quiz": [{ "questionText": "Netron 应用程序可帮助您：", "answerOptions": [{ @@ -1331,29 +1331,29 @@ ] }, { - "questionText": "转换您的 Scikit 学习模型，以便与 Onnx 一起使用：", + "questionText": "使用以下方法转换您的 Scikit-learn 模型以与 Onnx 一起使用：", "answerOptions": [{ - "answerText": "斯克莱恩应用程序", + "answerText": "sklearn-app", "isCorrect": "false" }, { - "answerText": "斯克莱恩网", + "answerText": "sklearn-web", "isCorrect": "false" }, { - "answerText": "斯克莱恩 - 奥恩克斯", + "answerText": "sklearn-onnx", "isCorrect": "true" } ] }, { - "questionText": "在 Web 应用中使用您的模型称为：", + "questionText": "在网络应用程序中使用您的模型称为：", "answerOptions": [{ - "answerText": "推理", + "answerText": "推论", "isCorrect": "true" }, { - "answerText": "干涉", + "answerText": "干扰", "isCorrect": "false" }, { @@ -1366,19 +1366,19 @@ }, { "id": 27, - "title": "集群简介：讲座前测验", + "title": "聚类简介：课前测验", "quiz": [{ - "questionText": "聚类的真实例子是", + "questionText": "聚类的一个现实例子是", "answerOptions": [{ "answerText": "设置餐桌", "isCorrect": "false" }, { - "answerText": "整理衣物", + "answerText": "排序洗衣", "isCorrect": "true" }, { - "answerText": "买", + "answerText": "杂货店购物", "isCorrect": "false" } ] @@ -1394,7 +1394,7 @@ "isCorrect": "false" }, { - "answerText": "两者都是", + "answerText": "这两个", "isCorrect": "true" } ] @@ -1406,7 +1406,7 @@ "isCorrect": "false" }, { - "answerText": "无人监督的学习", + "answerText": "无监督学习", "isCorrect": "true" }, { @@ -1419,9 +1419,9 @@ }, { "id": 28, - "title": "集群简介：讲座后测验", + "title": "聚类简介：课后测验", "quiz": [{ - "questionText": "欧几里德几何排列沿", + "questionText": "欧几里德几何布置沿", "answerOptions": [{ "answerText": "飞机", "isCorrect": "true" @@ -1431,15 +1431,15 @@ "isCorrect": "false" }, { - "answerText": "领域", + "answerText": "球体", "isCorrect": "false" } ] }, { - "questionText": "聚类数据的密度与其相关", + "questionText": "您的群集数据的密度有关，其", "answerOptions": [{ - "answerText": "noise", + "answerText": "噪音", "isCorrect": "true" }, { @@ -1455,15 +1455,15 @@ { "questionText": "最著名的聚类算法是", "answerOptions": [{ - "answerText": "k- 手段", + "answerText": "k均值", "isCorrect": "true" }, { - "answerText": "k - 中", + "answerText": "K-中间", "isCorrect": "false" }, { - "answerText": "k - mart", + "answerText": "k-mart", "isCorrect": "false" } ] @@ -1472,11 +1472,11 @@ }, { "id": 29, - "title": "K-平均聚类：讲座前测验", + "title": "K-Means 聚类：课前测验", "quiz": [{ - "questionText": "K-手段来源于：", + "questionText": "K-Means 源自：", "answerOptions": [{ - "answerText": "电机工程", + "answerText": "电气工程", "isCorrect": "false" }, { @@ -1492,7 +1492,7 @@ { "questionText": "一个好的剪影分数意味着：", "answerOptions": [{ - "answerText": "集群分离良好，定义清晰", + "answerText": "集群分离良好且定义明确", "isCorrect": "true" }, { @@ -1500,7 +1500,7 @@ "isCorrect": "false" }, { - "answerText": "有许多集群", + "answerText": "有很多集群", "isCorrect": "false" } ] @@ -1508,15 +1508,15 @@ { "questionText": "方差为：", "answerOptions": [{ - "answerText": "与平均值的平方差异的平均值", + "answerText": "与平均值的平方差的平均值", "isCorrect": "false" }, { - "answerText": "如果聚类变得过高，则会出现问题", + "answerText": "如果它变得太高，则会出现聚类问题", "isCorrect": "false" }, { - "answerText": "两者都是", + "answerText": "这两个", "isCorrect": "true" } ] @@ -1525,11 +1525,11 @@ }, { "id": 30, - "title": "K-平均分组：讲座后测验", + "title": "K-Means 聚类：课后测验", "quiz": [{ - "questionText": "沃罗诺伊图显示：", + "questionText": "Voronoi 图显示：", "answerOptions": [{ - "answerText": "聚类的方差", + "answerText": "集群的方差", "isCorrect": "false" }, { @@ -1545,27 +1545,27 @@ { "questionText": "惯性是", "answerOptions": [{ - "answerText": "衡量内部连贯性聚类的指标", + "answerText": "衡量内部连贯性集群的程度", "isCorrect": "true" }, { - "answerText": "测量组移动的量", + "answerText": "衡量集群移动的程度", "isCorrect": "false" }, { - "answerText": "集群质量的衡量标准", + "answerText": "衡量集群质量", "isCorrect": "false" } ] }, { - "questionText": "使用 K 手段，您必须首先确定'k'值", + "questionText": "使用K-意味着，你必须首先确定的“K”值", "answerOptions": [{ - "answerText": "真", + "answerText": "真的", "isCorrect": "true" }, { - "answerText": "错误", + "answerText": "虚假", "isCorrect": "false" } ] @@ -1574,9 +1574,9 @@ }, { "id": 31, - "title": "NLP 简介：演讲前测验", + "title": "NLP 简介：课前测验", "quiz": [{ - "questionText": "Nlp 在这些课程中代表什么？", + "questionText": "NLP 在这些课程中代表什么？", "answerOptions": [{ "answerText": "神经语言处理", "isCorrect": "false" @@ -1592,13 +1592,13 @@ ] }, { - "questionText": "伊丽莎是一个早期的机器人， 充当计算机", + "questionText": "伊丽莎是一个早期的机器人是充当计算机", "answerOptions": [{ - "answerText": "心理医生", + "answerText": "治疗师", "isCorrect": "true" }, { - "answerText": "大夫", + "answerText": "医生", "isCorrect": "false" }, { @@ -1608,7 +1608,7 @@ ] }, { - "questionText": "艾伦·图灵的'图灵测试'试图确定计算机是否", + "questionText": "艾伦图灵的“图灵测试”试图确定计算机是否", "answerOptions": [{ "answerText": "与人类无法区分", "isCorrect": "false" @@ -1627,9 +1627,9 @@ }, { "id": 32, - "title": "NLP 简介：演讲后测验", + "title": "NLP 简介：课后测验", "quiz": [{ - "questionText": "约瑟夫 · 韦森鲍姆发明了机器人", + "questionText": "Joseph Weizenbaum 发明了机器人", "answerOptions": [{ "answerText": "以利沙", "isCorrect": "false" @@ -1639,39 +1639,39 @@ "isCorrect": "true" }, { - "answerText": "艾萝依", + "answerText": "埃洛伊丝", "isCorrect": "false" } ] }, { - "questionText": "对话机器人根据", + "questionText": "一个对话的机器人给出了基于输出", "answerOptions": [{ - "answerText": "随机选择预先定义的选择", + "answerText": "随机选择预定义选项", "isCorrect": "false" }, { - "answerText": "分析输入和使用机器智能", + "answerText": "分析输入并使用机器智能", "isCorrect": "false" }, { - "answerText": "两者都是", + "answerText": "这两个", "isCorrect": "true" } ] }, { - "questionText": "您如何使机器人更有效？", + "questionText": "你如何让机器人更有效？", "answerOptions": [{ "answerText": "通过问更多的问题。", "isCorrect": "false" }, { - "answerText": "通过给它提供更多的数据并相应地进行培训", + "answerText": "通过喂养它更多的数据，并相应地训练它", "isCorrect": "true" }, { - "answerText": "机器人是哑巴， 它不能学习:(", + "answerText": "该机器人是哑巴，所以不能学习：(", "isCorrect": "false" } ] @@ -1680,15 +1680,15 @@ }, { "id": 33, - "title": "NLP 任务：讲座前测验", + "title": "NLP 任务：课前测验", "quiz": [{ - "questionText": "令牌化", + "questionText": "代币化", "answerOptions": [{ "answerText": "通过标点符号拆分文本", "isCorrect": "false" }, { - "answerText": "将文本拆分为单独的代币（文字）", + "answerText": "将文本拆分为单独的标记（单词）", "isCorrect": "true" }, { @@ -1698,29 +1698,29 @@ ] }, { - "questionText": "嵌入", + "questionText": "的嵌入", "answerOptions": [{ - "answerText": "以数字形式转换文本数据，以便单词可以聚类", + "answerText": "以数字方式转换文本数据，以便单词可以聚类", "isCorrect": "true" }, { - "answerText": "将单词嵌入短语", + "answerText": "将单词嵌入到短语中", "isCorrect": "false" }, { - "answerText": "将句子嵌入段落", + "answerText": "将句子嵌入到段落中", "isCorrect": "false" } ] }, { - "questionText": "语音标记部分", + "questionText": "词性标注", "answerOptions": [{ - "answerText": "将句子除以其部分的语音", + "answerText": "按词性划分句子", "isCorrect": "false" }, { - "answerText": "采取象征性的单词， 并标记他们说话的一部分", + "answerText": "获取标记词并按其词性标记它们", "isCorrect": "true" }, { @@ -1733,35 +1733,35 @@ }, { "id": 34, - "title": "NLP 任务：讲座后测验", + "title": "NLP 任务：课后测验", "quiz": [{ - "questionText": "构建单词重复使用的频率字典：", + "questionText": "使用以下方法构建单词重复出现频率的字典：", "answerOptions": [{ - "answerText": "单词和短语词典", + "answerText": "单词和短语的字典", "isCorrect": "false" }, { - "answerText": "单词和短语频率", + "answerText": "词和词组频率", "isCorrect": "true" }, { - "answerText": "单词和短语库", + "answerText": "单词和短语的图书馆", "isCorrect": "false" } ] }, { - "questionText": "N 克指", + "questionText": "N-gram 指的是", "answerOptions": [{ - "answerText": "文本可以分为一定长度的单词序列", + "answerText": "文本可以被分成一组长度的单词序列", "isCorrect": "true" }, { - "answerText": "一个单词可以分为一定长度的字符序列", + "answerText": "一个单词可以被拆分成一组固定长度的字符序列", "isCorrect": "false" }, { - "answerText": "文本可以拆分为一定长度的段落", + "answerText": "文本可以被分成一组长度的段落", "isCorrect": "false" } ] @@ -1769,11 +1769,11 @@ { "questionText": "情绪分析", "answerOptions": [{ - "answerText": "分析积极或消极的短语", + "answerText": "分析一个短语为阳性或阴性", "isCorrect": "true" }, { - "answerText": "分析一个感伤的短语", + "answerText": "分析一个多愁善感的短语", "isCorrect": "false" }, { @@ -1786,15 +1786,15 @@ }, { "id": 35, - "title": "NLP 和翻译：讲座前测验", + "title": "NLP 和翻译：课前测验", "quiz": [{ "questionText": "天真翻译", "answerOptions": [{ - "answerText": "仅翻译单词", + "answerText": "只翻译单词", "isCorrect": "true" }, { - "answerText": "仅翻译单词", + "answerText": "翻译句子结构", "isCorrect": "false" }, { @@ -1804,9 +1804,9 @@ ] }, { - "questionText": "文本的 [语料库] 指", + "questionText": "文本的*语料库*指的是", "answerOptions": [{ - "answerText": "少量文本", + "answerText": "少量文字", "isCorrect": "false" }, { @@ -1814,7 +1814,7 @@ "isCorrect": "true" }, { - "answerText": "一个标准文本", + "answerText": "一标准文本", "isCorrect": "false" } ] @@ -1839,15 +1839,15 @@ }, { "id": 36, - "title": "提高翻译的准确性", + "title": "NLP 和翻译：课后测验", "quiz": [{ - "questionText": "文本博客的翻译库基础是：", + "questionText": "底层 TextBlob 的翻译库是：", "answerOptions": [{ "answerText": "谷歌翻译", "isCorrect": "true" }, { - "answerText": "必应", + "answerText": "BING", "isCorrect": "false" }, { @@ -1857,33 +1857,33 @@ ] }, { - "questionText": "要使用您需要的'blob.翻译: '", + "questionText": "要使用 `blob.translate` 你需要：", "answerOptions": [{ "answerText": "互联网连接", "isCorrect": "true" }, { - "answerText": "互联网连接", + "answerText": "一本字典", "isCorrect": "false" }, { - "answerText": "爪哇脚本", + "answerText": "JavaScript", "isCorrect": "false" } ] }, { - "questionText": "为了确定情绪，ML 的方法是：", + "questionText": "要确定情绪，ML 方法将是：", "answerOptions": [{ - "answerText": "应用回归技术手动生成的意见和分数，并查找模式", + "answerText": "将回归技术应用于手动生成的意见和分数并寻找模式", "isCorrect": "false" }, { - "answerText": "将 NLP 技术应用于手动生成的意见和分数并查找模式", + "answerText": "将 NLP 技术应用于手动生成的意见和分数并寻找模式", "isCorrect": "true" }, { - "answerText": "将聚类技术应用于手动生成的意见和分数并查找模式", + "answerText": "将聚类技术应用于手动生成的意见和分数并寻找模式", "isCorrect": "false" } ] @@ -1892,15 +1892,15 @@ }, { "id": 37, - "title": "NLP 4： 讲座前测验", + "title": "NLP 4：课前测验", "quiz": [{ - "questionText": "我们可以从人类撰写或发言的文本中获得哪些信息？", + "questionText": "我们可以从人类书写或说出的文本中获得哪些信息？", "answerOptions": [{ - "answerText": "模式和频率", + "answerText": "图案和频率", "isCorrect": "false" }, { - "answerText": "情绪和意义", + "answerText": "情感和意义", "isCorrect": "false" }, { @@ -1910,33 +1910,33 @@ ] }, { - "questionText": "什么是情绪分析？", + "questionText": "什么是情感分析？", "answerOptions": [{ - "answerText": "研究家族传家宝是否有感伤价值", + "answerText": "一项关于传家宝是否具有情感价值的研究", "isCorrect": "false" }, { - "answerText": "系统识别、提取、量化和研究情感状态和主观信息的方法", + "answerText": "一种系统地识别、提取、量化和研究情感状态和主观信息的方法", "isCorrect": "true" }, { - "answerText": "判断某人是悲伤还是快乐的能力", + "answerText": "告诉某人是否是悲伤或快乐的能力", "isCorrect": "false" } ] }, { - "questionText": "使用酒店评论、Python 和情绪分析的数据集可以回答什么问题？", + "questionText": "使用酒店评论、Python 和情感分析的数据集可以回答什么问题？", "answerOptions": [{ "answerText": "评论中最常用的单词和短语是什么？", "isCorrect": "true" }, { - "answerText": "哪个度假村有最好的游泳池？", + "answerText": "哪个度假村的游泳池最好？", "isCorrect": "false" }, { - "answerText": "这家酒店有代客泊车吗？", + "answerText": "有没有在这家酒店代客泊车？", "isCorrect": "false" } ] @@ -1947,25 +1947,25 @@ "id": 38, "title": "NLP 4：课后测验", "quiz": [{ - "questionText": "NLP 的本质是什么？", + "questionText": "什么是NLP的本质是什么？", "answerOptions": [{ - "answerText": "将人类语言分为快乐或悲伤", + "answerText": "分类人类语言转化成快乐或悲伤", "isCorrect": "false" }, { - "answerText": "解释意义或情感， 而不必有一个人这样做", + "answerText": "无需人工即可解释意义或情感", "isCorrect": "true" }, { - "answerText": "发现情绪的离群值并检查它们", + "answerText": "发现情绪中的异常值并检查它们", "isCorrect": "false" } ] }, { - "questionText": "在清洁数据时，您可以查找哪些内容？", + "questionText": "什么是一些事情，你可能会寻找在清洗数据？", "answerOptions": [{ - "answerText": "其他语言中的字符", + "answerText": "其他语言的字符", "isCorrect": "false" }, { @@ -1979,13 +1979,13 @@ ] }, { - "questionText": "在对其执行操作之前，了解您的数据及其弱点非常重要。", + "questionText": "t是重要的在其上进行操作前，要了解你的数据及其弱点。", "answerOptions": [{ - "answerText": "真", + "answerText": "真的", "isCorrect": "true" }, { - "answerText": "错误", + "answerText": "错误的", "isCorrect": "false" } ] @@ -1994,15 +1994,15 @@ }, { "id": 39, - "title": "NLP 5： 讲座前测验", + "title": "NLP 5：课前测验", "quiz": [{ - "questionText": "为什么在分析数据之前清理数据很重要？", + "questionText": "为什么在分析之前清理数据很重要？", "answerOptions": [{ "answerText": "某些列可能缺少或不正确的数据", "isCorrect": "false" }, { - "answerText": "混乱的数据可能导致有关数据集的错误结论", + "answerText": "杂乱的数据可能导致关于数据集的错误结论", "isCorrect": "false" }, { @@ -2012,29 +2012,29 @@ ] }, { - "questionText": "清洁数据策略的一个例子是什么？", + "questionText": "清理数据的策略的一个示例是什么？", "answerOptions": [{ - "answerText": "删除不适合回答特定问题的列/行", + "answerText": "删除对回答特定问题无用的列/行", "isCorrect": "true" }, { - "answerText": "删除不符合您假设的验证值", + "answerText": "摆脱不符合您的假设的验证值", "isCorrect": "false" }, { - "answerText": "将离群值移到单独的表中，并运行该表的计算，以查看它们是否匹配", + "answerText": "将异常值移动到一个单独的表并运行该表的计算以查看它们是否匹配", "isCorrect": "false" } ] }, { - "questionText": "使用标签列对数据进行分类是很有用的。", + "questionText": "它可以是有用的分类使用Tag列数据。", "answerOptions": [{ - "answerText": "真", + "answerText": "真的", "isCorrect": "true" }, { - "answerText": "错误", + "answerText": "错误的", "isCorrect": "false" } ] @@ -2047,27 +2047,27 @@ "quiz": [{ "questionText": "数据集的目标是什么？", "answerOptions": [{ - "answerText": "看看全世界酒店有多少负面和正面的评论", + "answerText": "查看全球酒店有多少负面和正面评价", "isCorrect": "false" }, { - "answerText": "添加情绪和专栏，这将有助于您选择最好的酒店", + "answerText": "添加有助于您选择最佳酒店的情绪和专栏", "isCorrect": "true" }, { - "answerText": "分析人们为什么留下特定的评论", + "answerText": "分析人们留下特定评论的原因", "isCorrect": "false" } ] }, { - "questionText": "什么是停止词？", + "questionText": "什么是停用词？", "answerOptions": [{ - "answerText": "不改变句子情绪的普通英语单词", + "answerText": "不会改变句子情绪的常用英语单词", "isCorrect": "false" }, { - "answerText": "单词，你可以删除，以加快情绪分析", + "answerText": "您可以删除以加快情绪分析的单词", "isCorrect": "false" }, { @@ -2077,13 +2077,13 @@ ] }, { - "questionText": "要测试情绪分析，请确保它与审阅者的分数相匹配，以便进行相同的审核。", + "questionText": "要测试情绪分析，请确保它与评论者对同一评论的分数相匹配。", "answerOptions": [{ - "answerText": "真", + "answerText": "真的", "isCorrect": "true" }, { - "answerText": " 错误", + "answerText": "错误的", "isCorrect": "false" } ] @@ -2092,9 +2092,9 @@ }, { "id": 41, - "title": "时间系列简介：演讲前测验", + "title": "时间序列简介：课前测验", "quiz": [{ - "questionText": "时间系列预测在", + "questionText": "时间序列预测在以下方面很有用", "answerOptions": [{ "answerText": "确定未来成本", "isCorrect": "false" @@ -2110,23 +2110,23 @@ ] }, { - "questionText": "时间序列是按下列顺序拍摄的：", + "questionText": "时间序列是一个序列采取：", "answerOptions": [{ - "answerText": "空间中连续的相同间隔点", + "answerText": "空间中连续的等距点", "isCorrect": "false" }, { - "answerText": "连续的相同间隔点的时间", + "answerText": "在时间连续的等间隔的点", "isCorrect": "true" }, { - "answerText": "空间和时间的连续等间隔点", + "answerText": "在空间和时间的连续相等间隔的点", "isCorrect": "false" } ] }, { - "questionText": "时间系列可用于：", + "questionText": "时间序列可用于：", "answerOptions": [{ "answerText": "地震预报", "isCorrect": "true" @@ -2145,31 +2145,31 @@ }, { "id": 42, - "title": "时间系列简介：讲座后测验", + "title": "时间序列简介：课后测验", "quiz": [{ - "questionText": "时间系列趋势是", + "questionText": "时间序列趋势是", "answerOptions": [{ - "answerText": "可测量的增减随时间推移而增加和减少", + "answerText": "随着时间的推移可测量的增加和减少", "isCorrect": "true" }, { - "answerText": "量化会随着时间推移而减少", + "answerText": "量化随时间减少", "isCorrect": "false" }, { - "answerText": "随着时间的推移，增减之间的差距", + "answerText": "随着时间的推移增加和减少之间的差距", "isCorrect": "false" } ] }, { - "questionText": "离群值是", + "questionText": "异常值是", "answerOptions": [{ "answerText": "接近标准数据方差的点", "isCorrect": "false" }, { - "answerText": "远离标准数据差异的点", + "answerText": "远离标准数据方差的点", "isCorrect": "true" }, { @@ -2179,7 +2179,7 @@ ] }, { - "questionText": "时间系列预测最有用", + "questionText": "时间序列预测是最有用的", "answerOptions": [{ "answerText": "计量经济学", "isCorrect": "true" @@ -2198,35 +2198,35 @@ }, { "id": 43, - "title": "时间系列阿里玛：演讲前测验", + "title": "时间序列 ARIMA：课前测验", "quiz": [{ - "questionText": "阿里玛代表", + "questionText": "ARIMA 代表", "answerOptions": [{ - "answerText": "自动回归整体移动平均线", + "answerText": "自回归积分移动平均线", "isCorrect": "false" }, { - "answerText": "自动回归综合移动操作", + "answerText": "自回归整合移动动作", "isCorrect": "false" }, { - "answerText": "自动递减综合移动平均线", + "answerText": "自回归综合移动平均线", "isCorrect": "true" } ] }, { - "questionText": "固定性是指", + "questionText": "平稳性是指", "answerOptions": [{ - "answerText": "属性在时间转移时不会更改的数据", + "answerText": "属性不随时间变化的数据", "isCorrect": "false" }, { - "answerText": "分布在时间转移时不会更改的数据", + "answerText": "数据，其分布在时间上偏移时，不改", "isCorrect": "true" }, { - "answerText": "数据其分布在时间转移时发生更改", + "answerText": "数据，其分布发生变化时，在时间上偏移", "isCorrect": "false" } ] @@ -2251,25 +2251,25 @@ }, { "id": 44, - "title": "时间系列阿里玛：课后测验", + "title": "时间序列 ARIMA：课后测验", "quiz": [{ - "questionText": "ARIMA 用于使模型适合特殊时间系列数据的形式", + "questionText": "ARIMA 用于使模型适合时间序列数据的特殊形式", "answerOptions": [{ "answerText": "尽可能平坦", "isCorrect": "false" }, { - "answerText": "尽可能紧密", + "answerText": "尽可能接近", "isCorrect": "true" }, { - "answerText": "通过散射图", + "answerText": "通过散点图", "isCorrect": "false" } ] }, { - "questionText": "使用萨里玛克斯", + "questionText": "使用SARIMAX到", "answerOptions": [{ "answerText": "管理季节性 ARIMA 模型", "isCorrect": "true" @@ -2285,17 +2285,17 @@ ] }, { - "questionText": "'向前走'验证涉及", + "questionText": "Walk-Forward 验证涉及", "answerOptions": [{ - "answerText": "重新评估模型，使其在验证时逐步进行", + "answerText": "在验证模型时逐步重新评估模型", "isCorrect": "false" }, { - "answerText": "重新培训模型，使其在验证时逐步恢复", + "answerText": "在验证模型时逐步重新训练模型", "isCorrect": "true" }, { - "answerText": "验证时逐步重新配置模型", + "answerText": "在验证模型时逐步重新配置模型", "isCorrect": "false" } ] @@ -2304,11 +2304,11 @@ }, { "id": 45, - "title": "强化 1：课前测验", + "title": "强化1：预讲座问答", "quiz": [{ "questionText": "什么是强化学习？", "answerOptions": [{ - "answerText": "一遍又一遍地教某人一些东西， 直到他们明白", + "answerText": "一遍又一遍地教某人某事直到他们理解", "isCorrect": "false" }, { @@ -2324,27 +2324,27 @@ { "questionText": "什么是政策？", "answerOptions": [{ - "answerText": "在任何给定状态下返回操作的功能", + "answerText": "在任何给定状态下返回动作的函数", "isCorrect": "true" }, { - "answerText": "一份文件，告诉你是否可以返回一个项目", + "answerText": "告诉您是否可以退货的文件", "isCorrect": "false" }, { - "answerText": "用于随机目的的功能", + "answerText": "一个用于随机目的的功能", "isCorrect": "false" } ] }, { - "questionText": "奖励函数会返回环境中每个状态的分数。", + "questionText": "奖励函数为环境的每个状态返回一个分数。", "answerOptions": [{ - "answerText": "真", + "answerText": "真的", "isCorrect": "true" }, { - "answerText": "错误", + "answerText": "错误的", "isCorrect": "false" } ] @@ -2353,27 +2353,27 @@ }, { "id": 46, - "title": "强化 1：课后测验", + "title": "强化1：课后测验", "quiz": [{ "questionText": "什么是 Q 学习？", "answerOptions": [{ - "answerText": "记录每个状态的'善良'的机制", + "answerText": "一种记录每个状态“善”的机制", "isCorrect": "false" }, { - "answerText": "由 Q 表定义策略的算法", + "answerText": "一种由 Q-Table 定义策略的算法", "isCorrect": "false" }, { - "answerText": "以上两个", + "answerText": "以上两者", "isCorrect": "true" } ] }, { - "questionText": "Q-Table 与随机步行策略相对应的值是什么？", + "questionText": "对于什么样的价值观做了Q-表对应于随机游走政策？", "answerOptions": [{ - "answerText": "所有等值", + "answerText": "所有相等的值", "isCorrect": "true" }, { @@ -2387,13 +2387,13 @@ ] }, { - "questionText": "在我们的学习过程中，使用探索比开发更好。", + "questionText": "在我们课程的学习过程中，最好使用探索而不是利用。", "answerOptions": [{ - "answerText": "真", + "answerText": "真的", "isCorrect": "false" }, { - "answerText": "错误", + "answerText": "虚假", "isCorrect": "true" } ] @@ -2402,27 +2402,27 @@ }, { "id": 47, - "title": "强化 2：课前测验", + "title": "强化2：课前测验", "quiz": [{ "questionText": "国际象棋和围棋是具有连续状态的游戏。", "answerOptions": [{ - "answerText": "真", + "answerText": "真的", "isCorrect": "false" }, { - "answerText": "错误", + "answerText": "虚假", "isCorrect": "true" } ] }, { - "questionText": "什么是卡特波尔问题？", + "questionText": "什么是 CartPole 问题？", "answerOptions": [{ - "answerText": "消除离群值的过程", + "answerText": "消除异常值的过程", "isCorrect": "false" }, { - "answerText": "优化购物车的方法", + "answerText": "优化您的购物车的方法", "isCorrect": "false" }, { @@ -2432,7 +2432,7 @@ ] }, { - "questionText": "我们可以使用什么工具在游戏中播放不同的潜在状态场景？", + "questionText": "我们可以使用什么工具来播放游戏中潜在状态的不同场景？", "answerOptions": [{ "answerText": "猜测和检查", "isCorrect": "false" @@ -2442,7 +2442,7 @@ "isCorrect": "true" }, { - "answerText": "状态过渡测试", + "answerText": "状态转换测试", "isCorrect": "false" } ] @@ -2451,9 +2451,9 @@ }, { "id": 48, - "title": "强化 2：课后测验", + "title": "强化2：课后测验", "quiz": [{ - "questionText": "我们在哪里定义环境中的所有可能操作？", + "questionText": "我们在哪里定义环境中所有可能的操作？", "answerOptions": [{ "answerText": "方法", "isCorrect": "false" @@ -2463,39 +2463,39 @@ "isCorrect": "true" }, { - "answerText": "行动列表", + "answerText": "动作列表", "isCorrect": "false" } ] }, { - "questionText": "我们用什么对作为字典的关键值？", + "questionText": "我们使用哪一对作为字典键值？", "answerOptions": [{ - "answerText": "（状态、动作）为键，Q表输入为值", + "answerText": "(state, action) 为键，Q-Table 条目为值", "isCorrect": "true" }, { - "answerText": "状态为键，行动为价值", + "answerText": "状态为键，动作为值", "isCorrect": "false" }, { - "answerText": "qvalue 的价值以功能为键，以行动为价值", + "answerText": "qvalues的值函数为键，动作为值", "isCorrect": "false" } ] }, { - "questionText": "我们在 Q 学习期间使用的超参数是什么？", + "questionText": "我们在 Q-Learning 中使用了哪些超参数？", "answerOptions": [{ - "answerText": "q 表值、当前奖励、随机操作", + "answerText": "q-table 值，当前奖励，随机动作", "isCorrect": "false" }, { - "answerText": "学习率、折扣系数、勘探/开发系数", + "answerText": "学习率、折扣因子、探索/利用因子", "isCorrect": "true" }, { - "answerText": "累积奖励、学习率、探索因素", + "answerText": "累积奖励、学习率、探索因子", "isCorrect": "false" } ] @@ -2504,9 +2504,9 @@ }, { "id": 49, - "title": "现实世界应用：讲座前测验", + "title": "现实世界的应用：预讲座问答", "quiz": [{ - "questionText": "金融行业 ML 应用的例子是什么？", + "questionText": "金融行业 ML 应用程序的示例是什么？", "answerOptions": [{ "answerText": "使用 NLP 个性化客户旅程", "isCorrect": "false" @@ -2516,19 +2516,19 @@ "isCorrect": "true" }, { - "answerText": "使用时间系列进行能源管理", + "answerText": "使用时间序列进行能源管理", "isCorrect": "false" } ] }, { - "questionText": "医院可以使用什么ML技术来管理重新接纳？", + "questionText": "医院可以使用什么 ML 技术来管理再入院？", "answerOptions": [{ "answerText": "聚类", "isCorrect": "true" }, { - "answerText": "时间系列", + "answerText": "时间序列", "isCorrect": "false" }, { @@ -2538,17 +2538,17 @@ ] }, { - "questionText": "使用时间系列进行能源管理的例子是什么？", + "questionText": "使用时间序列进行能源管理的示例是什么？", "answerOptions": [{ - "answerText": "运动感应动物", + "answerText": "动作感应动物", "isCorrect": "false" }, { - "answerText": "智能停车表", + "answerText": "智能停车计时器", "isCorrect": "true" }, { - "answerText": "跟踪森林火灾", + "answerText": "追踪森林火灾", "isCorrect": "false" } ] @@ -2557,9 +2557,9 @@ }, { "id": 50, - "title": "现实世界应用：讲座后测验", + "title": "现实世界的应用：后讲座问答", "quiz": [{ - "questionText": "哪些 ML 技术可用于检测信用卡欺诈？", + "questionText": "哪种 ML 技术可用于检测信用卡欺诈？", "answerOptions": [{ "answerText": "回归", "isCorrect": "false" @@ -2575,13 +2575,13 @@ ] }, { - "questionText": "在森林管理中体现了哪种ML技术？", + "questionText": "哪种机器学习技术在森林管理中得到了体现？", "answerOptions": [{ "answerText": "强化学习", "isCorrect": "true" }, { - "answerText": "时间系列", + "answerText": "时间序列", "isCorrect": "false" }, { @@ -2591,7 +2591,7 @@ ] }, { - "questionText": "在医疗保健行业应用 ML 的例子是什么？", + "questionText": "医疗保健行业 ML 应用程序的示例是什么？", "answerOptions": [{ "answerText": "使用回归预测学生行为", "isCorrect": "false" @@ -2601,7 +2601,7 @@ "isCorrect": "true" }, { - "answerText": "使用分类器对动物的运动感应", + "answerText": "使用分类器的动物运动感知", "isCorrect": "false" } ]