Practical Automated Machine Learning on Azure
Using Azure Machine Learning to Quickly Build AI Solutions
暫譯: 在 Azure 上的實用自動化機器學習
Mukunthu, Deepak, Shah, Parashar, Tok, Wee Hyong
- 出版商: O'Reilly
- 出版日期: 2019-10-29
- 定價: $1,980
- 售價: 9.5 折 $1,881
- 貴賓價: 9.0 折 $1,782
- 語言: 英文
- 頁數: 215
- 裝訂: Quality Paper - also called trade paper
- ISBN: 149205559X
- ISBN-13: 9781492055594
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相關分類:
Microsoft Azure、人工智慧、Machine Learning
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相關翻譯:
基於 Azure 的自動機器學習 (Practical Automated Machine Learning on Azure Using Azure Machine Learning to Quickly Build AI Solutions) (簡中版)
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商品描述
Develop smart applications without spending days and weeks building machine-learning models. With this practical book, you'll learn how to apply automated machine learning (AutoML), a process that uses machine learning to help people build machine learning models. Deepak Mukunthu, Parashar Shah, and Wee Hyong Tok provide a mix of technical depth, hands-on examples, and case studies that show how customers are solving real-world problems with this technology.
Building machine-learning models is an iterative and time-consuming process. Even those who know how to create ML models may be limited in how much they can explore. Once you complete this book, you'll understand how to apply AutoML to your data right away.
- Learn how companies in different industries are benefiting from AutoML
- Get started with AutoML using Azure
- Explore aspects such as algorithm selection, auto featurization, and hyperparameter tuning
- Understand how data analysts, BI professions, developers can use AutoML in their familiar tools and experiences
- Learn how to get started using AutoML for use cases including classification, regression, and forecasting.
商品描述(中文翻譯)
開發智能應用程式,而不需要花費數天或數週來建立機器學習模型。透過這本實用的書籍,您將學會如何應用自動化機器學習(AutoML),這是一個利用機器學習幫助人們建立機器學習模型的過程。Deepak Mukunthu、Parashar Shah 和 Wee Hyong Tok 提供了技術深度、實作範例和案例研究的混合,展示客戶如何利用這項技術解決現實世界的問題。
建立機器學習模型是一個迭代且耗時的過程。即使是那些知道如何創建機器學習模型的人,也可能在探索的深度上受到限制。一旦您完成這本書,您將立即了解如何將 AutoML 應用於您的數據。
- 了解不同行業的公司如何從 AutoML 中受益
- 使用 Azure 開始使用 AutoML
- 探索算法選擇、自動特徵化和超參數調整等方面
- 理解數據分析師、商業智慧專業人士和開發人員如何在他們熟悉的工具和經驗中使用 AutoML
- 學習如何開始使用 AutoML 進行分類、回歸和預測等用例。
作者簡介
Deepak Mukunthu is a product leader with 16+ years of experience. With his experience in Big data, Analytics and AI, Deepak has played instrumental leadership roles in transforming organizations and teams become data driven and adopt machine learning. He brings a good mix of thought leadership, customer understanding and innovation to design and deliver compelling products that resonate well with customers. In his current role of Principal Program Manager on Automated ML in Azure AI platform group at Microsoft, Deepak drives product strategy and roadmap for Automated ML with the goal of accelerating AI for data scientists and democratizing AI for other personas interested in machine learning. In addition to shaping the product direction, he also plays an instrumental role in helping customers adopt Automated ML for their business-critical scenarios. Prior to joining Microsoft, Deepak worked at Trilogy where he played multiple roles - Consultant, Business development, Program manager, Engineering manager - successfully leading distributed teams across the globe and managing technical integration of acquisitions.
Parashar Shah works for Microsoft as a Data Scientist, Senior Program/Product Manager in Azure Machine Learning platform team within the Cloud + AI Platform organization. His first book, Hands-On Machine Learning with Azure: Build powerful models with cognitive machine learning and artificial intelligence, was published in Nov 2018. Prior to joining Microsoft, he worked for Alcatel-Lucent/Nokia Networks/Bell Labs where he helped global telecom operators (across North America, Europe, Middle East and APAC) as a solution architect/product manager. Parashar has a MBA from Indian Institute of Management Bangalore & B.E. (E.C.) from Nirma Institute of Technology, Ahmedabad. He has filed for 5 patents (in published state), he loves to work on new technologies and ideas. Parashar's experience and interests span across Artificial Intelligence, Machine Learning, Big Data, Data Science, Blockchain, Virtual Reality, Internet of Things (IoT), Advanced Analytics, Mobile application development, Wireless Technologies & Device Management.
Wee Hyong Tok is part of the AzureCAT team at Microsoft. He has extensive leadership experience leading multi-disciplinary team of engineers and data scientists, working on cutting-edge AI capabilities that are infused into products and services. He is a tech visionary with a background in product management, machine learning/deep learning and working on complex engagements with customers. Over the years, he has demonstrated that his early thought-leadership white papers on tech trends have become reality, and deeply integrated into many products. His ability to strategize, and turn strategy to execution, and hunting for customer adoption has enabled many projects that he works on to be successful. He is continuously pushing the boundaries of products for machine learning and deep learning. His team works extensively with deep learning frameworks, ranging from TensorFlow, CNTK, Keras, and PyTorch. Wee Hyong has worn many hats in his career - developer, program/product manager, data scientist, researcher, and strategist, and his range of experience has given him unique super powers to lead and define the strategy for high-performing Data and AI innovation teams. Throughout his career, he has been a trusted advisor to the C-suite, from Fortune 500 companies to startups.
作者簡介(中文翻譯)
Deepak Mukunthu 是一位擁有超過 16 年經驗的產品領導者。憑藉在大數據、分析和人工智慧方面的經驗,Deepak 在轉型組織和團隊成為數據驅動並採用機器學習方面發揮了重要的領導作用。他結合了思想領導力、客戶理解和創新,設計並交付與客戶需求相符的引人注目的產品。在他目前擔任微軟 Azure AI 平台組的自動化機器學習(Automated ML)首席專案經理的角色中,Deepak 推動自動化機器學習的產品策略和路線圖,目標是加速數據科學家的人工智慧發展,並使其他對機器學習感興趣的人士能夠民主化人工智慧。除了塑造產品方向外,他還在幫助客戶採用自動化機器學習以應對業務關鍵場景方面發揮了重要作用。在加入微軟之前,Deepak 曾在 Trilogy 擔任多個角色,包括顧問、業務開發、專案經理和工程經理,成功領導全球分散的團隊並管理收購的技術整合。
Parashar Shah 在微軟擔任數據科學家、高級專案/產品經理,隸屬於雲端 + AI 平台組織的 Azure 機器學習平台團隊。他的第一本書《Hands-On Machine Learning with Azure: Build powerful models with cognitive machine learning and artificial intelligence》於 2018 年 11 月出版。在加入微軟之前,他曾在 Alcatel-Lucent/Nokia Networks/Bell Labs 擔任解決方案架構師/產品經理,幫助全球電信運營商(遍及北美、歐洲、中東和亞太地區)。Parashar 擁有印度班加羅爾管理學院的 MBA 學位及阿默達巴德 Nirma 技術學院的電子工程學士學位。他已申請 5 項專利(已公開狀態),熱愛從事新技術和新想法的工作。Parashar 的經驗和興趣涵蓋人工智慧、機器學習、大數據、數據科學、區塊鏈、虛擬實境、物聯網 (IoT)、高級分析、行動應用程式開發、無線技術和設備管理。
Wee Hyong Tok 是微軟 AzureCAT 團隊的一員。他擁有豐富的領導經驗,領導多學科的工程師和數據科學家團隊,致力於將尖端的人工智慧能力融入產品和服務中。他是一位技術願景家,擁有產品管理、機器學習/深度學習的背景,並與客戶進行複雜的合作。多年來,他證明了自己早期的思想領導白皮書關於技術趨勢的預測已成為現實,並深度整合到許多產品中。他的策略規劃能力、將策略轉化為執行的能力,以及尋求客戶採用的能力,使他所參與的許多專案都取得了成功。他不斷推動機器學習和深度學習產品的邊界。他的團隊廣泛使用深度學習框架,包括 TensorFlow、CNTK、Keras 和 PyTorch。Wee Hyong 在職業生涯中擔任過多個角色——開發者、專案/產品經理、數據科學家、研究員和策略家,他的多元經驗賦予了他獨特的超能力,能夠領導和定義高效能數據和人工智慧創新團隊的策略。在他的職業生涯中,他一直是 C-suite 的可信顧問,服務於從《財富》500 強公司到初創企業的各類客戶。