....The most comprehensive guide to designing practical and efficient algorithms.... Written by a well-known algorithms researcher who received the IEEE Computer Science and Engineering Teaching Award, this new edition of The Algorithm Design Manual is an essential learning tool for students needing a solid grounding in algorithms, as well as a special text/reference for professionals who need an authoritative and insightful guide. Professor Skiena is also author of the popular Springer text, Programming Challenges: The Programming Contest Training Manual.

The practice of building software is a “new kid on the block” technology. Though it may not seem this way for those who have been in the field for most of their careers, in the overall scheme of professions, software builders are relative “newbies.” In the short history of the software field, a lot of facts have been identified, and a lot of fallacies promulgated. Those facts and fallacies are what this book is about. There’s a problem with those facts–and, as you might imagine, those fallacies. Many of these fundamentally important facts are learned by a software engineer, but over the short lifespan of the software field, all too many of them have been forgotten. While reading Facts and Fallacies of Software Engineering, you may experience moments of “Oh, yes, I had forgotten that,” alongside some “Is that really true?” thoughts. The author of this book doesn’t shy away from controversy. In fact, each of the facts and fallacies is accompanied by a discussion of whatever controversy envelops it. You may find yourself agreeing with a lot of the facts and fallacies, yet emotionally disturbed by a few of them! Whether you agree or disagree, you will learn why the author has been called “the premier curmudgeon of software practice.” These facts and fallacies are fundamental to the software building field–forget or neglect them at your peril!

This is official Amazon Web Services (AWS) documentation for Amazon Redshift. Amazon Redshift is a fast, fully managed, petabyte-scale data warehouse service that makes it simple and cost-effective to efficiently analyze all your data using your existing business intelligence tools.

Product Description Web technology has become the foundation for all sorts of critical networked applications and far-reaching methods of data exchange, and beneath it all is a fundamental protocol: HyperText Transfer Protocol, or HTTP. HTTP: The Definitive Guide documents everything that technical people need for using HTTP efficiently-including the "black arts" and "tricks of the trade"-and does so in a clear and readable manner. Written by experts with years of practical and teaching experience, this book is the definitive technical bible on HTTP and related core web technologies because it clearly explains the "why" as well as the "how". A reader can understand how web applications work, how the core Internet protocols and architectural building blocks interact, and how to correctly implement Internet clients and servers. It's an essential toolkit that no technically-inclined member of the Internet community should be without.

The software development ecosystem is constantly changing, providing a constant stream of new tools, frameworks, techniques, and paradigms. Over the past few years, incremental developments in core engineering practices for software development have created the foundations for rethinking how architecture changes over time, along with ways to protect important architectural characteristics as it evolves. This practical guide ties those parts together with a new way to think about architecture and time.

Kubernetes in Action teaches you to use Kubernetes to deploy container-based distributed applications. You'll start with an overview of Docker and Kubernetes before building your first Kubernetes cluster. You'll gradually expand your initial application, adding features and deepening your knowledge of Kubernetes architecture and operation. As you navigate this comprehensive guide, you'll explore high-value topics like monitoring, tuning, and scaling. Kubernetes is Greek for "helmsman," your guide through unknown waters. The Kubernetes container orchestration system safely manages the structure and flow of a distributed application, organizing containers and services for maximum efficiency. Kubernetes serves as an operating system for your clusters, eliminating the need to factor the underlying network and server infrastructure into your designs.

C++是在C语言基础上开发的一种集面向对象编程、泛型编程和过程化编程于一体的编程语言，是C语言的超集。《C++ Primer Plus(第6版)英文版(上、下册)》是根据2003年的ISO/ANSI C++标准编写的，通过大量短小精悍的程序详细而全面地阐述了C++的基本概念和技术，并专辟一章介绍了C++11新增的功能。 全书分18章和10个附录，分别介绍了C++程序的运行方式、基本数据类型、复合数据类型、循环和关系表达式、分支语句和逻辑运算符、函数重载和函数模板、内存模型和名称空间、类的设计和使用、多态、虚函数、动态内存分配、继承、代码重用、友元、异常处理技术、string类和标准模板库、输入/输出、C++11新增功能等内容。 《C++ Primer Plus(第6版)英文版(上、下册)》针对C++初学者，书中从C语言基础知识开始介绍，然后在此基础上详细阐述C++新增的特性，因此不要求读者有C语言方面的背景知识。 《C++ Primer Plus(第6版)英文版(上、下册)》可作为高等院校C++课程的教材，也可供初学者自学C++时使用。

Algorithms are the heart and soul of computer science. Their applications range from network routing and computational genomics to public-key cryptography and machine learning. Studying algorithms can make you a better programmer, a clearer thinker, and a master of technical interviews. Algorithms Illuminated is an accessible introduction to the subject for anyone with at least a little programming experience. The exposition emphasizes the big picture and conceptual understanding over low-level implementation and mathematical details---like a transcript of what an expert algorithms tutor would say over a series of one-on-one lessons. Part 1 covers asymptotic analysis and big-O notation, divide-and-conquer algorithms and the master method, randomized algorithms, and several famous algorithms for sorting and selection.

This text, extensively class-tested over a decade at UC Berkeley and UC San Diego, explains the fundamentals of algorithms in a story line that makes the material enjoyable and easy to digest. Emphasis is placed on understanding the crisp mathematical idea behind each algorithm, in a manner that is intuitive and rigorous without being unduly formal.

Now that answering complex and compelling questions with data can make the difference in an election or a business model, data science is an attractive discipline. But how can you learn this wide-ranging, interdisciplinary field? With this book, you’ll get material from Columbia University’s "Introduction to Data Science" class in an easy-to-follow format. Each chapter-long lecture features a guest data scientist from a prominent company such as Google, Microsoft, or eBay teaching new algorithms, methods, or models by sharing case studies and actual code they use. You’ll learn what’s involved in the lives of data scientists and be able to use the techniques they present. Guest lectures focus on topics such as: Machine learning and data mining algorithms Statistical models and methods Prediction vs. description Exploratory data analysis Communication and visualization Data processing Big data Programming Ethics Asking good questions If you’re familiar with linear algebra, probability and statistics, and have some programming experience, this book will get you started with data science. Doing Data Science is collaboration between course instructor Rachel Schutt (also employed by Google) and data science consultant Cathy O’Neil (former quantitative analyst for D.E. Shaw) who attended and blogged about the course.

《世界绘画经典教程——铅笔素描的乐趣》的作者安德鲁·路米斯是美国知名插画家、作家和艺术导师。本书是路米斯在美国艺术学院任教时的教材，因其生动有趣，广受学生欢迎。路米斯的教学方法有许多独特之处，比如用球画人头部的方法等，其特点是零基础快速入门。按照路米斯教的方法，通过他幽默的对白指导，初学者很快能就能用铅笔画出人物的漫画形象。打开这本书，按照路米斯的指导方法去绘画，，你就会从一个铅笔手绘“屌丝”提升到“大神”级别

《算法技术手册》内容简介：开发健壮的软件需要高效的算法，然后程序员们往往直至问题发生之时，才会去求助于算法。《算法技术手册》讲解了许多现有的算法，可用于解决各种问题。通过阅读它，可以使您学会如何选择和实现正确的算法，来达成自己的目标。另外，书中的数学深浅适中，足够使您可以了解并分析算法的性能。 较之理论而言，《算法技术手册》更专注于应用。《算法技术手册》提供了高效的代码解决方案，使用多种语言进行编写，让您可以轻松地将其应用于特定的工程当中。通过《算法技术手册》，您可以： · 解决特定代码的问题，或者提升既有解决方案的性能 · 快速找到与您所解决的问题相关的算法，并决定哪个算法才是最适合的那一个 · 探索使用C、C++、Java以及Ruby实现的算法解决方案以及开发小贴士 · 了解算法预期的性能，以及它达到最高性能时所需要的条件 · 发现不同算法之间相似的设计哲学 · 学习高级数据结构，来提升算法的性能 通过《算法技术手册》，您能学到如何提升算法的性能，这将是您的软件应用程序走向成功的关键。

内容包括：电影剧本是什么、主题、人物、构成人物、结尾和开端、段落、情节点、写电影剧本、剧本的形式、改编、用电脑来写作等。

The First Complete Guide to Bluetooth Low Energy: How It Works, What It Can Do, and How to Apply It A radical departure from conventional Bluetooth technology, Bluetooth low energy (BLE) enables breakthrough wireless applications in industries ranging from healthcare to transportation. Running on a coin-sized battery, BLE can operate reliably for years, connecting and extending everything from personal area network devices to next-generation sensors. Now, one of the standard's leading developers has written the first comprehensive, accessible introduction to BLE for every system developer, designer, and engineer. Robin Heydon, a member of the Bluetooth SIG Hall of Fame, has brought together essential information previously scattered through multiple standards documents, sharing the context and expert insights needed to implement high-performance working systems. He first reviews BLE's design goals, explaining how they drove key architectural decisions, and introduces BLE's innovative usage models. Next, he thoroughly covers how the two main parts of BLE, the controller and host, work together, and then addresses key issues from security and profiles through testing and qualification. This knowledge has enabled the creation of Bluetooth Smart and Bluetooth Smart Ready devices. This guide is an indispensable companion to the official BLE standards documents and is for every technical professional and decision-maker considering BLE, planning BLE products, or transforming plans into working systems. Topics Include * BLE device types, design goals, terminology, and core concepts * Architecture: controller, host, applications, and stack splits * Usage models: presence detection, data broadcasting, connectionless models, and gateways * Physical Layer: modulation, frequency band, radio channels, power, tolerance, and range * Direct Test Mode: transceiver testing, hardware interfaces, and HCI * Link Layer: state machine, packets, channels, broadcasting, encryption, and optimization * HCI: physical/logical interfaces, controller setup, and connection management * L2CAP: channels and packet structure, and LE signaling channels * Attributes: grouping, services, characteristics, and protocols * Security: pairing, bonding, and data signing * Generic Access Profiles: roles, modes, procedures, security modes, data advertising, and services * Applications, devices, services, profiles, and peripherals * Testing/qualification: starting projects, selecting features, planning, testing, compliance, and more

TensorFlow, a popular library for machine learning, embraces the innovation and community-engagement of open source, but has the support, guidance, and stability of a large corporation. Because of its multitude of strengths, TensorFlow is appropriate for individuals and businesses ranging from startups to companies as large as, well, Google. TensorFlow is currently being used for natural language processing, artificial intelligence, computer vision, and predictive analytics. TensorFlow, open sourced to the public by Google in November 2015, was made to be flexible, efficient, extensible, and portable. Computers of any shape and size can run it, from smartphones all the way up to huge computing clusters. This book is for anyone who knows a little machine learning (or not) and who has heard about TensorFlow, but found the documentation too daunting to approach. It introduces the TensorFlow framework and the underlying machine learning concepts that are important to harness machine intelligence. After reading this book, you should have a deep understanding of the core TensorFlow API.

Multivariate Analysis deals with observations on more than one variable where there is some inherent interdependence between the variables. With several texts already available in this area, one may very well enquire of the authors as to the need for yet another book. Most of the available books fall into two categories, either theoretical or data analytic. The present book not only combines the two approaches but it also has been guided by the need to give suitable matter for the beginner as well as illustrating some deeper aspects of the subject for the research worker. Practical examples are kept to the forefront and, wherever feasible, each technique is motivated by such an example.

A comprehensive look at learning and generalization theory. The statistical theory of learning and generalization concerns the problem of choosing desired functions on the basis of empirical data. Highly applicable to a variety of computer science and robotics fields, this book offers lucid coverage of the theory as a whole. Presenting a method for determining the necessary and sufficient conditions for consistency of learning process, the author covers function estimates from small data pools, applying these estimations to real-life problems, and much more.

The aim of this book is to discuss the fundamental ideas which lie behind the statistical theory of learning and generalization. It considers learning as a general problem of function estimation based on empirical data. Omitting proofs and technical details, the author concentrates on discussing the main results of learning theory and their connections to fundamental problems in statistics. This second edition contains three new chapters devoted to further development of the learning theory and SVM techniques. Written in a readable and concise style, the book is intended for statisticians, mathematicians, physicists, and computer scientists.

Normal 0 false false false Numerical Analysis, Second Edition , is a modern and readable text. This book covers not only the standard topics but also some more advanced numerical methods being used by computational scientists and engineers—topics such as compression, forward and backward error analysis, and iterative methods of solving equations—all while maintaining a level of discussion appropriate for undergraduates. Each chapter contains a Reality Check, which is an extended exploration of relevant application areas that can launch individual or team projects. MATLAB ® is used throughout to demonstrate and implement numerical methods. The Second Edition features many noteworthy improvements based on feedback from users, such as new coverage of Cholesky factorization, GMRES methods, and nonlinear PDEs.