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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307
第 8 章
规模化训练与优化模型
杰夫
·
贝索斯最喜爱的商业战略家之一彼得
·
德鲁克（
Peter Drucker
）曾说过：“
If
you can
’
t measure it, you can
’
t improve it
（如果你不能衡量它，你就不能增长它）。”
这句名言概括了本章的本质，关注衡量、优化并改进我们的预测模型。
在第
7
章，我们使用
Amazon SageMaker
基于单独一组超参数训练了单个模型。我
们也演示了如何微调预训练的
BERT
模型来构建评价文本分类模型，用以大致预测
来源于社交渠道、合作网站等的产品评价的情绪。
在本章，我们将使用
SageMaker
实验（
Experiments
）来规模化地衡量、追踪、比较
并改善模型。之后，我们使用
SageMaker
超参数微调（
Hyper-Parameter Tuning
）为
特定的算法和数据集选择最佳超参数。接下来，我们也将展示如何使用不同的通信
策略和分布式文件系统进行分布式训练。最后，我们提供通过
SageMaker Autopilot
的超参数选择算法、
SageMaker
为
S3
优化的管道以及
AWS
的增强网络硬件来降低
开销并提升性能的技巧。
8.1
自动发现模型的最优超参数
既然我们理解了如何追踪并比较模型训练，我们就可以使用被称为超参数微调
（
hyper-parameter tuning, HPT
）或超参数优化（
hyper-parameter optimization,
HPO
）的可扩展过程，为数据集和算法自动寻找最佳超参数。
SageMaker
原生支持 ...
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I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
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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.