Algorithms

New York Times and USA Today BestsellerOne thing I've learned being around Gary for the last decade is that when he sees new consumer trends or new best practices in marketing, people should listen and act on them. Michael Rubin, CEO of FanaticsIn his seventh business book, bestselling author, entrepreneur, and investor Gary Vaynerchuk offers fresh, in-depth advice to enhance brand development, grow sales, and beat the competition using modern advertising strategies grounded in social media.In his 2013 bestseller Jab, Jab, Jab, Right Hook, Gary Vaynerchuk showed the world how to create winning content for underpriced attention channels.But since then, new platforms have emerged, others have become less relevant, and algorithms are incentivizing new styles of content. New skills are necessary to create advertising that builds brand and sales.In his latest book, Vaynerchuk argues that todays fast-growing businesses, brands, content creators, and influencers have one thing in common: They

Topology optimization is a digital method for designing objects in order to achieve the best structural performance, sometimes in combination with other physical requirements. Topology optimization tools use mathematical algorithms, such as the finite element method and gradient computation, to generate designs based on desired characteristics and predetermined constraints. Initially a purely academic tool, topology optimization has advanced rapidly and is increasingly being applied to the design of a wide range of products and components, from furniture to spacecraft. To explore the potential and challenges of topology optimization, the National Academies of Sciences, Engineering, and Medicine hosted a two-day workshop on November 19-20, 2019, Exploiting Advanced Manufacturing Capabilities: Topology Optimization in Design. The workshop was organized around three main topics: how topology optimization can incorporate manufacturability along with functional design; challenges and opport

Road Traffic Modelling and Management: Using Statistical Monitoring and Deep Learning provides a framework for understanding and enhancing road traffic monitoring and management. It examines both the commonly used traffic analysis methodologies as well the emerging ones that use deep learning methods. The book also shows how to understand statistical models and machine learning algorithms, and how to apply them to traffic modelling, estimation, forecasting and traffic congestion monitoring. Providing both a theoretical framework along with practical technical solutions, Road Traffic Modelling and Management: Using Statistical Monitoring and Deep Learning is for researchers and practitioners who want to improve the performance of intelligent transportation systems.

This book provides a comprehensive foundation of machine learning. To answer the questions of what to learn, how to learn, what to get from learning, and how to evaluate, as well as what is meant by learning, the book focuses on the fundamental basics of machine learning, its methodology, theory, algorithms, and evaluations, together with some philosophical thinking on comparison between machine learning and human learning for machinery intelligence.The book is organized as follows: Introduction (Chapter 1), Evaluation (Chapter 2), Supervised learning (Chapters 3, 4, and 5), Unsupervised learning (Chapter 6), Representation learning (Chapter 7), Problem decomposition (Chapter 8), Ensemble learning (Chapter 9), Deep learning (Chapter 10), Application (Chapter 11), and Challenges (Chapter 12).The book can be used as a textbook for college, undergraduate, graduate and PhD students majored in computer science, automation, electronic engineering, communication, ect. It can also be used as

本書著重論述了實值Gabor變換的理論、快速演算法及應用，介紹了如何快速求解信號的Gabor變換係數以及如何由變換係數快速地重建原信號，給出了便於軟體尤其是硬體實現的條件與路徑，極大提高了信號分析與處理的質量與效率，學術價值與應用價值顯而易見。

演算法正在影響人類活動的每個領域，從市場、醫學、遊戲到流行音樂。如果你想要了解形塑今日與明日世界的最強力量，請務必讀這本書。——Andrew McAfee，MIT首席研究科學家當你在網路上下單買下一張股票的時候，是否知道，我們的競爭對手其實都是一支支「演算法」？這些演算法每一秒鐘都在高速閱讀它們的專屬新聞、分析歷史資訊、在股市進行各種交易。我們考慮下單的猶豫片刻，全世界的演算法早已進出股市上萬次。

Find an introduction to the architecture, concepts and algorithms of the Linux kernel in Professional Linux Kernel Architecture, a guide to the kernel sources and large number of connections among subsystems. Find an introduction to the relevant structures and functions exported by the kernel to userland, understand the theoretical and conceptual aspects of the Linux kernel and Unix derivatives, and gain a deeper understanding of the kernel. Learn how to reduce the vast amount of information contained in the kernel sources and obtain the skills necessary to understand the kernel sources.

◎應用數學權威理性解析演算法與社會，讓我們對演算法不再害怕的第一本書◎資訊社會學專家曹家榮（世新大學社會心理學系助理教授）專文導讀 從選餐廳到選總統，演算法正全面影響我們的生活？Facebook和劍橋分析公司分享用戶個資，讓演算法操弄人心？Google的搜尋演算法暗藏性別與種族歧視？ 讓應用數學權威帶我們一一解答以上的問題。 我們對科技與網路的仰賴有如開了一扇窗，

歡迎來到「演算法萬事通」時代── 你能找到最該聘用的人、和最速配的伴侶約會，製作讓人痛哭狂笑的大賣劇本如果想離婚，甚至最佳決策程式幫你順利甩開前妻／夫；當「人工智能＋大數據分析」的情節可以誇張再誇張以後……你確定「知道答案」對你比較好嗎？ 這本書要探討一個重要的問題：如果生活中每一件事都可以變成一個簡單的公式呢？ 這些公式要是能告訴我們：誰將會是我們未來最速配的伴侶；或者哪些電影將會是票房大熱門，

衝突多目標優化問題在現實世界中廣泛存在，這類多目標優化問題(multi-objective optimization problems,MOP)不存在單個最優解使所有目標均達到最優，理論上只能尋求多個衝突目標的權衡優化解。相較于傳統的數學優化方法而言，基於種群優化的多目標演化算法(multi-objective evolutionary algorithms,MOEA)則能更好地權衡多個衝突目標的優化。近年來，針對具有超多目標、大規模決策變量、約束條件、數據驅動及不確定性魯棒性等特性的複雜多目標優化問題，MOEA已成為研究熱點並受到廣泛關注，各類新方法與新技術不斷湧現。與此同時，隨著智能計算技術的發展，將MOEA與各種先進智能計算方法相結合，推動著更有效地解決一系列複雜的多目標優化問題，這成為非常有前景的研究方向。本書主要內容為作者在上述研究領域內過去近十年中的主要相關研究成果的總結。全書共14章，大致分為四個部分：第一部分(第1章)對智能計算進行簡要概述；第二部分(第2～3章)比較全面地介紹各類複雜的MOPs及複雜多目標演化優化的基礎知識；第三部分(第4～10章)介紹求解各類複雜的MOPs所提出的各種先進的MOEAs，並涉及利用智能計算技術輔助的一系列理論成果；第四部分(第11～14章)圍繞MOEA與智能計算的進一步聯繫，介紹將MOEA應用于求解機器學習、深度學習中複雜MOP的前沿理論成果。

This is a reissue of a classic originally published in 1970, with a new preface and some additional problems. Acton deals with a commonsense approach to numerical algorithms for the solution of equati