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  • 1
    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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  • 2
    Online Resource
    Online Resource
    [Erscheinungsort nicht ermittelbar] : O'Reilly Media, Inc. | Boston, MA : Safari
    Language: English
    Pages: 1 online resource (497 pages)
    Edition: 1st edition
    Keywords: Electronic books ; local
    Abstract: Time series data analysis is increasingly important due to the massive production of such data through the internet of things, the digitalization of healthcare, and the rise of smart cities. As continuous monitoring and data collection become more common, the need for competent time series analysis with both statistical and machine learning techniques will increase. Covering innovations in time series data analysis and use cases from the real world, this practical guide will help you solve the most common data engineering and analysis challengesin time series, using both traditional statistical and modern machine learning techniques. Author Aileen Nielsen offers an accessible, well-rounded introduction to time series in both R and Python that will have data scientists, software engineers, and researchers up and running quickly. You’ll get the guidance you need to confidently: Find and wrangle time series data Undertake exploratory time series data analysis Store temporal data Simulate time series data Generate and select features for a time series Measure error Forecast and classify time series with machine or deep learning Evaluate accuracy and performance
    Note: Online resource; Title from title page (viewed October 25, 2019)
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  • 3
    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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  • 4
    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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  • 5
    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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  • 6
    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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  • 7
    Language: English
    Pages: 1 online resource (1 streaming video file (2 hr., 9 min., 23 sec.)) , digital, sound, color
    Keywords: Medical care ; Data processing ; Machine learning ; Artificial intelligence ; Electronic videos ; local
    Abstract: "Artificial intelligence (AI) and machine learning (ML) in healthcare comprise a rapidly expanding and very promising field. The marriage of technology and health has the potential to empower medical professionals and patients alike, while drastically cutting the cost of healthcare. Using a measured no-hype approach, this video will help technology entrepreneurs, product managers, and health care executives understand what can be done with AI and ML in healthcare today and what concepts are most crucial to producing valuable applications in the near future."--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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  • 8
    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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  • 9
    Online Resource
    Online Resource
    [Erscheinungsort nicht ermittelbar] : O'Reilly Media, Inc.
    ISBN: 9781098189693 , 1098189698
    Language: Undetermined
    Pages: 1 online resource
    Note: Machine-generated record
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  • 10
    Online Resource
    Online Resource
    [Erscheinungsort nicht ermittelbar] : O'Reilly Media, Inc.
    ISBN: 9798341610125
    Language: Undetermined
    Pages: 1 online resource
    Note: Machine-generated record
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