diff --git a/quiz-app/src/App.vue b/quiz-app/src/App.vue index ef95dbed..e155f8ab 100644 --- a/quiz-app/src/App.vue +++ b/quiz-app/src/App.vue @@ -8,6 +8,7 @@ +
diff --git a/quiz-app/src/assets/translations/zh-cn.json b/quiz-app/src/assets/translations/zh-cn.json new file mode 100644 index 00000000..8ad50bc5 --- /dev/null +++ b/quiz-app/src/assets/translations/zh-cn.json @@ -0,0 +1,2612 @@ +[{ + "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", + "quizzes": [{ + "id": 1, + "title": "机器学习简介：讲座前测验", + "quiz": [{ + "questionText": "机器学习的应用都在我们身边", + "answerOptions": [{ + "answerText": "是", + "isCorrect": "true" + }, + { + "answerText": "否", + "isCorrect": "false" + } + ] + }, + { + "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", + "isCorrect": "false" + }, + { + "answerText": "神经网络的使用", + "isCorrect": "true" + }, + { + "answerText": "深度学习用于机器人", + "isCorrect": "false" + } + ] + }, + { + "questionText": "为什么企业要使用 ML 策略？", + "answerOptions": [{ + "answerText": "自动化解决多维问题", + "isCorrect": "false" + }, + { + "answerText": "根据客户类型定制购物体验", + "isCorrect": "false" + }, + { + "answerText": "以上两者", + "isCorrect": "true" + } + ] + } + ] + }, + { + "id": 2, + "title": "机器学习简介：讲座后测验", + "quiz": [{ + "questionText": "机器学习算法旨在模拟", + "answerOptions": [{ + "answerText": "智能机器", + "isCorrect": "false" + }, + { + "answerText": "人脑", + "isCorrect": "true" + }, + { + "answerText": "猩猩", + "isCorrect": "false" + } + ] + }, + { + "questionText": "什么是经典 ML 技术的例子？", + "answerOptions": [{ + "answerText": "自然语言处理", + "isCorrect": "true" + }, + { + "answerText": "深度学习", + "isCorrect": "false" + }, + { + "answerText": "神经网络", + "isCorrect": "false" + } + ] + }, + { + "questionText": "为什么每个人都要学习 ML 的基本知识？", + "answerOptions": [{ + "answerText": "学习 Ml 是有趣和访问的每个人", + "isCorrect": "false" + }, + { + "answerText": "ML 策略正在许多行业和领域使用", + "isCorrect": "false" + }, + { + "answerText": "以上两者", + "isCorrect": "true" + } + ] + } + ] + }, + { + "id": 3, + "title": "机器学习史：课前测验", + "quiz": [{ + "questionText": "大约是'人工智能'一词的创造时间？", + "answerOptions": [{ + "answerText": "20 世纪 80 年代", + "isCorrect": "false" + }, + { + "answerText": "20 世纪 50 年代", + "isCorrect": "true" + }, + { + "answerText": "20 世纪 30 年代", + "isCorrect": "false" + } + ] + }, + { + "questionText": "谁是机器学习的早期先驱之一？", + "answerOptions": [{ + "answerText": "艾伦·图灵", + "isCorrect": "true" + }, + { + "answerText": "比尔·盖茨", + "isCorrect": "false" + }, + { + "answerText": "摇动机器人", + "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？", + "answerOptions": [{ + "answerText": "计算能力有限", + "isCorrect": "true" + }, + { + "answerText": "没有足够的熟练工程师", + "isCorrect": "false" + }, + { + "answerText": "国家间的冲突", + "isCorrect": "false" + } + ] + } + ] + }, + { + "id": 4, + "title": "机器学习史：讲座后测验", + "quiz": [{ + "questionText": "什么是'粗糙'AI 系统的例子？", + "answerOptions": [{ + "answerText": "伊丽莎", + "isCorrect": "true" + }, + { + "answerText": "哈克姆", + "isCorrect": "false" + }, + { + "answerText": "系统", + "isCorrect": "false" + } + ] + }, + { + "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", + "isCorrect": "false" + }, + { + "answerText": "机器人狗", + "isCorrect": "false" + } + ] + }, + { + "questionText": "人工智能领域的创建和扩展是基础性的？", + "answerOptions": [{ + "answerText": "图灵测试", + "isCorrect": "false" + }, + { + "answerText": "达特茅斯夏季研究项目", + "isCorrect": "true" + }, + { + "answerText": "AI 冬季", + "isCorrect": "false" + } + ] + } + ] + }, + { + "id": 5, + "title": "公平与机器学习：演讲前测验", + "quiz": [{ + "questionText": "机器学习中的不公平可能发生", + "answerOptions": [{ + "answerText": "有意", + "isCorrect": "false" + }, + { + "answerText": "不经意间", + "isCorrect": "false" + }, + { + "answerText": "以上两者", + "isCorrect": "true" + } + ] + }, + { + "questionText": "ML 中的'不公平'一词表示：", + "answerOptions": [{ + "answerText": "对一群人的伤害", + "isCorrect": "true" + }, + { + "answerText": "伤害一个人", + "isCorrect": "false" + }, + { + "answerText": "对大多数人的伤害", + "isCorrect": "false" + } + ] + }, + { + "questionText": "五种主要伤害类型包括", + "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", + "isCorrect": "true" + }, + { + "answerText": "定位、服务质量、成见、诋毁以及过度或不足代表 ", + "isCorrect": "false" + }, + { + "answerText": "分配、服务质量、立体声、诋毁以及过度或不足代表 ", + "isCorrect": "false" + } + ] + } + ] + }, + { + "id": 6, + "title": "公平与机器学习：讲座后测验", + "quiz": [{ + "questionText": "模型中的不公平可能由", + "answerOptions": [{ + "answerText": "过度依赖历史数据", + "isCorrect": "true" + }, + { + "answerText": "依赖历史数据", + "isCorrect": "false" + }, + { + "answerText": "与历史数据过于紧密一致", + "isCorrect": "false" + } + ] + }, + { + "questionText": "为了减轻不公平，你可以", + "answerOptions": [{ + "answerText": "识别伤害和受影响的群体", + "isCorrect": "false" + }, + { + "answerText": "定义公平指标", + "isCorrect": "false" + }, + { + "answerText": "以上两者", + "isCorrect": "true" + } + ] + }, + { + "questionText": "公平学习是一个包， 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", + "isCorrect": "false" + }, + { + "answerText": "帮助您决定什么是公平的，什么是不公平的", + "isCorrect": "false" + } + ] + } + ] + }, + { + "id": 7, + "title": "工具和技术：讲座前测验", + "quiz": [{ + "questionText": "构建模型时，应：", + "answerOptions": [{ + "answerText": "准备数据，然后训练您的模型", + "isCorrect": "true" + }, + { + "answerText": "选择培训方法，然后准备数据", + "isCorrect": "false" + }, + { + "answerText": "调整参数，然后训练您的模型", + "isCorrect": "false" + } + ] + }, + { + "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", + "isCorrect": "false" + }, + { + "answerText": "以上两者", + "isCorrect": "true" + } + ] + }, + { + "questionText": "功能变量是：", + "answerOptions": [{ + "answerText": "数据质量", + "isCorrect": "false" + }, + { + "answerText": "数据的可测量属性", + "isCorrect": "true" + }, + { + "answerText": "一排数据", + "isCorrect": "false" + } + ] + } + ] + }, + { + "id": 8, + "title": "工具和技术：讲座后测验", + "quiz": [{ + "questionText": "您应该可视化您的数据，因为", + "answerOptions": [{ + "answerText": "您可以发现离群值", + "isCorrect": "false" + }, + { + "answerText": "您可以发现偏见的潜在原因", + "isCorrect": "false" + }, + { + "answerText": "两者都是", + "isCorrect": "true" + } + ] + }, + { + "questionText": "将数据拆分为：", + "answerOptions": [{ + "answerText": "训练和图灵集", + "isCorrect": "false" + }, + { + "answerText": "培训和测试集", + "isCorrect": "true" + }, + { + "answerText": "验证和评估集", + "isCorrect": "false" + } + ] + }, + { + "questionText": "在各种 ML 库中启动培训过程的常见命令是：", + "answerOptions": [{ + "answerText": "model.travel", + "isCorrect": "false" + }, + { + "answerText": "模型.火车", + "isCorrect": "false" + }, + { + "answerText": "模型.适合", + "isCorrect": "true" + } + ] + } + ] + }, + { + "id": 9, + "title": "回归简介：演讲前测验", + "quiz": [{ + "questionText": "这些变量中哪一个是数字变量？", + "answerOptions": [{ + "answerText": "高度", + "isCorrect": "true" + }, + { + "answerText": "性别", + "isCorrect": "false" + }, + { + "answerText": "毛色", + "isCorrect": "false" + } + ] + }, + { + "questionText": "这些变量中哪一个是绝对变量？", + "answerOptions": [{ + "answerText": "心率", + "isCorrect": "false" + }, + { + "answerText": "血型", + "isCorrect": "true" + }, + { + "answerText": "重量", + "isCorrect": "false" + } + ] + }, + { + "questionText": "这些问题中哪一个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" + }, + { + "answerText": "预测一个人的血型", + "isCorrect": "false" + }, + { + "answerText": "预测电子邮件是否是垃圾邮件", + "isCorrect": "false" + } + ] + } + ] + }, + { + "id": 10, + "title": "回归简介：演讲后测验", + "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？", + "answerOptions": [{ + "answerText": "过度拟合", + "isCorrect": "true" + }, + { + "answerText": "不合身", + "isCorrect": "false" + }, + { + "answerText": "双合身", + "isCorrect": "false" + } + ] + }, + { + "questionText": "从一组功能中识别重要特征的过程称为：", + "answerOptions": [{ + "answerText": "功能提取", + "isCorrect": "false" + }, + { + "answerText": "功能尺寸降低", + "isCorrect": "false" + }, + { + "answerText": "功能选择", + "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：", + "answerOptions": [{ + "answerText": "交叉验证", + "isCorrect": "false" + }, + { + "answerText": "坚持验证", + "isCorrect": "true" + }, + { + "answerText": "将一个排除在验证外", + "isCorrect": "false" + } + ] + } + ] + }, + { + "id": 11, + "title": "准备和可视化回归数据：讲座前测验", + "quiz": [{ + "questionText": "这些 Python 模块中哪一个用于绘制数据的可视化图？", + "answerOptions": [{ + "answerText": "努皮", + "isCorrect": "false" + }, + { + "answerText": "科学学习", + "isCorrect": "false" + }, + { + "answerText": "马特普洛特利布", + "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：", + "answerOptions": [{ + "answerText": "数据可视化", + "isCorrect": "true" + }, + { + "answerText": "数据预处理", + "isCorrect": "false" + }, + { + "answerText": "列车测试拆分", + "isCorrect": "false" + } + ] + }, + { + "questionText": "哪些是机器学习项目中数据可视化步骤的一部分？", + "answerOptions": [{ + "answerText": "纳入特定的机器学习算法", + "isCorrect": "false" + }, + { + "answerText": "使用不同的绘图方法创建数据的图片表示", + "isCorrect": "true" + }, + { + "answerText": "使数据集值正常化", + "isCorrect": "false" + } + ] + } + ] + }, + { + "id": 12, + "title": "准备和可视化回归数据：讲座后测验", + "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。", + "answerOptions": [{ + "answerText": "数据集. isnull （. 和 （）", + "isCorrect": "true" + }, + { + "answerText": "查找错误（数据集）", + "isCorrect": "false" + }, + { + "answerText": "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？", + "answerOptions": [{ + "answerText": "散布图", + "isCorrect": "false" + }, + { + "answerText": "线图", + "isCorrect": "false" + }, + { + "answerText": "酒吧情节", + "isCorrect": "true" + } + ] + }, + { + "questionText": "数据可视化不能告诉你什么？", + "answerOptions": [{ + "answerText": "数据点之间的关系", + "isCorrect": "false" + }, + { + "answerText": "收集数据集的来源", + "isCorrect": "true" + }, + { + "answerText": "在数据集中查找离群值的存在", + "isCorrect": "false" + } + ] + } + ] + }, + { + "id": 13, + "title": "线性和多面回归：演讲前测验", + "quiz": [{ + "questionText": "马特普洛特利布是一个", + "answerOptions": [{ + "answerText": "绘图库", + "isCorrect": "false" + }, + { + "answerText": "数据可视化库", + "isCorrect": "true" + }, + { + "answerText": "借阅库", + "isCorrect": "false" + } + ] + }, + { + "questionText": "线性回归使用以下图来绘制变量之间的关系", + "answerOptions": [{ + "answerText": "直线", + "isCorrect": "true" + }, + { + "answerText": "一个圆圈", + "isCorrect": "false" + }, + { + "answerText": "曲线", + "isCorrect": "false" + } + ] + }, + { + "questionText": "一个好的线性回归模型具有 ___ 相关系数", + "answerOptions": [{ + "answerText": "低", + "isCorrect": "false" + }, + { + "answerText": "轩", + "isCorrect": "true" + }, + { + "answerText": "平", + "isCorrect": "false" + } + ] + } + ] + }, + { + "id": 14, + "title": "线性和多面回归：讲座后测验", + "quiz": [{ + "questionText": "如果您的数据是非线性的，请尝试 '回归'类型", + "answerOptions": [{ + "answerText": "线性", + "isCorrect": "false" + }, + { + "answerText": "球形", + "isCorrect": "false" + }, + { + "answerText": "多项式", + "isCorrect": "true" + } + ] + }, + { + "questionText": "这些都是类型的回归方法", + "answerOptions": [{ + "answerText": "假步、岭、拉索和弹性网", + "isCorrect": "false" + }, + { + "answerText": "步进， 岭， 拉索和弹性网", + "isCorrect": "true" + }, + { + "answerText": "步进， 岭， 拉里亚特和弹性网", + "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：", + "answerOptions": [{ + "answerText": "平方，然后减去", + "isCorrect": "false" + }, + { + "answerText": "乘以", + "isCorrect": "false" + }, + { + "answerText": "平方，然后加起来", + "isCorrect": "true" + } + ] + } + ] + }, + { + "id": 15, + "title": "后勤回归：课前测验", + "quiz": [{ + "questionText": "使用物流回归来预测", + "answerOptions": [{ + "answerText": "苹果是否成熟", + "isCorrect": "true" + }, + { + "answerText": "一个月内能卖出多少张票", + "isCorrect": "false" + }, + { + "answerText": "天空明天下午 6 点会转动什么颜色", + "isCorrect": "false" + } + ] + }, + { + "questionText": "后勤回归类型包括", + "answerOptions": [{ + "answerText": "多名和枢机主教", + "isCorrect": "false" + }, + { + "answerText": "多名和序", + "isCorrect": "true" + }, + { + "answerText": "校长和序人", + "isCorrect": "false" + } + ] + }, + { + "questionText": "您的数据相关性较弱。最佳类型的回归使用是：", + "answerOptions": [{ + "answerText": "物流", + "isCorrect": "true" + }, + { + "answerText": "线性", + "isCorrect": "false" + }, + { + "answerText": "红衣主教", + "isCorrect": "false" + } + ] + } + ] + }, + { + "id": 16, + "title": "后勤回归：课后测验", + "quiz": [{ + "questionText": "海出生是一种类型", + "answerOptions": [{ + "answerText": "数据可视化库", + "isCorrect": "true" + }, + { + "answerText": "制图库", + "isCorrect": "false" + }, + { + "answerText": "数学库", + "isCorrect": "false" + } + ] + }, + { + "questionText": "混淆矩阵也称为：", + "answerOptions": [{ + "answerText": "错误矩阵", + "isCorrect": "true" + }, + { + "answerText": "真相矩阵", + "isCorrect": "false" + }, + { + "answerText": "精度矩阵", + "isCorrect": "false" + } + ] + }, + { + "questionText": "一个好的模型将有：", + "answerOptions": [{ + "answerText": "大量的误报和真底片在其混乱矩阵", + "isCorrect": "false" + }, + { + "answerText": "大量的真正的积极和真正的负面在其混乱矩阵", + "isCorrect": "true" + }, + { + "answerText": "大量的真正反误矩阵", + "isCorrect": "false" + } + ] + } + ] + }, + { + "id": 17, + "title": "构建 Web 应用程序：讲座前测验", + "quiz": [{ + "questionText": "ONNX 代表什么？", + "answerOptions": [{ + "answerText": "通过神经网络交换", + "isCorrect": "false" + }, + { + "answerText": "开放神经网络交换", + "isCorrect": "true" + }, + { + "answerText": "输出神经网络交换", + "isCorrect": "false" + } + ] + }, + { + "questionText": "弗拉斯克是如何由它的创造者定义的？", + "answerOptions": [{ + "answerText": "迷你框架", + "isCorrect": "false" + }, + { + "answerText": "大框架", + "isCorrect": "false" + }, + { + "answerText": "微型框架", + "isCorrect": "true" + } + ] + }, + { + "questionText": "Python 的泡菜模块是做什么的", + "answerOptions": [{ + "answerText": "序列化 Python 对象", + "isCorrect": "false" + }, + { + "answerText": "去序列化 Python 对象", + "isCorrect": "false" + }, + { + "answerText": "序列化和去序列化 Python 对象", + "isCorrect": "true" + } + ] + } + ] + }, + { + "id": 18, + "title": "构建 Web 应用程序：讲座后测验", + "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？", + "answerOptions": [{ + "answerText": "瓶", + "isCorrect": "true" + }, + { + "answerText": "滕索弗.js", + "isCorrect": "false" + }, + { + "answerText": ".js", + "isCorrect": "false" + } + ] + }, + { + "questionText": "萨斯代表什么？", + "answerOptions": [{ + "answerText": "系统作为服务", + "isCorrect": "false" + }, + { + "answerText": "软件作为服务", + "isCorrect": "true" + }, + { + "answerText": "安全作为一种服务", + "isCorrect": "false" + } + ] + }, + { + "questionText": "科学学习的标签编码器库是做什么的？", + "answerOptions": [{ + "answerText": "按字母顺序编码数据", + "isCorrect": "true" + }, + { + "answerText": "以数字编码数据", + "isCorrect": "false" + }, + { + "answerText": "串行编码数据", + "isCorrect": "false" + } + ] + } + ] + }, + { + "id": 19, + "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", + "answerOptions": [{ + "answerText": "时间系列", + "isCorrect": "false" + }, + { + "answerText": "回归技术", + "isCorrect": "true" + }, + { + "answerText": "NLP", + "isCorrect": "false" + } + ] + }, + { + "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？", + "isCorrect": "true" + }, + { + "answerText": "猪会飞吗？", + "isCorrect": "false" + }, + { + "answerText": "生命的意义何在？", + "isCorrect": "false" + } + ] + }, + { + "questionText": "使用分类技术的第一步是什么？", + "answerOptions": [{ + "answerText": "创建数据集的类", + "isCorrect": "false" + }, + { + "answerText": "清洁和平衡您的数据", + "isCorrect": "true" + }, + { + "answerText": "将数据点分配给组或结果", + "isCorrect": "false" + } + ] + } + ] + }, + { + "id": 20, + "title": "分类1：课后测验", + "quiz": [{ + "questionText": "什么是多类问题？", + "answerOptions": [{ + "answerText": "将数据点分类为多个类的任务", + "isCorrect": "false" + }, + { + "answerText": "将数据点分类为几个类之一的任务", + "isCorrect": "true" + }, + { + "answerText": "以多种方式清理数据点的任务", + "isCorrect": "false" + } + ] + }, + { + "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。", + "answerOptions": [{ + "answerText": "真", + "isCorrect": "true" + }, + { + "answerText": "错误", + "isCorrect": "false" + } + ] + }, + { + "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", + "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", + "isCorrect": "true" + }, + { + "answerText": "平衡数据为您提供了更多的数据点", + "isCorrect": "false" + } + ] + } + ] + }, + { + "id": 21, + "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", + "answerOptions": [{ + "answerText": "真", + "isCorrect": "true" + }, + { + "answerText": "错误", + "isCorrect": "false" + } + ] + }, + { + "questionText": "如何选择正确的分类器？", + "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", + "isCorrect": "false" + }, + { + "answerText": "以上两者", + "isCorrect": "true" + } + ] + }, + { + "questionText": "分类是一种类型", + "answerOptions": [{ + "answerText": "NLP", + "isCorrect": "false" + }, + { + "answerText": "监督学习", + "isCorrect": "true" + }, + { + "answerText": "程序设计语言", + "isCorrect": "false" + } + ] + } + ] + }, + { + "id": 22, + "title": "分类2：课后测验", + "quiz": [{ + "questionText": "什么是'解算器'？", + "answerOptions": [{ + "answerText": "仔细检查您工作的人", + "isCorrect": "false" + }, + { + "answerText": "优化问题中使用的算法", + "isCorrect": "true" + }, + { + "answerText": "机器学习技术", + "isCorrect": "false" + } + ] + }, + { + "questionText": "我们在这节课中使用了哪个分类器？", + "answerOptions": [{ + "answerText": "物流回归", + "isCorrect": "true" + }, + { + "answerText": "决策树", + "isCorrect": "false" + }, + { + "answerText": "一对全多类", + "isCorrect": "false" + } + ] + }, + { + "questionText": "您如何知道分类算法是否按预期工作？", + "answerOptions": [{ + "answerText": "通过检查其预测的准确性", + "isCorrect": "true" + }, + { + "answerText": "通过检查它与其他算法", + "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", + "isCorrect": "false" + } + ] + } + ] + }, + { + "id": 23, + "title": "分类3：课前测验", + "quiz": [{ + "questionText": "要尝试的一个好的初始分类器是：", + "answerOptions": [{ + "answerText": "线性 SVC", + "isCorrect": "true" + }, + { + "answerText": "K-手段", + "isCorrect": "false" + }, + { + "answerText": "逻辑 SVC", + "isCorrect": "false" + } + ] + }, + { + "questionText": "正规化控制：", + "answerOptions": [{ + "answerText": "参数的影响", + "isCorrect": "true" + }, + { + "answerText": "训练速度的影响", + "isCorrect": "false" + }, + { + "answerText": "离群值的影响", + "isCorrect": "false" + } + ] + }, + { + "questionText": "K-邻居分类器可用于：", + "answerOptions": [{ + "answerText": "监督学习", + "isCorrect": "false" + }, + { + "answerText": "无人监督的学习", + "isCorrect": "false" + }, + { + "answerText": "两者都是", + "isCorrect": "true" + } + ] + } + ] + }, + { + "id": 24, + "title": "分类3：课后测验", + "quiz": [{ + "questionText": "支持矢量分类器可用于", + "answerOptions": [{ + "answerText": "分类", + "isCorrect": "false" + }, + { + "answerText": "回归", + "isCorrect": "false" + }, + { + "answerText": "两者都是", + "isCorrect": "true" + } + ] + }, + { + "questionText": "随机森林是一种___类型的分类器", + "answerOptions": [{ + "answerText": "整体", + "isCorrect": "true" + }, + { + "answerText": "掩饰", + "isCorrect": "false" + }, + { + "answerText": "聚集", + "isCorrect": "false" + } + ] + }, + { + "questionText": "阿达布斯特以：", + "answerOptions": [{ + "answerText": "关注分类错误项目的权重", + "isCorrect": "true" + }, + { + "answerText": "关注离群值", + "isCorrect": "false" + }, + { + "answerText": "关注不正确的数据", + "isCorrect": "false" + } + ] + } + ] + }, + { + "id": 25, + "title": "分类4：课前测验", + "quiz": [{ + "questionText": "建议系统可用于", + "answerOptions": [{ + "answerText": "推荐一家好餐厅", + "isCorrect": "false" + }, + { + "answerText": "推荐时尚尝试", + "isCorrect": "false" + }, + { + "answerText": "两者都是", + "isCorrect": "true" + } + ] + }, + { + "questionText": "将模型嵌入 Web 应用有助于它具有离线能力", + "answerOptions": [{ + "answerText": "真", + "isCorrect": "true" + }, + { + "answerText": "错误", + "isCorrect": "false" + } + ] + }, + { + "questionText": "Onnx 运行时间可用于", + "answerOptions": [{ + "answerText": "在 Web 应用中运行模型", + "isCorrect": "true" + }, + { + "answerText": "培训模式", + "isCorrect": "false" + }, + { + "answerText": "超参数调谐", + "isCorrect": "false" + } + ] + } + ] + }, + { + "id": 26, + "title": "分类4：课后测验", + "quiz": [{ + "questionText": "Netron 应用程序可帮助您：", + "answerOptions": [{ + "answerText": "可视化数据", + "isCorrect": "false" + }, + { + "answerText": "可视化模型的结构", + "isCorrect": "true" + }, + { + "answerText": "测试您的网络应用", + "isCorrect": "false" + } + ] + }, + { + "questionText": "转换您的 Scikit 学习模型，以便与 Onnx 一起使用：", + "answerOptions": [{ + "answerText": "斯克莱恩应用程序", + "isCorrect": "false" + }, + { + "answerText": "斯克莱恩网", + "isCorrect": "false" + }, + { + "answerText": "斯克莱恩 - 奥恩克斯", + "isCorrect": "true" + } + ] + }, + { + "questionText": "在 Web 应用中使用您的模型称为：", + "answerOptions": [{ + "answerText": "推理", + "isCorrect": "true" + }, + { + "answerText": "干涉", + "isCorrect": "false" + }, + { + "answerText": "保险", + "isCorrect": "false" + } + ] + } + ] + }, + { + "id": 27, + "title": "集群简介：讲座前测验", + "quiz": [{ + "questionText": "聚类的真实例子是", + "answerOptions": [{ + "answerText": "设置餐桌", + "isCorrect": "false" + }, + { + "answerText": "整理衣物", + "isCorrect": "true" + }, + { + "answerText": "买", + "isCorrect": "false" + } + ] + }, + { + "questionText": "聚类技术可用于这些行业", + "answerOptions": [{ + "answerText": "银行业", + "isCorrect": "false" + }, + { + "answerText": "电子商务", + "isCorrect": "false" + }, + { + "answerText": "两者都是", + "isCorrect": "true" + } + ] + }, + { + "questionText": "聚类是一种类型：", + "answerOptions": [{ + "answerText": "监督学习", + "isCorrect": "false" + }, + { + "answerText": "无人监督的学习", + "isCorrect": "true" + }, + { + "answerText": "强化学习", + "isCorrect": "false" + } + ] + } + ] + }, + { + "id": 28, + "title": "集群简介：讲座后测验", + "quiz": [{ + "questionText": "欧几里德几何排列沿", + "answerOptions": [{ + "answerText": "飞机", + "isCorrect": "true" + }, + { + "answerText": "曲线", + "isCorrect": "false" + }, + { + "answerText": "领域", + "isCorrect": "false" + } + ] + }, + { + "questionText": "聚类数据的密度与其相关", + "answerOptions": [{ + "answerText": "noise", + "isCorrect": "true" + }, + { + "answerText": "深度", + "isCorrect": "false" + }, + { + "answerText": "有效性", + "isCorrect": "false" + } + ] + }, + { + "questionText": "最著名的聚类算法是", + "answerOptions": [{ + "answerText": "k- 手段", + "isCorrect": "true" + }, + { + "answerText": "k - 中", + "isCorrect": "false" + }, + { + "answerText": "k - mart", + "isCorrect": "false" + } + ] + } + ] + }, + { + "id": 29, + "title": "K-平均聚类：讲座前测验", + "quiz": [{ + "questionText": "K-手段来源于：", + "answerOptions": [{ + "answerText": "电机工程", + "isCorrect": "false" + }, + { + "answerText": "信号处理", + "isCorrect": "true" + }, + { + "answerText": "计算语言学", + "isCorrect": "false" + } + ] + }, + { + "questionText": "一个好的剪影分数意味着：", + "answerOptions": [{ + "answerText": "集群分离良好，定义清晰", + "isCorrect": "true" + }, + { + "answerText": "集群很少", + "isCorrect": "false" + }, + { + "answerText": "有许多集群", + "isCorrect": "false" + } + ] + }, + { + "questionText": "方差为：", + "answerOptions": [{ + "answerText": "与平均值的平方差异的平均值", + "isCorrect": "false" + }, + { + "answerText": "如果聚类变得过高，则会出现问题", + "isCorrect": "false" + }, + { + "answerText": "两者都是", + "isCorrect": "true" + } + ] + } + ] + }, + { + "id": 30, + "title": "K-平均分组：讲座后测验", + "quiz": [{ + "questionText": "沃罗诺伊图显示：", + "answerOptions": [{ + "answerText": "聚类的方差", + "isCorrect": "false" + }, + { + "answerText": "集群的种子及其区域", + "isCorrect": "true" + }, + { + "answerText": "集群的惯性", + "isCorrect": "false" + } + ] + }, + { + "questionText": "惯性是", + "answerOptions": [{ + "answerText": "衡量内部连贯性聚类的指标", + "isCorrect": "true" + }, + { + "answerText": "测量组移动的量", + "isCorrect": "false" + }, + { + "answerText": "集群质量的衡量标准", + "isCorrect": "false" + } + ] + }, + { + "questionText": "使用 K 手段，您必须首先确定'k'值", + "answerOptions": [{ + "answerText": "真", + "isCorrect": "true" + }, + { + "answerText": "错误", + "isCorrect": "false" + } + ] + } + ] + }, + { + "id": 31, + "title": "NLP 简介：演讲前测验", + "quiz": [{ + "questionText": "Nlp 在这些课程中代表什么？", + "answerOptions": [{ + "answerText": "神经语言处理", + "isCorrect": "false" + }, + { + "answerText": "自然语言处理", + "isCorrect": "true" + }, + { + "answerText": "自然语言处理", + "isCorrect": "false" + } + ] + }, + { + "questionText": "伊丽莎是一个早期的机器人， 充当计算机", + "answerOptions": [{ + "answerText": "心理医生", + "isCorrect": "true" + }, + { + "answerText": "大夫", + "isCorrect": "false" + }, + { + "answerText": "护士", + "isCorrect": "false" + } + ] + }, + { + "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" + }, + { + "answerText": "思维", + "isCorrect": "false" + }, + { + "answerText": "以上两者", + "isCorrect": "true" + } + ] + } + ] + }, + { + "id": 32, + "title": "NLP 简介：演讲后测验", + "quiz": [{ + "questionText": "约瑟夫 · 韦森鲍姆发明了机器人", + "answerOptions": [{ + "answerText": "以利沙", + "isCorrect": "false" + }, + { + "answerText": "伊丽莎", + "isCorrect": "true" + }, + { + "answerText": "艾萝依", + "isCorrect": "false" + } + ] + }, + { + "questionText": "对话机器人根据", + "answerOptions": [{ + "answerText": "随机选择预先定义的选择", + "isCorrect": "false" + }, + { + "answerText": "分析输入和使用机器智能", + "isCorrect": "false" + }, + { + "answerText": "两者都是", + "isCorrect": "true" + } + ] + }, + { + "questionText": "您如何使机器人更有效？", + "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", + "isCorrect": "true" + }, + { + "answerText": "机器人是哑巴， 它不能学习:(", + "isCorrect": "false" + } + ] + } + ] + }, + { + "id": 33, + "title": "NLP 任务：讲座前测验", + "quiz": [{ + "questionText": "令牌化", + "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）", + "isCorrect": "true" + }, + { + "answerText": "将文本拆分为短语", + "isCorrect": "false" + } + ] + }, + { + "questionText": "嵌入", + "answerOptions": [{ + "answerText": "以数字形式转换文本数据，以便单词可以聚类", + "isCorrect": "true" + }, + { + "answerText": "将单词嵌入短语", + "isCorrect": "false" + }, + { + "answerText": "将句子嵌入段落", + "isCorrect": "false" + } + ] + }, + { + "questionText": "语音标记部分", + "answerOptions": [{ + "answerText": "将句子除以其部分的语音", + "isCorrect": "false" + }, + { + "answerText": "采取象征性的单词， 并标记他们说话的一部分", + "isCorrect": "true" + }, + { + "answerText": "图表句子", + "isCorrect": "false" + } + ] + } + ] + }, + { + "id": 34, + "title": "NLP 任务：讲座后测验", + "quiz": [{ + "questionText": "构建单词重复使用的频率字典：", + "answerOptions": [{ + "answerText": "单词和短语词典", + "isCorrect": "false" + }, + { + "answerText": "单词和短语频率", + "isCorrect": "true" + }, + { + "answerText": "单词和短语库", + "isCorrect": "false" + } + ] + }, + { + "questionText": "N 克指", + "answerOptions": [{ + "answerText": "文本可以分为一定长度的单词序列", + "isCorrect": "true" + }, + { + "answerText": "一个单词可以分为一定长度的字符序列", + "isCorrect": "false" + }, + { + "answerText": "文本可以拆分为一定长度的段落", + "isCorrect": "false" + } + ] + }, + { + "questionText": "情绪分析", + "answerOptions": [{ + "answerText": "分析积极或消极的短语", + "isCorrect": "true" + }, + { + "answerText": "分析一个感伤的短语", + "isCorrect": "false" + }, + { + "answerText": "分析悲伤的短语", + "isCorrect": "false" + } + ] + } + ] + }, + { + "id": 35, + "title": "NLP 和翻译：讲座前测验", + "quiz": [{ + "questionText": "天真翻译", + "answerOptions": [{ + "answerText": "仅翻译单词", + "isCorrect": "true" + }, + { + "answerText": "仅翻译单词", + "isCorrect": "false" + }, + { + "answerText": "翻译情绪", + "isCorrect": "false" + } + ] + }, + { + "questionText": "文本的 [语料库] 指", + "answerOptions": [{ + "answerText": "少量文本", + "isCorrect": "false" + }, + { + "answerText": "大量的文本", + "isCorrect": "true" + }, + { + "answerText": "一个标准文本", + "isCorrect": "false" + } + ] + }, + { + "questionText": "如果 ML 模型有足够的人工翻译来构建模型，它可以", + "answerOptions": [{ + "answerText": "缩写翻译", + "isCorrect": "false" + }, + { + "answerText": "标准化翻译", + "isCorrect": "false" + }, + { + "answerText": "提高翻译的准确性", + "isCorrect": "true" + } + ] + } + ] + }, + { + "id": 36, + "title": "提高翻译的准确性", + "quiz": [{ + "questionText": "文本博客的翻译库基础是：", + "answerOptions": [{ + "answerText": "谷歌翻译", + "isCorrect": "true" + }, + { + "answerText": "必应", + "isCorrect": "false" + }, + { + "answerText": "自定义 ML 模型", + "isCorrect": "false" + } + ] + }, + { + "questionText": "要使用您需要的'blob.翻译: '", + "answerOptions": [{ + "answerText": "互联网连接", + "isCorrect": "true" + }, + { + "answerText": "互联网连接", + "isCorrect": "false" + }, + { + "answerText": "爪哇脚本", + "isCorrect": "false" + } + ] + }, + { + "questionText": "为了确定情绪，ML 的方法是：", + "answerOptions": [{ + "answerText": "应用回归技术手动生成的意见和分数，并查找模式", + "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", + "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", + "isCorrect": "false" + } + ] + } + ] + }, + { + "id": 37, + "title": "NLP 4： 讲座前测验", + "quiz": [{ + "questionText": "我们可以从人类撰写或发言的文本中获得哪些信息？", + "answerOptions": [{ + "answerText": "模式和频率", + "isCorrect": "false" + }, + { + "answerText": "情绪和意义", + "isCorrect": "false" + }, + { + "answerText": "以上两者", + "isCorrect": "true" + } + ] + }, + { + "questionText": "什么是情绪分析？", + "answerOptions": [{ + "answerText": "研究家族传家宝是否有感伤价值", + "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", + "isCorrect": "true" + }, + { + "answerText": "判断某人是悲伤还是快乐的能力", + "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？", + "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？", + "isCorrect": "false" + }, + { + "answerText": "这家酒店有代客泊车吗？", + "isCorrect": "false" + } + ] + } + ] + }, + { + "id": 38, + "title": "NLP 4：课后测验", + "quiz": [{ + "questionText": "NLP 的本质是什么？", + "answerOptions": [{ + "answerText": "将人类语言分为快乐或悲伤", + "isCorrect": "false" + }, + { + "answerText": "解释意义或情感， 而不必有一个人这样做", + "isCorrect": "true" + }, + { + "answerText": "发现情绪的离群值并检查它们", + "isCorrect": "false" + } + ] + }, + { + "questionText": "在清洁数据时，您可以查找哪些内容？", + "answerOptions": [{ + "answerText": "其他语言中的字符", + "isCorrect": "false" + }, + { + "answerText": "空白行或列", + "isCorrect": "false" + }, + { + "answerText": "以上两者", + "isCorrect": "true" + } + ] + }, + { + "questionText": "在对其执行操作之前，了解您的数据及其弱点非常重要。", + "answerOptions": [{ + "answerText": "真", + "isCorrect": "true" + }, + { + "answerText": "错误", + "isCorrect": "false" + } + ] + } + ] + }, + { + "id": 39, + "title": "NLP 5： 讲座前测验", + "quiz": [{ + "questionText": "为什么在分析数据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", + "isCorrect": "false" + }, + { + "answerText": "以上两者", + "isCorrect": "true" + } + ] + }, + { + "questionText": "清洁数据策略的一个例子是什么？", + "answerOptions": [{ + "answerText": "删除不适合回答特定问题的列/行", + "isCorrect": "true" + }, + { + "answerText": "删除不符合您假设的验证值", + "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", + "isCorrect": "false" + } + ] + }, + { + "questionText": "使用标签列对数据进行分类是很有用的。", + "answerOptions": [{ + "answerText": "真", + "isCorrect": "true" + }, + { + "answerText": "错误", + "isCorrect": "false" + } + ] + } + ] + }, + { + "id": 40, + "title": "NLP 5：课后测验", + "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", + "isCorrect": "false" + }, + { + "answerText": "添加情绪和专栏，这将有助于您选择最好的酒店", + "isCorrect": "true" + }, + { + "answerText": "分析人们为什么留下特定的评论", + "isCorrect": "false" + } + ] + }, + { + "questionText": "什么是停止词？", + "answerOptions": [{ + "answerText": "不改变句子情绪的普通英语单词", + "isCorrect": "false" + }, + { + "answerText": "单词，你可以删除，以加快情绪分析", + "isCorrect": "false" + }, + { + "answerText": "以上两者", + "isCorrect": "true" + } + ] + }, + { + "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。", + "answerOptions": [{ + "answerText": "真", + "isCorrect": "true" + }, + { + "answerText": " 错误", + "isCorrect": "false" + } + ] + } + ] + }, + { + "id": 41, + "title": "时间系列简介：演讲前测验", + "quiz": [{ + "questionText": "时间系列预测在", + "answerOptions": [{ + "answerText": "确定未来成本", + "isCorrect": "false" + }, + { + "answerText": "预测未来定价", + "isCorrect": "false" + }, + { + "answerText": "以上两者", + "isCorrect": "true" + } + ] + }, + { + "questionText": "时间序列是按下列顺序拍摄的：", + "answerOptions": [{ + "answerText": "空间中连续的相同间隔点", + "isCorrect": "false" + }, + { + "answerText": "连续的相同间隔点的时间", + "isCorrect": "true" + }, + { + "answerText": "空间和时间的连续等间隔点", + "isCorrect": "false" + } + ] + }, + { + "questionText": "时间系列可用于：", + "answerOptions": [{ + "answerText": "地震预报", + "isCorrect": "true" + }, + { + "answerText": "计算机视觉", + "isCorrect": "false" + }, + { + "answerText": "颜色分析", + "isCorrect": "false" + } + ] + } + ] + }, + { + "id": 42, + "title": "时间系列简介：讲座后测验", + "quiz": [{ + "questionText": "时间系列趋势是", + "answerOptions": [{ + "answerText": "可测量的增减随时间推移而增加和减少", + "isCorrect": "true" + }, + { + "answerText": "量化会随着时间推移而减少", + "isCorrect": "false" + }, + { + "answerText": "随着时间的推移，增减之间的差距", + "isCorrect": "false" + } + ] + }, + { + "questionText": "离群值是", + "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", + "isCorrect": "true" + }, + { + "answerText": "标准数据方差内的点", + "isCorrect": "false" + } + ] + }, + { + "questionText": "时间系列预测最有用", + "answerOptions": [{ + "answerText": "计量经济学", + "isCorrect": "true" + }, + { + "answerText": "历史", + "isCorrect": "false" + }, + { + "answerText": "图书馆", + "isCorrect": "false" + } + ] + } + ] + }, + { + "id": 43, + "title": "时间系列阿里玛：演讲前测验", + "quiz": [{ + "questionText": "阿里玛代表", + "answerOptions": [{ + "answerText": "自动回归整体移动平均线", + "isCorrect": "false" + }, + { + "answerText": "自动回归综合移动操作", + "isCorrect": "false" + }, + { + "answerText": "自动递减综合移动平均线", + "isCorrect": "true" + } + ] + }, + { + "questionText": "固定性是指", + "answerOptions": [{ + "answerText": "属性在时间转移时不会更改的数据", + "isCorrect": "false" + }, + { + "answerText": "分布在时间转移时不会更改的数据", + "isCorrect": "true" + }, + { + "answerText": "数据其分布在时间转移时发生更改", + "isCorrect": "false" + } + ] + }, + { + "questionText": "差分", + "answerOptions": [{ + "answerText": "稳定趋势和季节性", + "isCorrect": "false" + }, + { + "answerText": "加剧趋势和季节性", + "isCorrect": "false" + }, + { + "answerText": "消除趋势和季节性", + "isCorrect": "true" + } + ] + } + ] + }, + { + "id": 44, + "title": "时间系列阿里玛：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", + "answerOptions": [{ + "answerText": "尽可能平坦", + "isCorrect": "false" + }, + { + "answerText": "尽可能紧密", + "isCorrect": "true" + }, + { + "answerText": "通过散射图", + "isCorrect": "false" + } + ] + }, + { + "questionText": "使用萨里玛克斯", + "answerOptions": [{ + "answerText": "管理季节性 ARIMA 模型", + "isCorrect": "true" + }, + { + "answerText": "管理特殊的 ARIMA 模型", + "isCorrect": "false" + }, + { + "answerText": "管理统计 ARIMA 模型", + "isCorrect": "false" + } + ] + }, + { + "questionText": "'向前走'验证涉及", + "answerOptions": [{ + "answerText": "重新评估模型，使其在验证时逐步进行", + "isCorrect": "false" + }, + { + "answerText": "重新培训模型，使其在验证时逐步恢复", + "isCorrect": "true" + }, + { + "answerText": "验证时逐步重新配置模型", + "isCorrect": "false" + } + ] + } + ] + }, + { + "id": 45, + "title": "强化 1：课前测验", + "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", + "isCorrect": "false" + }, + { + "answerText": "一种通过运行许多实验来破译代理在某些环境中的最佳行为的学习技术", + "isCorrect": "true" + }, + { + "answerText": "了解如何同时运行多个实验", + "isCorrect": "false" + } + ] + }, + { + "questionText": "什么是政策？", + "answerOptions": [{ + "answerText": "在任何给定状态下返回操作的功能", + "isCorrect": "true" + }, + { + "answerText": "一份文件，告诉你是否可以返回一个项目", + "isCorrect": "false" + }, + { + "answerText": "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。", + "answerOptions": [{ + "answerText": "真", + "isCorrect": "true" + }, + { + "answerText": "错误", + "isCorrect": "false" + } + ] + } + ] + }, + { + "id": 46, + "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", + "isCorrect": "false" + }, + { + "answerText": "由 Q 表定义策略的算法", + "isCorrect": "false" + }, + { + "answerText": "以上两个", + "isCorrect": "true" + } + ] + }, + { + "questionText": "Q-Table 与随机步行策略相对应的值是什么？", + "answerOptions": [{ + "answerText": "所有等值", + "isCorrect": "true" + }, + { + "answerText": "-0.25", + "isCorrect": "false" + }, + { + "answerText": "所有不同的值", + "isCorrect": "false" + } + ] + }, + { + "questionText": "在我们的学习过程中，使用探索比开发更好。", + "answerOptions": [{ + "answerText": "真", + "isCorrect": "false" + }, + { + "answerText": "错误", + "isCorrect": "true" + } + ] + } + ] + }, + { + "id": 47, + "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", + "isCorrect": "false" + }, + { + "answerText": "错误", + "isCorrect": "true" + } + ] + }, + { + "questionText": "什么是卡特波尔问题？", + "answerOptions": [{ + "answerText": "消除离群值的过程", + "isCorrect": "false" + }, + { + "answerText": "优化购物车的方法", + "isCorrect": "false" + }, + { + "answerText": "平衡的简化版本", + "isCorrect": "true" + } + ] + }, + { + "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？", + "answerOptions": [{ + "answerText": "猜测和检查", + "isCorrect": "false" + }, + { + "answerText": "模拟环境", + "isCorrect": "true" + }, + { + "answerText": "状态过渡测试", + "isCorrect": "false" + } + ] + } + ] + }, + { + "id": 48, + "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？", + "answerOptions": [{ + "answerText": "方法", + "isCorrect": "false" + }, + { + "answerText": "行动空间", + "isCorrect": "true" + }, + { + "answerText": "行动列表", + "isCorrect": "false" + } + ] + }, + { + "questionText": "我们用什么对作为字典的关键值？", + "answerOptions": [{ + "answerText": "（状态、动作）为键，Q表输入为值", + "isCorrect": "true" + }, + { + "answerText": "状态为键，行动为价值", + "isCorrect": "false" + }, + { + "answerText": "qvalue 的价值以功能为键，以行动为价值", + "isCorrect": "false" + } + ] + }, + { + "questionText": "我们在 Q 学习期间使用的超参数是什么？", + "answerOptions": [{ + "answerText": "q 表值、当前奖励、随机操作", + "isCorrect": "false" + }, + { + "answerText": "学习率、折扣系数、勘探/开发系数", + "isCorrect": "true" + }, + { + "answerText": "累积奖励、学习率、探索因素", + "isCorrect": "false" + } + ] + } + ] + }, + { + "id": 49, + "title": "现实世界应用：讲座前测验", + "quiz": [{ + "questionText": "金融行业 ML 应用的例子是什么？", + "answerOptions": [{ + "answerText": "使用 NLP 个性化客户旅程", + "isCorrect": "false" + }, + { + "answerText": "使用线性回归的财富管理", + "isCorrect": "true" + }, + { + "answerText": "使用时间系列进行能源管理", + "isCorrect": "false" + } + ] + }, + { + "questionText": "医院可以使用什么ML技术来管理重新接纳？", + "answerOptions": [{ + "answerText": "聚类", + "isCorrect": "true" + }, + { + "answerText": "时间系列", + "isCorrect": "false" + }, + { + "answerText": "NLP", + "isCorrect": "false" + } + ] + }, + { + "questionText": "使用时间系列进行能源管理的例子是什么？", + "answerOptions": [{ + "answerText": "运动感应动物", + "isCorrect": "false" + }, + { + "answerText": "智能停车表", + "isCorrect": "true" + }, + { + "answerText": "跟踪森林火灾", + "isCorrect": "false" + } + ] + } + ] + }, + { + "id": 50, + "title": "现实世界应用：讲座后测验", + "quiz": [{ + "questionText": "哪些 ML 技术可用于检测信用卡欺诈？", + "answerOptions": [{ + "answerText": "回归", + "isCorrect": "false" + }, + { + "answerText": "聚类", + "isCorrect": "true" + }, + { + "answerText": "NLP", + "isCorrect": "false" + } + ] + }, + { + "questionText": "在森林管理中体现了哪种ML技术？", + "answerOptions": [{ + "answerText": "强化学习", + "isCorrect": "true" + }, + { + "answerText": "时间系列", + "isCorrect": "false" + }, + { + "answerText": "NLP", + "isCorrect": "false" + } + ] + }, + { + "questionText": "在医疗保健行业应用 ML 的例子是什么？", + "answerOptions": [{ + "answerText": "使用回归预测学生行为", + "isCorrect": "false" + }, + { + "answerText": "使用分类器管理临床试验", + "isCorrect": "true" + }, + { + "answerText": "使用分类器对动物的运动感应", + "isCorrect": "false" + } + ] + } + ] + } + ] +}] \ No newline at end of file