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 count(i)(t) = �ni j=1 1(x(i) j = t) is the number of features in x(i) whose values are t (i.e., how many time t occurs in x(i) ). By now, we formulate our NB model for multinomial distribution as follows max

model, input data and the hyperparameter trial set is ready. Let's go ahead and train the model, each time choosing one item from the trial set. Each model is trained for 2000 iterations. At the end of a trial on the hyperparameters for the final training. For large models, this is very expensive in terms of time and resources. Alternatively, we can base the search approach on the budget allocation to cap the 24s] val_accuracy: 0.6313725709915161 Best val_accuracy So Far: 0.7284313440322876 Total elapsed time: 00h 17m 23s Results summary Results in hpo/hyperband Showing 3 best trials Trial summary Hyperparameters:

similar to the baseline, but does so in fewer epochs. We could ideally save an epoch’s worth of training time by terminating the training early, if we adopt this hypothetical sample efficient model training. effective utilization of the training data. Labeling data is often an expensive process both in terms of time consumption and fiscal expenditure because it involves human labelers looking at each example and the four classes, three of which are the keywords that the device will accept: hello, weather and time. The fourth class (none) indicates the absence of an acceptable keyword in the input signal. Figure

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

tested against each NGC monthly container release to ensure consistent accuracy and performance over time. ‣ ResNeXt101-32x4d model: This model was introduced in the Aggregated Residual Transformations for leverages mixed precision arithmetic by using Tensor Cores on NVIDIA V100 GPUs for 1.3x faster training time while maintaining target accuracy. This model script is available on GitHub and NGC. ‣ Tacotron tested against each NGC monthly container release to ensure consistent accuracy and performance over time. ‣ ResNeXt101-32x4d model: This model was introduced in the Aggregated Residual Transformations for

(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

import math import os import random import re import shutil import sys import tarfile import time import zipfile from collections import defaultdict import pandas as pd import requests from IPython 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

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