e more 計算機的問題，透過圖書和論文來找解法和答案更準確安心。 我們找到下列問答集和資訊懶人包

Introduction to Algorithms, Fourth Edition

為了解決e more 計算機的問題，作者Cormen, Thomas H.,Leiserson, Charles E.,Rivest, Ronald L. 這樣論述：

A comprehensive update of the leading algorithms text, with new material on matchings in bipartite graphs, online algorithms, machine learning, and other topics.Some books on algorithms are rigorous but incomplete; others cover masses of material but lack rigor. Introduction to Algorithms uniquel

y combines rigor and comprehensiveness. It covers a broad range of algorithms in depth, yet makes their design and analysis accessible to all levels of readers, with self-contained chapters and algorithms in pseudocode. Since the publication of the first edition, Introduction to Algorithms has becom

e the leading algorithms text in universities worldwide as well as the standard reference for professionals. This fourth edition has been updated throughout. New for the fourth edition New chapters on matchings in bipartite graphs, online algorithms, and machine learningNew material on topics includ

ing solving recurrence equations, hash tables, potential functions, and suffix arrays140 new exercises and 22 new problemsReader feedback-informed improvements to old problemsClearer, more personal, and gender-neutral writing styleColor added to improve visual presentationNotes, bibliography, and in

dex updated to reflect developments in the fieldWebsite with new supplementary material

e more 計算機進入發燒排行的影片

探討認知師徒制融入數位學習之學習成效及自主學習行為-以醫放系實習生學習上腹部超音波病灶辨認為例

為了解決e more 計算機的問題，作者蘇于珊 這樣論述：

近幾年，受到疫情的影響使得數位學習在教學領域上的應用愈來愈普遍，數位學習運用在醫學領域相關課程的學門逐漸受到重視。醫院放射科的超音波技術非常重視實作經驗及影像辨認，一向使用師徒制的方式來進行教學，每位實習生所遇到的病灶量與質有差異，且學習過程缺少了反思和探索。因此本研究運用融入認知師徒制之數位學習來進行上腹部超音波病灶之教學，以到醫院實習的醫放系22位實習生為研究對象，希望能藉此提升實習生辨認超音波病灶的學習成效、並探討其學習滿意度及自主學習行為。結果發現運用數位學習上腹部超音波的方式確實能夠提升實習生辨認超音波病灶的學習成效，且整體學習滿意度頗佳，自主學習能力也有提升學習滿意度及自主學習之

間具有顯著相關，且學生的自主學習能力與專題報告也呈現顯著正相關。建議臨床教師推動數位學習融入超音波實習課程，可採用同步線上課程和非同步線上課程的搭配方式及利用線上討論和通訊軟體提供互動活動，未來研究可融入自主學習策略於教學探討對學生自主學習行為和能力的幫助。

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

為了解決e more 計算機的問題，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.  

三流門控自適應圖卷積用於骨架動作數據識別

為了解決e more 計算機的問題，作者林志強 這樣論述：

近年來隨著計算機視覺技術的高速發展，人體動作識別作為其中一個重要的方向吸引了越來越多學者的興趣，得到了廣泛的研究。人體動作識別在人機交互，機器人視覺等方面都有廣泛的應用。但由於場景中存在光照、物體、顏色等複雜的變化以及障礙物的遮擋、背景的噪音等會對動作識別造成巨大的影響。而基於骨骼的動作識別具有強適應性，並且資料更加的簡潔。因此在基於骨架的動作識別上還有許多發展以及改善的空間。近幾年圖卷積神經網路在許多應用中得到了成功的應用，並且成功應用於骨骼的動作識別當中。圖卷積神經網路是一種能對圖數據進行深度學習的方法，其原理為將卷積從一幅圖像推廣到另一幅圖像，其中圖（Graph）結構是一種非線性的數據

結構。因此本文根據已提出的雙流自我調整性圖卷積模型進行改善。本文提出的改善為兩個部分，首先，對於某些動作對於順序資訊的強烈依賴性文中並沒有應用，因此在原有的雙流（骨骼流以及關節流）基礎上，加入整體的運動流來補充時間域的資訊。其次，在原有的雙流網路當中，具有兩種類型的圖，分別為全域圖以及局部圖，兩種類型的圖都針對不同的層進行了單獨的優化。基於每個模型層中所需兩種圖的重要性並不一致，本文中使用門控機制將這兩種圖形融合在一起。當模型使用三流門控自適應圖卷積時，在X-View模式下，三流結合作為輸入數據時，與雙流相比正確率提升了0.19%。在X-Subject模式下，三流結合作為輸入數據時，與雙流相比

正確率提升了0.97%。依據實驗結果顯示得出，利用三流門控的方式可以得出較好的結果，有效的改善辨識的錯誤率。