Sách Machine Learning for High-Risk Applications Approaches to Responsible AI

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Sách Machine Learning for High-Risk Applications Approaches to Responsible AI

Sách Machine Learning for High-Risk Applications Approaches to Responsible AI

Sách Machine Learning for High-Risk Applications Approaches to Responsible AI

Sách Machine Learning for High-Risk Applications Approaches to Responsible AI

Sách Machine Learning for High-Risk Applications Approaches to Responsible AI

Sách Machine Learning for High-Risk Applications Approaches to Responsible AI

Sách Machine Learning for High-Risk Applications Approaches to Responsible AI

Sách Machine Learning for High-Risk Applications Approaches to Responsible AI

Sách Machine Learning for High-Risk Applications Approaches to Responsible AI

Sách Machine Learning for High-Risk Applications Approaches to Responsible AI

Sách Machine Learning for High-Risk Applications Approaches to Responsible AI

Sách Machine Learning for High-Risk Applications Approaches to Responsible AI

Sách Machine Learning for High-Risk Applications Approaches to Responsible AI

Sách Machine Learning for High-Risk Applications Approaches to Responsible AI

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Sách Machine Learning for High-Risk Applications Approaches to Responsible AI

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Danh mục: Sách ngoại ngữ, Sách IT, Sách chuyên ngành, Sách Tiếng Anh quyển Lẻ
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Mô tả

Sách Machine Learning for High-Risk Applications Approaches to Responsible AI

Sách Machine Learning for High-Risk Applications Approaches to Responsible AI (sách keo gáy, bìa mềm)

Categories:Computers - Artificial Intelligence (AI)

Year:2023

Edition:1st

Language:english

Pages:469

The 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

https://zalo.me/0969436832

0969436832