91精品国产综合久久四虎久久_国产成人午夜高潮毛片_99er视频精品免费观看_2020亚洲熟女在线观看_日本女优人体写真_国内黄色毛片_年轻的老师中文版在线_丰满女邻居做爰_久久久久久精品成人免费图片

 
面向高風(fēng)險應(yīng)用的機器學(xué)習(xí)（影印版）
面向高風(fēng)險應(yīng)用的機器學(xué)習(xí)（影印版）
Patrick Hall, James Curtis, Parul Pandey 著
出版時間：2024年03月
頁數(shù)：468
“作者出色地概述了監(jiān)管、風(fēng)險管理、可解釋性以及其他許多主題，同時提供了實用建議和代碼示例?！?br /> ——Christoph Molnar
Interpretable Machine Learning的作者

“這本著作以其獨特的戰(zhàn)術(shù)方法來解決ML中的系統(tǒng)風(fēng)險而脫穎而出。通過采取細(xì)致入微的方法來降低ML風(fēng)險，本書為讀者提供了寶貴的資源，幫助他們以負(fù)責(zé)任和可持續(xù)的方式成功部署ML系統(tǒng)。”
——Liz Grennan
麥肯錫公司副合伙人兼數(shù)字信托全球聯(lián)合主管

過去十年見證了人工智能和機器學(xué)習(xí)（AI/ML）技術(shù)的廣泛應(yīng)用。然而，由于在廣泛實施過程中缺乏監(jiān)督，導(dǎo)致了一些本可以通過適當(dāng)?shù)娘L(fēng)險管理來避免的事故和有害后果。在我們認(rèn)識到AI/ML的真正好處之前，從業(yè)者必須了解如何降低其風(fēng)險。
本書描述了負(fù)責(zé)任的AI方法，這是一種以風(fēng)險管理、網(wǎng)絡(luò)安全、數(shù)據(jù)隱私、應(yīng)用社會科學(xué)方面的最佳實踐為基礎(chǔ)，用于改進(jìn)AI/ML技術(shù)、業(yè)務(wù)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(tǒng)成果的數(shù)據(jù)科學(xué)家創(chuàng)作了這本指南。
● 學(xué)習(xí)負(fù)責(zé)任的AI技術(shù)方法，包括可解釋性、模型驗證和調(diào)試、偏差管理、數(shù)據(jù)隱私、ML安全
● 學(xué)習(xí)如何創(chuàng)建一套成功且有影響力的AI風(fēng)險管理實踐
● 獲得關(guān)于采用AI技術(shù)的現(xiàn)有標(biāo)準(zhǔn)、法律、評估的基本指南，包括新的NIST AI風(fēng)險管理框架
● 參與GitHub和Colab上的互動資源
  1. Foreword
  2. Preface
  3. Part I. Theories and Practical Applications of AI Risk Management
  4. 1. Contemporary Machine Learning Risk Management
  5. A Snapshot of the Legal and Regulatory Landscape
  6. Authoritative Best Practices
  7. AI Incidents
  8. Cultural Competencies for Machine Learning Risk Management
  9. Organizational Processes for Machine Learning Risk Management
  10. Case Study: The Rise and Fall of Zillow’s iBuying
  11. Resources
  12. 2. Interpretable and Explainable Machine Learning
  13. Important Ideas for Interpretability and Explainability
  14. Explainable Models
  15. Post Hoc Explanation
  16. Stubborn Difficulties of Post Hoc Explanation in Practice
  17. Pairing Explainable Models and Post Hoc Explanation
  18. Case Study: Graded by Algorithm
  19. Resources
  20. 3. Debugging Machine Learning Systems for Safety and Performance
  21. Training
  22. Model Debugging
  23. Deployment
  24. Case Study: Death by Autonomous Vehicle
  25. Resources
  26. 4. Managing Bias in Machine Learning
  27. ISO and NIST Definitions for Bias
  28. Legal Notions of ML Bias in the United States
  29. Who Tends to Experience Bias from ML Systems
  30. Harms That People Experience
  31. Testing for Bias
  32. Mitigating Bias
  33. Case Study: The Bias Bug Bounty
  34. Resources
  35. 5. Security for Machine Learning
  36. Security Basics
  37. Machine Learning Attacks
  38. General ML Security Concerns
  39. Countermeasures
  40. Case Study: Real-World Evasion Attacks
  41. Resources
  42. Part II. Putting AI Risk Management into Action
  43. 6. Explainable Boosting Machines and Explaining XGBoost
  44. Concept Refresher: Machine Learning Transparency
  45. The GAM Family of Explainable Models
  46. XGBoost with Constraints and Post Hoc Explanation
  47. Resources
  48. 7. Explaining a PyTorch Image Classifier
  49. Explaining Chest X-Ray Classification
  50. Concept Refresher: Explainable Models
  51. Explainable Models
  52. Training and Explaining a PyTorch Image Classifier
  53. Conclusion
  54. Resources
  55. 8. Selecting and Debugging XGBoost Models
  56. Concept Refresher: Debugging ML
  57. Selecting a Better XGBoost Model
  58. Sensitivity Analysis for XGBoost
  59. Residual Analysis for XGBoost
  60. Remediating the Selected Model
  61. Conclusion
  62. Resources
  63. 9. Debugging a PyTorch Image Classifier
  64. Concept Refresher: Debugging Deep Learning
  65. Debugging a PyTorch Image Classifier
  66. Conclusion
  67. Resources
  68. 10. Testing and Remediating Bias with XGBoost
  69. Concept Refresher: Managing ML Bias
  70. Model Training
  71. Evaluating Models for Bias
  72. Remediating Bias
  73. Conclusion
  74. Resources
  75. 11. Red-Teaming XGBoost
  76. Concept Refresher
  77. Model Training
  78. Attacks for Red-Teaming
  79. Conclusion
  80. Resources
  81. Part III. Conclusion
  82. 12. How to Succeed in High-Risk Machine Learning
  83. Who Is in the Room?
  84. Science Versus Engineering
  85. Evaluation of Published Results and Claims
  86. Apply External Standards
  87. Commonsense Risk Mitigation
  88. Conclusion
  89. Resources
  90. Index
書名：面向高風(fēng)險應(yīng)用的機器學(xué)習(xí)（影印版）
國內(nèi)出版社：東南大學(xué)出版社
出版時間：2024年03月
頁數(shù)：468
書號：978-7-5766-1291-2
原版書書名：Machine Learning for High-Risk Applications
原版書出版商：O'Reilly Media
Patrick Hall
 
Patrick Hall是BNH.AI的首席科學(xué)家，也是華盛頓大學(xué)的客座教授。
 
 
James Curtis
 
James Curtis是Solea Energy的量化研究員。
 
 
Parul Pandey
 
Parul Pandey是H2O.ai的首席數(shù)據(jù)科學(xué)家。
 
 
The animal on the cover of Machine Learning for High-Risk Applications is the giant African fruit beetle (Mecynorrhina polyphemus).

Formerly classified under the Latin name Chelorrhina polyphemus, this large, green scarab beetle is a member of the Cetoniinae family of flower chafers, a group of brightly colored beetles that feed primarily on flower pollen, nectar, and petals, as well as fruits and tree sap. Ranging from 35 to 80 mm in length, giant African fruit beetles are the largest beetles in the genus Mecynorrhina.

These colossal scarabs are found in the dense tropical forests of Central Africa. The adults are sexually dimorphic, with the females having a shiny, prismatic carapace, and the males having antlers and a more velvety or matte coloration. As attractive and relatively easy-to-raise beetles, they make popular pets among aspiring entomologists. This fact, along with habitat destruction, has been cited by at least one study as a factor in population declines in some areas, though they remain common overall.
Many of the animals on O’Reilly covers are endangered; all of them are important to the world.
購買選項
定價：138.00元
書號：978-7-5766-1291-2
出版社：東南大學(xué)出版社