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Machine Learning for High-Risk Applications : Approaches to Responsible AI by Parul Pandey, James Curtis and Patrick Hall (2023, Trade Paperback)

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Estimated delivery Thu, 15 May - Wed, 21 May
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About this product

Product Identifiers

PublisherO'reilly Media, Incorporated
ISBN-101098102436
ISBN-139781098102432
eBay Product ID (ePID)25057238456

Product Key Features

Number of Pages466 Pages
LanguageEnglish
Publication NameMachine Learning for High-Risk Applications : Approaches to Responsible Ai
SubjectSoftware Development & Engineering / General, Intelligence (Ai) & Semantics, Security / Online Safety & Privacy, Computer Science, Data Processing, Software Development & Engineering / Systems Analysis & Design, Social Aspects / Human-Computer Interaction
Publication Year2023
TypeTextbook
AuthorParul Pandey, James Curtis, Patrick Hall
Subject AreaComputers
FormatTrade Paperback

Dimensions

Item Height1.2 in
Item Weight28.4 Oz
Item Length9.2 in
Item Width7 in

Additional Product Features

LCCN2023-280039
Dewey Edition23
IllustratedYes
Dewey Decimal006.31
SynopsisThe past decade has witnessed the broad adoption of artificial intelligence and machine learning (AI/ML) technologies. However, a lack of oversight in their widespread implementation has resulted in some incidents and harmful outcomes that could have been avoided with proper risk management. Before we can realize AI/ML's true benefit, practitioners must understand how to mitigate its risks. This book describes approaches to responsible AI--a holistic framework for improving AI/ML technology, business processes, and cultural competencies that builds on best practices in risk management, cybersecurity, data privacy, and applied social science. Authors Patrick Hall, James Curtis, and Parul Pandey created this guide for data scientists who want to improve real-world AI/ML system outcomes for organizations, consumers, and the public. Learn technical approaches for responsible AI across explainability, model validation and debugging, bias management, data privacy, and ML security Learn how to create a successful and impactful AI risk management practice Get a basic guide to existing standards, laws, and assessments for adopting AI technologies, including the new NIST AI Risk Management Framework Engage with interactive resources on GitHub and Colab
LC Classification NumberQ325.5.H3 2023

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Patrick Hall James Curtis Parul Machine Learning for High-Risk Appli (Paperback)
item 1 Patrick Hall James Curtis Parul Machine Learning for High-Risk Appli (Paperback)
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Machine Learning for High-Risk Applications: Techniques for Responsible AI
item 2 Machine Learning for High-Risk Applications: Techniques for Responsible AI
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Machine Learning for High-Risk Applications: Approaches to Responsible AI by Pat
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Patrick Hall James Curtis Machine Learning for High-Risk (Paperback) (UK IMPORT)
item 10 Patrick Hall James Curtis Machine Learning for High-Risk (Paperback) (UK IMPORT)
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+S$ 5.70 postage
Patrick Hall James Curtis Machine Learning for High-Risk (Paperback) (UK IMPORT)
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