Explainable AI Recipes: Implement Solutions to Model Explainability and Interpretability with Python
暫譯: 可解釋的人工智慧食譜：使用 Python 實現模型可解釋性和可解釋性的解決方案
Mishra, Pradeepta
相關主題
商品描述
Understand how to use Explainable AI (XAI) libraries and build trust in AI and machine learning models. This book utilizes a problem-solution approach to explaining machine learning models and their algorithms.
The book starts with model interpretation for supervised learning linear models, which includes feature importance, partial dependency analysis, and influential data point analysis for both classification and regression models. Next, it explains supervised learning using non-linear models and state-of-the-art frameworks such as SHAP values/scores and LIME for local interpretation. Explainability for time series models is covered using LIME and SHAP, as are natural language processing-related tasks such as text classification, and sentiment analysis with ELI5, and ALIBI. The book concludes with complex model classification and regression-like neural networks and deep learning models using the CAPTUM framework that shows feature attribution, neuron attribution, and activation attribution.
After reading this book, you will understand AI and machine learning models and be able to put that knowledge into practice to bring more accuracy and transparency to your analyses.
What You Will Learn
- Create code snippets and explain machine learning models using Python
- Leverage deep learning models using the latest code with agile implementations
- Build, train, and explain neural network models designed to scale
- Understand the different variants of neural network models
Who This Book Is For
AI engineers, data scientists, and software developers interested in XAI
商品描述(中文翻譯)
了解如何使用可解釋的人工智慧（Explainable AI, XAI）庫，並建立對人工智慧和機器學習模型的信任。本書採用問題解決的方法來解釋機器學習模型及其演算法。
本書首先介紹監督學習的線性模型的模型解釋，包括特徵重要性、部分依賴分析以及對於分類和回歸模型的影響數據點分析。接下來，解釋使用非線性模型的監督學習，以及使用 SHAP 值/分數和 LIME 等最先進框架進行局部解釋。時間序列模型的可解釋性使用 LIME 和 SHAP 進行說明，自然語言處理相關任務如文本分類和情感分析則使用 ELI5 和 ALIBI。本書最後介紹複雜模型的分類和類似回歸的神經網絡及深度學習模型，使用 CAPTUM 框架顯示特徵歸因、神經元歸因和激活歸因。
閱讀完本書後，您將理解人工智慧和機器學習模型，並能將這些知識付諸實踐，以提高分析的準確性和透明度。
您將學到什麼
- 使用 Python 創建代碼片段並解釋機器學習模型
- 利用最新代碼和敏捷實現來使用深度學習模型
- 構建、訓練並解釋設計為可擴展的神經網絡模型
- 理解不同變體的神經網絡模型
本書適合誰閱讀
對可解釋的人工智慧（XAI）感興趣的人工智慧工程師、數據科學家和軟體開發人員
作者簡介
Pradeepta Mishra is the Director of AI, Fosfor at L&T Infotech (LTI). He leads a large group of data scientists, computational linguistics experts, and machine learning and deep learning experts in building the next-generation product--Leni--which is the world's first virtual data scientist. He has expertise across core branches of artificial intelligence, including autonomous ML and deep learning pipelines, ML ops, image processing, audio processing, natural language processing (NLP), natural language generation (NLG), design and implementation of expert systems, and personal digital assistants (PDAs). In 2019 and 2020, he was named one of "India's Top 40 Under 40 Data Scientists" by Analytics India magazine. Two of his books have been translated into Chinese and Spanish, based on popular demand.
Pradeepa delivered a keynote session at the Global Data Science Conference 2018, USA. He delivered a TEDx talk on "Can Machines Think?", available on the official TEDx YouTube channel. He has mentored more than 2,000 data scientists globally. He has delivered 200+ tech talks on data science, ML, DL, NLP, and AI at various universities, meetups, technical institutions, and community-arranged forums. He is a visiting faculty member to more than 10 universities, where he teaches deep learning and machine learning to professionals, and mentors them in pursuing a rewarding career in artificial intelligence.
作者簡介(中文翻譯)
Pradeepta Mishra 是 L&T Infotech (LTI) 的 AI 總監。他領導著一個由數據科學家、計算語言學專家以及機器學習和深度學習專家組成的大型團隊，致力於開發下一代產品——Leni——這是全球首個虛擬數據科學家。他在人工智慧的核心領域擁有專業知識，包括自主機器學習 (ML) 和深度學習管道、機器學習運營 (ML ops)、影像處理、音訊處理、自然語言處理 (NLP)、自然語言生成 (NLG)、專家系統的設計與實施，以及個人數位助理 (PDA)。在 2019 年和 2020 年，他被 Analytics India 雜誌評選為「印度 40 位以下最佳數據科學家」之一。他的兩本書因應廣泛需求已被翻譯成中文和西班牙文。
Pradeepa 在 2018 年美國全球數據科學大會上發表了主題演講。他在 TEDx 演講中探討了「機器能思考嗎？」的主題，該演講可在官方 TEDx YouTube 頻道上觀看。他已在全球指導了超過 2,000 名數據科學家。他在各大學、聚會、技術機構和社區安排的論壇上發表了 200 多場有關數據科學、機器學習、深度學習、自然語言處理和人工智慧的技術演講。他是超過 10 所大學的客座教授，教授深度學習和機器學習，並指導專業人士追求在人工智慧領域的成功職業生涯。
