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  • 1
    Orig.schr. Ausgabe: 初版.
    Title: 実践時系列解析 : : 統計と機械学習による予測 /
    Publisher: オライリー・ジャパン,
    ISBN: 9784873119601 , 487311960X
    Language: Japanese
    Pages: 1 online resource (484 pages)
    Edition: Shohan.
    Uniform Title: Practical time series analysis
    DDC: 519.5/50285
    Keywords: Time-series analysis Data processing ; Machine learning ; Python (Computer program language) ; R (Computer program language) ; Machine learning ; Python (Computer program language) ; R (Computer program language) ; Time-series analysis ; Data processing
    Note: In Japanese.
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  • 2
    Title: 时间序列分析实战 : : 基于机器学习和统计学 = Practical time series analysis /
    ISBN: 9787115605443 , 7115605440
    Language: Chinese
    Pages: 1 online resource (376 pages) , illustrations
    Edition: [First edition].
    Uniform Title: Practical time series analysis
    DDC: 519.5/50285
    Keywords: Time-series analysis Data processing ; Time-series analysis Statistical methods ; Machine learning ; Python (Computer program language) ; R (Computer program language)
    Abstract: Detailed summary in vernacular field.
    Note: Includes index
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  • 3
    Language: English
    Pages: 1 online resource (1 video file, approximately 3 hr., 39 min.)
    Edition: 1st edition.
    DDC: 006.3/1
    Keywords: Streaming video ; Internet videos ; Vidéo en continu ; Vidéos sur Internet ; streaming video ; Internet videos ; Streaming video ; Electronic videos
    Abstract: Join us for an event focused on the many aspects of designing, deploying, and maintaining responsible AI. Event chair and responsible AI expert Rumman Chowdhury offers overarching context, stitching together shorter tech talks and conversations with industry leaders. What you'll learn and how you can apply it Discover what responsible AI includes (and what it doesn't) See what responsible AI looks like in action, from data to deployment to debugging Learn how to debug your ML model Explore real-world applications of responsible AI Understand what industry leaders think about when they think about responsibility This course is for you because... You're a machine learning engineer or data scientist interested in responsible AI. You're engaged in conversations about ethics and AI. You're wondering how to improve your own AI and machine learning. You're responsible for implementing fair or ethical AI practices in your role or project and looking for hands-on examples. Recommended follow-up: Read Responsible Machine Learning (report) Read AI and the Law (report) Read Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow (book)
    Note: Online resource; Title from title screen (viewed June 16, 2021)
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  • 4
    Online Resource
    Online Resource
    [Erscheinungsort nicht ermittelbar] : Helion | Boston, MA : Safari
    ISBN: 9788328367210
    Language: English , Polish
    Pages: 1 online resource (432 pages)
    Edition: 3rd edition
    Keywords: Electronic books ; local
    Abstract: Ta książka jest szerokim, aktualnym i praktycznym przeglądem metod analizy szeregów czasowych, w którym ujęto pełny potok przetwarzania danych czasowych i modelowania. Zaprezentowano w niej rzeczywiste przypadki użycia tych metod i zilustrowano je obszernymi fragmentami znakomicie zaprojektowanego kodu w językach R i Python. Znalazły się tutaj praktyczne wskazówki ułatwiające rozwiązywanie najczęstszych problemów występujących w inżynierii danych czasowych i ich analizie. Ujęto tu zarówno konwencjonalne metody statystyczne, jak i nowoczesne techniki uczenia maszynowego. To bardzo przydatny przewodnik, dzięki któremu analitycy danych, inżynierowie oprogramowania i naukowcy będą mogli płynnie przejść od podstaw pracy z szeregami czasowymi do rozwiązywania konkretnych zagadnień na profesjonalnym poziomie.
    Note: Online resource; Title from title page (viewed September 23, 2024) , Mode of access: World Wide Web.
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  • 5
    Online Resource
    Online Resource
    [Erscheinungsort nicht ermittelbar] : O'Reilly Media, Inc. | Boston, MA : Safari
    Language: English
    Pages: 1 online resource (1 video file, approximately 56 min.)
    Edition: 1st edition
    Keywords: Electronic videos ; local
    Abstract: Join us for this edition of Meet the Expert with Aileen Niesen to learn how we recognize and cope with unfairness in AI. O'Reilly Meet the Expert explores emerging business and technology topics and ideas through a series of one-hour interactive events. You’ll engage in a live conversation with experts, sharing your questions and ideas while hearing their unique perspectives, insights, fears, and predictions. What you’ll learn and how you can apply it By the end of this live show, you’ll better understand: What fairness and unfairness in AI look like How unfairness comes about How you can address unfairness in AI This event is for you because… You want to learn about the fundamental shifts that are transforming the business landscape and customer needs Prerequisites: Come with your questions for the expert Have a pen and paper handy to capture notes, insights, and inspiration
    Note: Online resource; Title from title screen (viewed December 8, 2020) , Mode of access: World Wide Web.
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  • 6
    Language: English
    Pages: 1 online resource (1 video file, approximately 6 hr., 52 min.)
    Edition: 1st edition
    Keywords: Electronic videos
    Abstract: O’Reilly Radar: Data & AI will showcase what’s new, what’s important, and what’s coming in the field. It includes two keynotes and two concurrent three-hour tracks—designed to lay out for tech leaders the issues, tools, and best practices that are critical to an organization at any step of their data and AI journey. You’ll explore everything from prototyping and pipelines to deployment and DevOps to responsible and ethical AI.
    Note: Online resource; Title from title screen (viewed October 14, 2021) , Mode of access: World Wide Web.
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  • 7
    Language: English
    Pages: 1 online resource (1 video file, approximately 3 hr., 39 min.)
    Edition: 1st edition
    Keywords: Electronic videos ; local
    Abstract: Join us for an event focused on the many aspects of designing, deploying, and maintaining responsible AI. Event chair and responsible AI expert Rumman Chowdhury offers overarching context, stitching together shorter tech talks and conversations with industry leaders. What you’ll learn and how you can apply it Discover what responsible AI includes (and what it doesn’t) See what responsible AI looks like in action, from data to deployment to debugging Learn how to debug your ML model Explore real-world applications of responsible AI Understand what industry leaders think about when they think about responsibility This course is for you because… You're a machine learning engineer or data scientist interested in responsible AI. You’re engaged in conversations about ethics and AI. You're wondering how to improve your own AI and machine learning. You're responsible for implementing fair or ethical AI practices in your role or project and looking for hands-on examples. Recommended follow-up: Read Responsible Machine Learning (report) Read AI and the Law (report) Read Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow (book)
    Note: Online resource; Title from title screen (viewed June 16, 2021) , Mode of access: World Wide Web.
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  • 8
    Language: English
    Pages: 1 online resource (1 streaming video file (1 hr., 58 min., 5 sec.)) , digital, sound, color
    Keywords: Medical care ; Data processing ; Quantitative research ; Linear models (Statistics) ; Data mining ; Electronic videos ; local
    Abstract: "Linear methods have traditionally been the workhorse of data analysis in many domains, and health-related applications are no exception. However, linear methods have a lot more to offer than standard regression analysis. This video explains why linear thinking remains a powerful and sophisticated way to think about data for prediction, causal analysis, and optimization in health tech. Designed for data scientists and for data savvy health care managers and clinicians, it demonstrates how to strengthen the conclusions you draw from health-related data and how to better allocate your health care resources."--Resource description page.
    Note: Title from title screen (Safari, viewed December 6, 2017). - Release information from resource description page (Safari, viewed December 6, 2017)
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  • 9
    Online Resource
    Online Resource
    [Place of publication not identified] : O'Reilly Media
    Language: English
    Pages: 1 online resource (1 streaming video file (1 hr., 33 min., 17 sec.)) , digital, sound, color
    Keywords: Medical care ; Data processing ; Neural networks (Computer science) ; Data mining ; Quantitative research ; Electronic videos ; local
    Abstract: "Neural networks have been widely adopted across many industries as the ultimate pattern recognition tool. While their current uses in healthcare are limited, neural networks have a promising future in diagnostic and decision making applications, because of their ability to mimic--and improve on--human capabilities in health-related advice and treatment. This video explains the basics of neural networks; shows examples of training neural networks with both image-based and unstructured healthcare data; and describes the kinds of neural networks most likely to be useful for health-related applications."--Resource description page.
    Note: Title from title screen (Safari, viewed December 6, 2017). - Release information from resource description page (Safari, viewed December 6, 2017)
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  • 10
    Online Resource
    Online Resource
    [Place of publication not identified] : O'Reilly Media
    Language: English
    Pages: 1 online resource (1 streaming video file (1 hr., 46 min., 43 sec.)) , digital, sound, color
    Keywords: Medical care ; Data processing ; Data mining ; Quantitative research ; Electronic videos ; local
    Abstract: "One of the most exciting and practical goals of combining healthcare with technology is to mine large quantities of data to discover what, if anything, has eluded researchers--either through a lack of sufficiently large datasets or a lack of human ability to notice unlikely relationships. Unsupervised learning is a promising avenue for pursuing this goal, because unsupervised machine learning techniques do not require existing human knowledge to generate new insights about structure within datasets. This video, designed for learners with a basic understanding of statistics and computer programming, provides a detailed introduction to three specific types of unsupervised learning: cluster analysis, association analysis, and principal components analysis, as applied to health data sets both at the individual and population levels. Examples will be introduced in both Python and R."--Resource description page.
    Note: Title from title screen (Safari, viewed December 6, 2017). - Release information from resource description page (Safari, viewed December 6, 2017)
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