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Evaluation of Some Online Banks, E-Wallets and Visa/Master Cards Issuers

為了解決科學計算機online的問題，作者Alassouli, Hidaia Mahmood 這樣論述：

ARTIFICIAL INTELLIGENCE: A MODERN APPROACH 4/E (GE)

為了解決科學計算機online的問題，作者RUSSELL,NORVIG 這樣論述：

　　The most comprehensive, up-to-date introduction to the theory and practice of artificial intelligence 　　The long-anticipated revision of Artificial Intelligence: A Modern Approach explores the full breadth and depth of the field of artificial intelligence (AI). The 4th Edition brings re

aders up to date on the latest technologies, presents concepts in a more unified manner, and offers new or expanded coverage of machine learning, deep learning, transfer learning, multiagent systems, robotics, natural language processing, causality, probabilistic programming, privacy, fairness, and

safe AI. 本書特色 　　Offer the most comprehensive, up-to-date introduction to the theory and practice of artificial intelligence 　　‧ Nontechnical learning material introduces major concepts using intuitive explanations, before going into mathematical or algorithmic details. The nontechnical language m

akes the book accessible to a broader range of readers. 　　‧ A unified approach to AI shows students how the various subfields of AI fit together to build actual, useful programs. 　　‧ UPDATED - The basic definition of AI systems is generalized to eliminate the standard assumption that the objective

is fixed and known by the intelligent agent; instead, the agent may be uncertain about the true objectives of the human(s) on whose behalf it operates. 　　‧ In-depth coverage of both basic and advanced topics provides students with a basic understanding of the frontiers of AI without compromising c

omplexity and depth. 　　‧ The Author-Maintained Website at http://aima.cs.berkeley.edu/ includes text-related comments and discussions, exercises, an online code repository, Instructor Resources, and more! 　　‧ UPDATED - Interactive student exercises are now featured on the website to allow for cont

inuous updating and additions. 　　‧ UPDATED - Online software gives students more opportunities to complete projects, including implementations of the algorithms in the book, plus supplemental coding examples and applications in Python, Java, and Javascript. 　　‧ NEW - Instructional video tutorials

deepen students’ engagement and bring key concepts to life. 　　‧ A flexible format makes the text adaptable for varying instructors' preferences. 　　Stay current with the latest technologies and present concepts in a more unified manner 　　‧ NEW - New chapters feature expanded coverage of probabilis

tic programming (Ch. 15); multiagent decision making (Ch. 18 with Michael Wooldridge); deep learning (Ch. 21 with Ian Goodfellow); and deep learning for natural language processing (Ch. 24 with Jacob Devlin and Mei-Wing Chang). 　　‧ UPDATED - Increased coverage of machine learning. 　　‧ UPDATED - Si

gnificantly updated material on robotics includes robots that interact with humans and the application of reinforcement learning to robotics. 　　‧ NEW - New section on causality by Judea Pearl. 　　‧ NEW - New sections on Monte Carlo search for games and robotics. 　　‧ NEW - New sections on transfer

learning for deep learning in general and for natural language. 　　‧ NEW - New sections on privacy, fairness, the future of work, and safe AI. 　　‧ NEW - Extensive coverage of recent advances in AI applications. 　　‧ UPDATED - Revised coverage of computer vision, natural language understanding, and

speech recognition reflect the impact of deep learning methods on these fields.