(conceptual or physical) random experiment Event A is a subset of the sample space S P(A) is the probability that event A happens It is a function that maps the event A onto the interval [0, 1]. P(A) is also also called the probability measure of A Kolmogorov axioms Non-negativity: p(A) ≥ 0 for each event A P(S) = 1 σ-additivity: For disjoint events {Ai}i such that Ai � Aj = ∅ for ∀i ̸= j P( ∞ � i=1 Ai) Conditional Probability Definition of conditional probability: Fraction of worlds in which event A is true given event B is true P(A | B) = P(A, B) P(B) , P(A, B) = P(A | B)P(B) Corollary: The chain rule

P(B) (1) where P(A | B) is the conditional probability of event A given event B happens, P(B | A) is the conditional probability of event B given A is true, and P(A) and P(B) are probability of observing

而分类预测结果需要得到[0,1]的概率值。 在二分类模型中，事件的几率odds：事件发生与事件不发生的概率之比为 ? 1−?， 称为事件的发生比（the odds of experiencing an event） 其中?为随机事件发生的概率，?的范围为[0,1]。 取对数得到：log ? 1−?，而log ? 1−? = ?T? = ? 求解得到：? = 1 1+?−?T? = 1

模块。其主要功能是获取和输出模型相关的 序列化数据，它贯通 TensorBoard 的整个使用流程。 tf.summary 模块的核心部分由一组汇总操作以及 FileWriter、Summary 和 Event 3个类组成。 可视化数据流图 工作流 创建 数据流图 创建 FileWriter 实例 启动 TensorBoard Which one is better？ VS ✅

TensorFlow on Yarn设计 TensorFlow作业Tensorboard页面：� TensorFlow on Yarn设计 TensorFlow作业history页面：� Event log上传到了HDFS� 查看历史日志� TensorFlow on Yarn技术细节揭秘 实现Yarn Application的标准流程：� TensorFlow on Yarn技术细节揭秘

不重不丢：重复的数据会使模型有偏，数据的缺失 会使模型丢失重要信息  数据有序性：数据乱序会导致样本穿越的现象 • Log Join框架  双流拼接框架，通过组合方式支持多流拼接  基于Event Time的Window机制拼接方式  基于Low Watermark解决流乱序、流延迟等流式常 见问题 流式拼接框架 • Low Watermark机制  定义了流式数据的时钟，不可逆性

and combination operations naturally fit into evolution based architecture search where a crossover event could be implemented through a random tweak to the configuration of a block. In the paper8 titled

similar to typical human behavior when making a big decision (a big purchase or an important life event). We discuss with friends and family to decide whether it is a good decision. We rely on their perspectives

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否有瑕疵。检查骰子的唯一方法是多 次投掷并记录结果。对于每个骰子，我们将观察到{1, . . . , 6}中的一个值。对于每个值，一种自然的方法是将 它出现的次数除以投掷的总次数，即此事件（event）概率的估计值。大数定律（law of large numbers）告 诉我们：随着投掷次数的增加，这个估计值会越来越接近真实的潜在概率。让我们用代码试一试！ 首先，我们导入必要的软件包。 74 在处理骰子掷出时，我们将集合S = {1, 2, 3, 4, 5, 6} 称为样本空间（sample space）或结果空间（outcome space），其中每个元素都是结果（outcome）。事件（event）是一组给定样本空间的随机结果。例如，“看 到5”（{5}）和“看到奇数”（{1, 3, 5}）都是掷出骰子的有效事件。注意，如果一个随机实验的结果在A中，则 事件A已经发生。也就是说，如果投掷出3点，因为3