September 2022
Intermediate to advanced
549 pages
11h 27m
Chinese
Content preview from 基于AWS的数据科学实践
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450
｜
第
11
章
也将计算接收到消息的持续近似计数（
continuous approximate count
）。这一在线消
息的计数可用于数字营销团队以衡量社交媒体活动的有效性。
本章提供描述性分析（汇总统计）和预测分析的示例，使用我们在前面章节训练、
微调并部署的基于
BERT
的
SageMaker
模型。
11.1
在线学习与离线学习
在第
9
章，我们通过使用来自示例客户评价应用的实时回报数据持续训练强化学习
模型，演示了如何进行近实时的“在线学习”。在线机器学习或增量机器学习是机
器学习的一个小的子集，其有些难以采用经典离线算法进行有效的在线训练。借助
在线学习，新数据并入模型而无需使用完整数据集完全重新训练模型。
一般来说，线性算法，例如线性回归（
linear regression
）、逻辑回归（
logistic
regression
）和
K
均值聚类（
K-Means Clustering
），由于其背后的数学方法相对简单，
更容易使用实时数据进行训练。
Scikit-learn
支持对特定线性算法调用
partial_fit()
函数进行增量学习。
Apache Spark
支持流版本的线性回归和
K
均值聚类。
深度学习算法也能够进行在线学习，因为其使用微小批量的新数据对学到的权重持
续进行微小调整。事实上，任何时候我们从已有模型检查点或预训练模型（而非随
机的初始权重）训练深度学习模型，我们就是在运行增量在线学习，尽管由于数据
通常从磁盘而非流传递至算法这种方式通常较慢。
11.2
流应用
流应用数据不像传统应用数据那样，通常由 ...
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.