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自動機器學習 版權信息
- ISBN：9787121457050
- 條形碼：9787121457050 ; 978-7-121-45705-0
- 裝幀：平塑
- 冊數：暫無
- 重量：暫無
- 所屬分類：>>
自動機器學習 內容簡介
本書重點講解基于云平臺的超參數優化、神經構架搜索以及算法選擇等內容，是自動機器學習的基本任務。介紹了基于三個主要云服務提供商（包括 Microsoft Azure、Amazon Web Services (AWS) 和 Google Cloud Platform）進行 AutoML，同時部署 ML 模型和管道，具有較強的實用性。在應用場景中評估 AutoML 方面，例如算法選擇、自動特征化和超參數調整，并區分云和 OSS 產品等。本書適用于從事機器學習或人工智能方向的數據科學家或工程師學習，也適合學生或行業初學者進行入門學習實踐。
自動機器學習 目錄
第 1 章 走進自動機器學習··············································································.1
1.1 機器學習開發生命周期 ·······································································.1
1.2 自動機器學習簡介 ·············································································.2
1.3 自動機器學習的工作原理 ····································································.3
1.4 數據科學的大眾化 ·············································································.5
1.5 揭穿自動機器學習的迷思 ····································································.5
1.6 自動機器學習生態系統 ·······································································.6
1.7 小結 ·······························································································11
第 2 章 自動機器學習、算法和技術··································································12
2.1 自動機器學習概述 ·············································································12
2.2 自動特征工程 ···················································································15
2.3 超參數優化 ······················································································16
2.4 神經架構搜索 ···················································································18
2.5 小結 ·······························································································19
第 3 章 使用開源工具和庫進行自動機器學習······················································20
3.1 技術要求 ·························································································20
3.2 自動機器學習的開源生態系統 ······························································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 小結······························································································44
第 4 章 Azure Machine Learning········································································45
4.1 Azure Machine Learning 入門 ································································45
4.2 Azure Machine Learning 棧 ···································································46
4.3 Azure Machine Learning 服務 ································································50
4.4 使用 Azure Machine Learning 建模 ·························································56
4.5 使用 Azure Machine Learning 部署和測試模型 ··········································68
4.6 小結 ·······························································································70
第 5 章 使用 Azure 進行自動機器學習 ·······························································71
5.1 Azure 中的自動機器學習 ·····································································71
5.2 使用自動機器學習進行時間序列預測 ·····················································85
5.3 小結 ·······························································································97
第 6 章 使用 AWS 進行機器學習 ······································································98
6.1 AWS 環境中的機器學習······································································98
6.2 開始使用 AWS ···············································································.101
6.3 使用 Amazon SageMaker Autopilot·······················································.109
6.4 使用 Amazon SageMaker JumpStart······················································.111
6.5 小結 ····························································································.111
第 7 章 使用 Amazon SageMaker Autopilot 進行自動機器學習······························.113
7.1 技術要求 ······················································································.113
7.2 創建 Amazon SageMaker Autopilot
1.1 機器學習開發生命周期 ·······································································.1
1.2 自動機器學習簡介 ·············································································.2
1.3 自動機器學習的工作原理 ····································································.3
1.4 數據科學的大眾化 ·············································································.5
1.5 揭穿自動機器學習的迷思 ····································································.5
1.6 自動機器學習生態系統 ·······································································.6
1.7 小結 ·······························································································11
第 2 章 自動機器學習、算法和技術··································································12
2.1 自動機器學習概述 ·············································································12
2.2 自動特征工程 ···················································································15
2.3 超參數優化 ······················································································16
2.4 神經架構搜索 ···················································································18
2.5 小結 ·······························································································19
第 3 章 使用開源工具和庫進行自動機器學習······················································20
3.1 技術要求 ·························································································20
3.2 自動機器學習的開源生態系統 ······························································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 小結······························································································44
第 4 章 Azure Machine Learning········································································45
4.1 Azure Machine Learning 入門 ································································45
4.2 Azure Machine Learning 棧 ···································································46
4.3 Azure Machine Learning 服務 ································································50
4.4 使用 Azure Machine Learning 建模 ·························································56
4.5 使用 Azure Machine Learning 部署和測試模型 ··········································68
4.6 小結 ·······························································································70
第 5 章 使用 Azure 進行自動機器學習 ·······························································71
5.1 Azure 中的自動機器學習 ·····································································71
5.2 使用自動機器學習進行時間序列預測 ·····················································85
5.3 小結 ·······························································································97
第 6 章 使用 AWS 進行機器學習 ······································································98
6.1 AWS 環境中的機器學習······································································98
6.2 開始使用 AWS ···············································································.101
6.3 使用 Amazon SageMaker Autopilot·······················································.109
6.4 使用 Amazon SageMaker JumpStart······················································.111
6.5 小結 ····························································································.111
第 7 章 使用 Amazon SageMaker Autopilot 進行自動機器學習······························.113
7.1 技術要求 ······················································································.113
7.2 創建 Amazon SageMaker Autopilot
展開全部
自動機器學習 作者簡介
Adnan Masood，工程師、教師、研究員，在金融技術和開發大型系統方面擁有超過20年的全球經驗。被微軟評為微軟區域總監和微軟人工智能領域最有價值專家。擔任UST-Global的首席人工智能官和首席架構師，負責公司在認知計算、人工智能、機器學習和學術關系方面的整體戰略。與斯坦福人工智能實驗室、麻省理工學院CSAIL合作，領導數據科學家和工程師團隊構建人工智能解決方案，以產生影響一系列業務、產品和計劃的商業價值和見解。在帕克大學教授數據科學，并在加州大學圣地亞哥分校教授Windows WCF課程。擔任《財富》500強企業和初創企業顧問。曾出版亞馬遜編程語言暢銷書《f#函數編程》。
Adnan Masood，工程師、教師、研究員，在金融技術和開發大型系統方面擁有超過20年的全球經驗。被微軟評為微軟區域總監和微軟人工智能領域最有價值專家。擔任UST-Global的首席人工智能官和首席架構師，負責公司在認知計算、人工智能、機器學習和學術關系方面的整體戰略。與斯坦福人工智能實驗室、麻省理工學院CSAIL合作，領導數據科學家和工程師團隊構建人工智能解決方案，以產生影響一系列業務、產品和計劃的商業價值和見解。在帕克大學教授數據科學，并在加州大學圣地亞哥分校教授Windows WCF課程。擔任《財富》500強企業和初創企業顧問。曾出版亞馬遜編程語言暢銷書《f#函數編程》。
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