April 2026
Intermediate
586 pages
7h 41m
Chinese
Content preview from 学习 AutoML (Chinese Edition)
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第三部分： 适用于不同数据类型的AutoML
现实世界中的机器学习问题并非千篇一律。您可能需要基于结构化数据库记录预测客户流失率，根据原始文本对支持工单进行分类，对数千种产品进行需求预测，或者对产品目录中的图像进行分类。每种数据类型都面临独特的挑战——而 AutoML 框架已针对每种类型发展出了专门的解决方案。
在本部分中，我们将深入探讨你在生产环境中会遇到的四种主要数据模态的实际操作与实现。对于表格数据，我们将探讨为什么梯度提升集成模型依然占据主导地位，以及 AutoML 是如何协调这些模型的。对于文本数据，你将学习如何利用基于 Transformer 的模型，而无需成为 Deep Learning 专家。时间序列预测引入了时间依赖性，其评估策略与标准机器学习有着根本性的差异。 计算机视觉部分则展示了 AutoML 如何将曾经需要专业团队才能实现的能力普及化。每章都包含经过生产环境验证的代码、性能基准测试，以及那些区分成功部署与仅停留在演示阶段（却无法投入生产）的宝贵经验教训。
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