Explainable Artificial Intelligence: An Introduction to Xai
暫譯: 可解釋的人工智慧：XAI 入門指南

Kamath, Uday, Liu, John

Explainable Artificial Intelligence: An Introduction to Xai
  • 出版商: Springer
  • 出版日期: 2021-12-16
  • 售價: $5,660
  • 貴賓價: 9.5 折 $5,377
  • 語言: 英文
  • 頁數: 336
  • 裝訂: Hardcover - also called cloth, retail trade, or trade
  • ISBN: 3030833550
  • ISBN-13: 9783030833558
  • 相關分類: 人工智慧

    • 海外代購書籍(需單獨結帳)

相關主題

商品描述

1. Introduction to Interpretability and Explainability.- 2. Pre-Model Interpretability and Explainability.- 3. Model Visualization Techniques and Traditional Interpretable Algorithms.- 4. Model Interpretability: Advances in Interpretable Machine Learning.- 5. Post-hoc Interpretability and Explanations.- 6. Explainable Deep Learning.- 7. Explainability in Time Series Forecasting, Natural Language Processing, and Computer Vision.- 8. XAI: Challenges and Future.

商品描述(中文翻譯)

1. 可解釋性與解釋性的介紹
2. 模型前的可解釋性與解釋性
3. 模型可視化技術與傳統可解釋算法
4. 模型可解釋性：可解釋機器學習的進展
5. 事後可解釋性與解釋
6. 可解釋的深度學習
7. 時間序列預測、自然語言處理與計算機視覺中的解釋性
8. XAI：挑戰與未來

作者簡介

Uday Kamath has spent more than two decades developing analytics products in statistics, optimization, machine learning, NLP and speech recognition, and explainable AI. Uday has a Ph.D. in scalable machine learning and has contributed to many journals, conferences, and books in the field of AI. He is the author of books such as Deep Learning for NLP and Speech Recognition, Mastering Java Machine Learning, and Machine Learning: End-to-End Guide for Java Developers. He held many senior roles: Chief Analytics Officer for Digital Reasoning, Advisor for Falkonry, and Chief Data Scientist for BAE Systems Applied Intelligence. He has built products and solutions using AI in surveillance, compliance, cybersecurity, financial crime, anti-money laundering, and insurance fraud. Uday currently works as the Chief Analytics Officer for Smarsh. He is responsible for Data Science, research of analytics products employing deep learning and explainable AI, and modern techniques in speech and text used in the financial domain and healthcare.
John Chih Liu, PhD, CFA is Chief Executive Officer of Intelluron Corporation. Previously, he held senior executive roles overseeing quantitative research, portfolio management and data science organizations, including as VP of Data Science, Applied Machine Learning at Digital Reasoning Systems, MD of Equity Strategies at the Vanderbilt University endowment, and Head of Index Options Trading at BNP Paribas. He is a frequent speaker and published author on topics including natural language processing, reinforcement learning, asset allocation, systemic risk and EM theory. John was named Nashville's Data Scientist of the Year in 2016, Finalist for Community Leader of the Year in 2018, and Finalist for Innovator of the Year in 2020. He earned his B.S., M.S., and Ph.D. in electrical engineering from the University of Pennsylvania and is a CFA Charterholder, advocate for the global data science community and supporter of the International Science and Engineering Fair.

作者簡介(中文翻譯)

烏代·卡馬斯在統計學、優化、機器學習、自然語言處理（NLP）和語音識別以及可解釋的人工智慧（AI）領域擁有超過二十年的分析產品開發經驗。烏代擁有可擴展機器學習的博士學位，並在人工智慧領域的多本期刊、會議和書籍中做出了貢獻。他是《深度學習於自然語言處理與語音識別》（Deep Learning for NLP and Speech Recognition）、《精通Java機器學習》（Mastering Java Machine Learning）和《機器學習：Java開發者的端到端指南》（Machine Learning: End-to-End Guide for Java Developers）等書籍的作者。他曾擔任多個高級職位：數位推理公司的首席分析官、Falkonry的顧問，以及BAE系統應用智慧的首席數據科學家。他利用人工智慧在監控、合規性、網絡安全、金融犯罪、反洗錢和保險詐騙等領域構建產品和解決方案。烏代目前擔任Smarsh的首席分析官，負責數據科學、使用深度學習和可解釋的人工智慧的分析產品研究，以及在金融領域和醫療保健中使用的現代語音和文本技術。
劉志誠博士，CFA是Intelluron Corporation的首席執行官。此前，他擔任高級執行職位，負責量化研究、投資組合管理和數據科學組織，包括數位推理系統的應用機器學習數據科學副總裁、范德堡大學基金的股票策略董事總經理，以及法國巴黎銀行的指數期權交易主管。他經常發表演講並出版有關自然語言處理、強化學習、資產配置、系統性風險和新興市場理論等主題的文章。劉博士在2016年被評選為納什維爾年度數據科學家，2018年成為年度社區領袖的決賽入圍者，2020年則是年度創新者的決賽入圍者。他在賓夕法尼亞大學獲得電機工程的學士、碩士和博士學位，並且是CFA特許持有人，全球數據科學社群的倡導者，以及國際科學與工程博覽會的支持者。

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