自动机器学习
正版保障 假一赔十 可开发票
￥ 50.72 6.4折 ￥ 79 全新
库存38件
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作者(美)Adnan Masood著
出版社电子工业出版社
ISBN9787121457050
出版时间2023-06
装帧平装
开本其他
定价79元
货号12974687
上书时间2024-03-19
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作者简介
Adnan Masood，工程师、教师、研究员，在金融技术和开发大型系统方面拥有超过20年的全球经验。被微软评为微软区域总监和微软人工智能领域最有价值专家。担任UST-Global的首席人工智能官和首席架构师，负责公司在认知计算、人工智能、机器学习和学术关系方面的整体战略。与斯坦福人工智能实验室、麻省理工学院CSAIL合作，领导数据科学家和工程师团队构建人工智能解决方案，以产生影响一系列业务、产品和计划的商业价值和见解。在帕克大学教授数据科学，并在加州大学圣地亚哥分校教授Windows WCF课程。担任《财富》500强企业和初创企业顾问。曾出版亚马逊编程语言畅销书《f#函数编程》。
目录
第 1 章 走进自动机器学习··············································································.1 1.1 机器学习开发生命周期 ·······································································.1 1.2 自动机器学习简介 ·············································································.2 1.3 自动机器学习的工作原理 ····································································.3 1.4 数据科学的大众化 ·············································································.5 1.5 揭穿自动机器学习的迷思 ····································································.5 1.6 自动机器学习生态系统 ·······································································.6 1.7 小结 ·······························································································11 第 2 章 自动机器学习、算法和技术··································································12 2.1 自动机器学习概述 ·············································································12 2.2 自动特征工程 ···················································································15 2.3 超参数优化 ······················································································16 2.4 神经架构搜索 ···················································································18 2.5 小结 ·······························································································19 第 3 章 使用开源工具和库进行自动机器学习······················································20 3.1 技术要求 ·························································································20 3.2 自动机器学习的开源生态系统 ······························································21 3.3 TPOT······························································································22 3.4 Featuretools ······················································································29 3.5 Microsoft NNI ···················································································32 3.6 auto-sklearn ······················································································38 3.7 AutoKeras ························································································41 3.8 Ludwig ····························································································44 3.9 AutoGluon························································································44 3.10 小结······························································································44 第 4 章 Azure Machine Learning········································································45 4.1 Azure Machine Learning 入门 ································································45 4.2 Azure Machine Learning 栈 ···································································46 4.3 Azure Machine Learning 服务 ································································50 4.4 使用 Azure Machine Learning 建模 ·························································56 4.5 使用 Azure Machine Learning 部署和测试模型 ··········································68 4.6 小结 ·······························································································70 第 5 章 使用 Azure 进行自动机器学习 ·······························································71 5.1 Azure 中的自动机器学习 ·····································································71 5.2 使用自动机器学习进行时间序列预测 ·····················································85 5.3 小结 ·······························································································97 第 6 章 使用 AWS 进行机器学习 ······································································98 6.1 AWS 环境中的机器学习······································································98 6.2 开始使用 AWS ···············································································.101 6.3 使用 Amazon SageMaker Autopilot·······················································.109 6.4 使用 Amazon SageMaker JumpStart······················································.111 6.5 小结 ····························································································.111 第 7 章 使用 Amazon SageMaker Autopilot 进行自动机器学习······························.113 7.1 技术要求 ······················································································.113 7.2 创建 Amazon SageMaker Autopilot 受限实验··········································.113 7.3 创建 AutoML 实验 ··········································································.120 7.4 运行 SageMaker Autopilot 实验并部署模型············································.123 7.5 构建并运行 SageMaker Autopilot 实验··················································.126 7.6 小结 ····························································································.132 第 8 章 使用 Google Cloud Platform 进行机器学习 ·············································.134 8.1 Google Cloud Platform 使用入门 ·························································.134 8.2 使用 GCP 实现 AI 和 ML··································································.137 8.3 Google Cloud AI Platform 和 AI Hub·····················································.139 8.4 Google Cloud AI Platform 使用入门 ·····················································.141 8.5 使用 Google Cloud 进行 AutoML ························································.144 8.6 小结 ····························································································.146 第 9 章 使用 GCP 进行自动机器学习······························································.147 9.1 Google Cloud AutoML Tables······························································.147 9.2 创建 AutoML Tables 实验··································································.148 9.3 了解 AutoML Tables 模型部署····························································.158 9.4 在 AutoML Tables 上使用 BigQuery 公共数据集 ·····································.162 9.5 自动机器学习做价格预测 ·································································.164 9.6 小结 ····························································································.170 第 10 章 企业中的自动机器学习 ···································································.171 10.1 企业是否需要自动机器学习·····························································.171 10.2 自动机器学习――企业高级分析的加速器···········································.172 10.3 自动机器学习的挑战和机遇·····························································.173 10.4 建立信任――自动机器学习中的模型可解释性和透明度 ························.174 10.5 在企业中引入自动机器学习·····························································.176 10.6 总结与展望··················································································.177
内容摘要
本书重点讲解基于云平台的超参数优化、神经构架搜索以及算法选择等内容，是自动机器学习的基本任务。介绍了基于三个主要云服务提供商（包括 Microsoft Azure、Amazon Web Services (AWS) 和 Google Cloud Platform）进行 AutoML，同时部署 ML 模型和管道，具有较强的实用性。在应用场景中评估 AutoML 方面，例如算法选择、自动特征化和超参数调整，并区分云和 OSS 产品等。本书适用于从事机器学习或人工智能方向的数据科学家或工程师学习，也适合学生或行业初学者进行入门学习实践。
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精彩内容
本书重点讲解基于云平台的超参数优化、神经构架搜索以及算法选择等内容，是自动机器学习的基本任务。介绍了基于三个主要云服务提供商（包括 Microsoft Azure、Amazon Web Services (AWS) 和 Google Cloud Platform）进行 AutoML，同时部署 ML 模型和管道，具有较强的实用性。在应用场景中评估 AutoML 方面，例如算法选择、自动特征化和超参数调整，并区分云和 OSS 产品等。本书适用于从事机器学习或人工智能方向的数据科学家或工程师学习，也适合学生或行业初学者进行入门学习实践。
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