面向高風險應用的機器學習 (影印版) (英文) 9787576612912 帕特里克.霍爾 詹姆士.柯蒂斯 帕蒂爾.帕迪
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物品所在地：中國大陸
原出版社：東南大學
物品所在地：中國大陸
原出版社：東南大學
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| *完成訂單後正常情形下約兩周可抵台。 *本賣場提供之資訊僅供參考，以到貨標的為正確資訊。 印行年月：202403*若逾兩年請先於私訊洽詢存貨情況，謝謝。 台灣(台北市)在地出版社，每筆交易均開具統一發票，祝您中獎最高1000萬元。 書名：面向高風險應用的機器學習 (影印版) (英文) ISBN：9787576612912 出版社：東南大學 著編譯者：帕特里克.霍爾 詹姆士.柯蒂斯 帕蒂爾.帕迪 頁數：438 所在地：中國大陸 *此為代購商品 書號：1638102 可大量預訂，請先連絡。 【台灣高等教育出版社簡體書】 面向高風險應用的機器學習 (影印版) (英文) 787576612912 帕特里克.霍爾 詹姆士.柯蒂斯 帕蒂爾.帕迪 內容簡介 過去十年人們見證了人工智慧和機器學習（AI/ML）技術的廣泛應用。然而，由於在廣泛實施過程中缺乏監督，導致了一些本可以通過適當的風險管理來避免的事故和有害後果。在我們認識到AI/ML的真正好處之前，從業者必須了解如何降低其風險。 本書描述了負責任的AI方法，這是一種以風險管理、網路安全、數據隱私、應用社會科學方面的最佳實踐為基礎，用於改進AI/ML技術、業務流程、文化能力的綜合性框架。作者Patrick Hall、James Curtis、Parul Pandey為那些希望幫助組織、消費者和公眾改善實際AI/ML系統成果的數據科學家創作了這本指南。目錄 ForewordPreface Part Ⅰ Theories and Practical Applications of AI Risk Management 1 Contemporary Machine Learning Risk Management A Snapshot of the Legal and Regulatory Landscape The Proposed EU AI Act US Federal Laws and Regulations State and Municipal Laws Basic Product Liability Federal Trade Commission Enforcement Authoritative Best Practices AI Incidents Cultural Competencies for Machine Learning Risk Management Organizational Accountability Culture of Effective Challenge Diverse and Experienced Teams Drinking Our Own Champagne Moving Fast and Breaking Things Organizational Processes for Machine Learning Risk Management Forecasting Failure Modes Model Risk Management Processes Beyond Model Risk Management Case Study: The Rise and Fall of Zillow's iBuying ~ Fallout Lessons Learned Resources 2 Interpretable and Explainable Machine Learning Important Ideas for Interpretability and Explainability Explainable Models Additive Models Decision Trees An Ecosystem of Explainable Machine Learning Models Post Hoc Explanation Feature Attribution and Importance Surrogate Models Plots of Model Performance Cluster Profiling Stubborn Difficulties of Post Hoc Explanation in Practice Pairing Explainable Models and Post Hoc Explanation Case Study: Graded by Algorithm Resources 3 Debugging Machine Learning Systems for Safety and Performance Training Reproducibility Data Quality Model Specification for Real-World Outcomes Model Debugging Software Testing Traditional Model Assessment Common Machine Learning Bugs Residual Analysis Sensitivity Analysis Benchmark Models Remediation: Fixing Bugs Deployment Domain Safety Model Monitoring Case Study: Death by Autonomous Vehicle Fallout An Unprepared Legal System Lessons Learned Resources 4 Managing Bias in Machine Learning 5 Security for Machine Learning Part Ⅱ Putting AI Risk Management into Action 6 Explainable Boosting Machines and Explaining XGBoost 7 Explaining a PyTorch Image Classifier 8 Selecting and Debugging XGBoost Models 9 Debugging a PyTorch Image Classifier 10 Testing and Remediating Bias with XGBoost 11 Red-Teaming XGBoost Part Ⅲ Conclusion 12 How to Succeed in High-Risk Machine Learning 詳細資料或其他書籍請至台灣高等教育出版社查詢，查後請於PChome商店街私訊告知ISBN或書號，我們即儘速上架。 |
