Applied Machine Learning Explainability Techniques: Make ML models explainable and trustworthy for practical applications using LIME, SHAP, and more
暫譯: 應用機器學習可解釋性技術:使用 LIME、SHAP 等方法使機器學習模型可解釋且值得信賴的實用應用

Bhattacharya, Aditya

  • 出版商: Packt Publishing
  • 出版日期: 2022-07-29
  • 售價: $1,600
  • 貴賓價: 9.5$1,520
  • 語言: 英文
  • 頁數: 304
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 1803246154
  • ISBN-13: 9781803246154
  • 相關分類: Machine Learning
  • 立即出貨 (庫存=1)

商品描述

Leverage top XAI frameworks to explain your machine learning models with ease and discover best practices and guidelines to build scalable explainable ML systems


Key Features:

  • Explore various explainability methods for designing robust and scalable explainable ML systems
  • Use XAI frameworks such as LIME and SHAP to make ML models explainable to solve practical problems
  • Design user-centric explainable ML systems using guidelines provided for industrial applications


Book Description:

Explainable AI (XAI) is an emerging field that brings artificial intelligence (AI) closer to non-technical end users. XAI makes machine learning (ML) models transparent and trustworthy along with promoting AI adoption for industrial and research use cases.

Applied Machine Learning Explainability Techniques comes with a unique blend of industrial and academic research perspectives to help you acquire practical XAI skills. You'll begin by gaining a conceptual understanding of XAI and why it's so important in AI. Next, you'll get the practical experience needed to utilize XAI in AI/ML problem-solving processes using state-of-the-art methods and frameworks. Finally, you'll get the essential guidelines needed to take your XAI journey to the next level and bridge the existing gaps between AI and end users.

By the end of this ML book, you'll be equipped with best practices in the AI/ML life cycle and will be able to implement XAI methods and approaches using Python to solve industrial problems, successfully addressing key pain points encountered.


What You Will Learn:

  • Explore various explanation methods and their evaluation criteria
  • Learn model explanation methods for structured and unstructured data
  • Apply data-centric XAI for practical problem-solving
  • Hands-on exposure to LIME, SHAP, TCAV, DALEX, ALIBI, DiCE, and others
  • Discover industrial best practices for explainable ML systems
  • Use user-centric XAI to bring AI closer to non-technical end users
  • Address open challenges in XAI using the recommended guidelines


Who this book is for:

This book is for scientists, researchers, engineers, architects, and managers who are actively engaged in machine learning and related fields. Anyone who is interested in problem-solving using AI will benefit from this book. Foundational knowledge of Python, ML, DL, and data science is recommended. AI/ML experts working with data science, ML, DL, and AI will be able to put their knowledge to work with this practical guide. This book is ideal for you if you're a data and AI scientist, AI/ML engineer, AI/ML product manager, AI product owner, AI/ML researcher, and UX and HCI researcher.

商品描述(中文翻譯)

利用頂尖的可解釋人工智慧(XAI)框架輕鬆解釋您的機器學習模型,並發現構建可擴展的可解釋機器學習系統的最佳實踐和指導方針

主要特點:


  • 探索各種可解釋性方法,以設計穩健且可擴展的可解釋機器學習系統

  • 使用 LIME 和 SHAP 等 XAI 框架,使機器學習模型可解釋,以解決實際問題

  • 根據工業應用提供的指導方針設計以用戶為中心的可解釋機器學習系統

書籍描述:
可解釋人工智慧(XAI)是一個新興領域,旨在使人工智慧(AI)更接近非技術性最終用戶。XAI 使機器學習(ML)模型變得透明且值得信賴,並促進 AI 在工業和研究用例中的採用。

《應用機器學習可解釋性技術》結合了工業和學術研究的獨特視角,幫助您獲得實用的 XAI 技能。您將首先獲得對 XAI 的概念理解,以及它在 AI 中的重要性。接下來,您將獲得利用 XAI 解決 AI/ML 問題過程所需的實踐經驗,使用最先進的方法和框架。最後,您將獲得提升 XAI 旅程所需的基本指導方針,並彌合 AI 與最終用戶之間的現有差距。

在這本機器學習書籍結束時,您將掌握 AI/ML 生命週期中的最佳實踐,並能夠使用 Python 實施 XAI 方法和方法來解決工業問題,成功應對遇到的關鍵痛點。

您將學到什麼:


  • 探索各種解釋方法及其評估標準

  • 學習結構化和非結構化數據的模型解釋方法

  • 應用以數據為中心的 XAI 進行實際問題解決

  • 實際接觸 LIME、SHAP、TCAV、DALEX、ALIBI、DiCE 等工具

  • 發現可解釋機器學習系統的工業最佳實踐

  • 使用以用戶為中心的 XAI 使 AI 更接近非技術性最終用戶

  • 使用推薦的指導方針解決 XAI 中的開放挑戰

本書適合誰:
本書適合積極從事機器學習及相關領域的科學家、研究人員、工程師、架構師和管理者。任何對使用 AI 解決問題感興趣的人都將受益於本書。建議具備 Python、機器學習(ML)、深度學習(DL)和數據科學的基礎知識。從事數據科學、機器學習、深度學習和 AI 的 AI/ML 專家將能夠利用這本實用指南發揮他們的知識。如果您是數據和 AI 科學家、AI/ML 工程師、AI/ML 產品經理、AI 產品負責人、AI/ML 研究人員,以及 UX 和 HCI 研究人員,本書將非常適合您。