It is also about Python. However, there is much more. The study of algorithms and data structures is central to understanding what computer science is all about. Learning computer science is not unlike learning any other type of difficult subject matter. The only way to be successful is through deliberate and incremental exposure to the fundamental ideas. A beginning computer scientist needs practice so that there is a thorough understanding before continuing on to the more complex parts of the curriculum. In addition, a beginner needs to be given the opportunity to be successful and gain confidence. This textbook is designed to serve as a text for a first course on data structures and algorithms, typically taught as the second course in the computer science curriculum. Even though the second course is considered more advanced than the first course, this book assumes you are beginners at this level. You may still be struggling with some of the basic ideas and skills from a first computer science course and yet be ready to further explore the discipline and continue to practice problem solving. We cover abstract data types and data structures, writing algorithms, and solving problems. We look at a number of data structures and solve classic problems that arise. The tools and techniques that you learn here will be applied over and over as you continue your study of computer science.

    Polya, How to Solve It will show anyone in any field how to think straight. In lucid and appealing prose, Polya reveals how the mathematical method of demonstrating a proof or finding an unknown can be of help in attacking any problem that can be reasoned out--from building a bridge to winning a game of anagrams. Generations of readers have relished Polya's deft--indeed, brilliant--instructions on stripping away irrelevancies and going straight to the heart of the problem.

    You can’t just find a pattern and copy it into your program, the way you can with off-the-shelf functions or libraries. The pattern is not a specific piece of code, but a general concept for solving a particular problem. They are like pre-made blueprints that you can customize to solve a recurring design problem in your code.The book Dive Into Design Patterns illustrates 22 classic design patterns and 8 design principles that these patterns are based on.- Every chapter starts from a discussion of a real life software design problem which is then progressively solved by applying one of the patterns.- Then goes a detailed review of the pattern’s structure and its variations, followed by a code example.- Then the books shows various applications of the pattern and teaches how to implement the pattern step by step, even in an existing program.- Each chapter concludes with a discussion of pros and cons of the pattern and its relations, similarities and differences with other patterns.

    It's not that there is a lack of information out there. You can find academic papers, engineering blogs, and even books on the subject. The problem is that the available information is spread out all over the place, and if you were to put it on a spectrum from theory to practice, you would find a lot of material at the two ends, but not much in the middle.That is why I decided to write a book to teach the fundamentals of distributed systems so that you don’t have to spend countless hours scratching your head to understand how everything fits together. This is the guide I wished existed when I first started out, and it's based on my experience building large distributed systems that scale to millions of requests per second and billions of devices.If you develop the back-end of web or mobile applications (or would like to!), this book is for you. When building distributed systems, you need to be familiar with the network stack, data consistency models, scalability and reliability patterns, and much more. Although you can build applications without knowing any of that, you will end up spending hours debugging and re-designing their architecture, learning lessons that you could have acquired in a much faster and less painful way.

    Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning.The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models.Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.

    They use powerful techniques to process information intelligently and offer features based on patterns and relationships in data. Algorithms of the Intelligent Web shows readers how to use the same techniques employed by household names like Google Ad Sense, Netflix, and Amazon to transform raw data into actionable information.Algorithms of the Intelligent Web is an example-driven blueprint for creating applications that collect, analyze, and act on the massive quantities of data users leave in their wake as they use the web. Readers learn to build Netflix-style recommendation engines, and how to apply the same techniques to social-networking sites. See how click-trace analysis can result in smarter ad rotations. All the examples are designed both to be reused and to illustrate a general technique- an algorithm-that applies to a broad range of scenarios.As they work through the book's many examples, readers learn about recommendation systems, search and ranking, automatic grouping of similar objects, classification of objects, forecasting models, and autonomous agents. They also become familiar with a large number of open-source libraries and SDKs, and freely available APIs from the hottest sites on the internet, such as Facebook, Google, eBay, and Yahoo.Purchase of the print book comes with an offer of a free PDF, ePub, and Kindle eBook from Manning. Also available is all code from the book.

    Some of them are highly visible, in laptops, tablets, cell phones, and smart watches. But most are invisible, like those in appliances, cars, medical equipment, transportation systems, power grids, and weapons. We never see the myriad computers that quietly collect, share, and sometimes leak vast amounts of personal data about us. Through computers, governments and companies increasingly monitor what we do. Social networks and advertisers know far more about us than we should be comfortable with, using information we freely give them. Criminals have all-too-easy access to our data. Do we truly understand the power of computers in our world?Understanding the Digital World explains how computer hardware, software, networks, and systems work. Topics include how computers are built and how they compute; what programming is and why it is difficult; how the Internet and the web operate; and how all of these affect our security, privacy, property, and other important social, political, and economic issues. This book also touches on fundamental ideas from computer science and some of the inherent limitations of computers. It includes numerous color illustrations, notes on sources for further exploration, and a glossary to explain technical terms and buzzwords.Understanding the Digital World is a must-read for all who want to know more about computers and communications. It explains, precisely and carefully, not only how they operate but also how they influence our daily lives, in terms anyone can understand, no matter what their experience and knowledge of technology.

    Covers all the topics needed by computer scientists with a sometimes humorous approach that reviewers found refreshing. The goal of the book is to provide a firm understanding of the principles and the big picture of where computer theory fits into the field.

    The Productive Programmer offers critical timesaving and productivity tools that you can adopt right away, no matter what platform you use. Master developer Neal Ford not only offers advice on the mechanics of productivity-how to work smarter, spurn interruptions, get the most out your computer, and avoid repetition-he also details valuable practices that will help you elude common traps, improve your code, and become more valuable to your team. You'll learn to:Write the test before you write the codeManage the lifecycle of your objects fastidiously Build only what you need now, not what you might need later Apply ancient philosophies to software development Question authority, rather than blindly adhere to standardsMake hard things easier and impossible things possible through meta-programming Be sure all code within a method is at the same level of abstraction Pick the right editor and assemble the best tools for the job This isn't theory, but the fruits of Ford's real-world experience as an Application Architect at the global IT consultancy ThoughtWorks. Whether you're a beginner or a pro with years of experience, you'll improve your work and your career with the simple and straightforward principles in The Productive Programmer.

    In this newFourth Edition, Charles P. Pfleeger and Shari Lawrence Pfleeger have thoroughly updated their classic guide to reflect today's newest technologies, standards, and trends. The authors first introduce the core concepts and vocabulary of computer security, including cryptography. Next, they systematically identify and assess threats now facing programs, operating systems, databases, and networks. For each threat, they offer best-practice responses. Security in Computing, Fourth Edition, goes beyond technology, covering crucial management issues you face in protecting infrastructure and information. This edition contains an all-new chapter on the economics of cybersecurity, and making the business case for security investments. Another new chapter addresses privacy--from data mining to identity theft, to RFID and e-voting. New coverage also includes Programming mistakes that compromise security: man-in-the-middle, timing, and privilege escalation Web application threats and vulnerabilities Networks of compromised systems: bots, botnets, and drones Rootkits--including the notorious Sony XCP Wi-Fi network security challenges, standards, and techniques New malicious code attacks, including false interfaces and keystroke loggers Improving code quality: software engineering, testing, and liability approaches Biometric authentication: capabilities and limitations Using Advanced Encryption System (AES) more effectively Balancing efficiency and piracy control in music and other digital content Defending against new cryptanalytic attacks against RSA, DES, and SHA Responding to the emergence of organized attacker groups pursuing profit 0132390779B0721200 Every day, the news media giv

    Whether you’re an intermediate-level Python programmer or a student of computational modeling, you’ll delve into examples of complex systems through a series of exercises, case studies, and easy-to-understand explanations.You’ll work with graphs, algorithm analysis, scale-free networks, and cellular automata, using advanced features that make Python such a powerful language. Ideal as a text for courses on Python programming and algorithms, Think Complexity will also help self-learners gain valuable experience with topics and ideas they might not encounter otherwise.Work with NumPy arrays and SciPy methods, basic signal processing and Fast Fourier Transform, and hash tablesStudy abstract models of complex physical systems, including power laws, fractals and pink noise, and Turing machinesGet starter code and solutions to help you re-implement and extend original experiments in complexityExplore the philosophy of science, including the nature of scientific laws, theory choice, realism and instrumentalism, and other topicsExamine case studies of complex systems submitted by students and readers

    It's certainly no walk in the park. And, depending on where you've been for your information when it comes to SEO, it might be outdated, or just flat-out wrong. Why is that? Search has been evolving at an uncanny rate in recent years. And, if you're not in the know, then you could end up spinning your wheels and wasting valuable and precious time and resources on techniques that no longer work. The main reason for the recent changes: to increase relevancy. Google's sole mission is to provide the most relevant search results at the top of its searches, in the quickest manner possible. But, in recent years, due to some mischievous behavior at the hand of a small group of people, relevancy began to wane. SEO 2016 :: Understanding Google's Algorithm Adjustments The field of SEO has been changing, all led by Google's onslaught of algorithm adjustments that have decimated and razed some sites while uplifting and building others. Since 2011, Google has made it its mission to hunt out and demote spammy sites that sacrifice user-experience, focus on thin content, or simply spend their time trying to trick and deceive their way to the top of its search results. At the same time, Google has increased its reliance on four major components of trust, that work at the heart of its search algorithm: Trust in Age Trust in Authority Trust in Content Relevancy In this book, you'll learn just how each of these affects Google's search results, and just how you can best optimize your site and content to ensure that you're playing by Google's many rules. And, although there have been many algorithm adjustments over the years, four major ones have shaped and forever changed the search engine landscape: Google Panda Google Penguin Google Hummingbird Google Mobilegeddon We'll discuss the nature of these changes and just how each of these algorithm adjustments have shaped the current landscape in search engine optimization. So what does it take to rank your site today? In order to compete at any level in SEO, you have to earn trust - Google's trust that is. But, what does that take? How can we build trust quickly without jumping through all the hoops? SEO is by no means a small feat. It takes hard work applied consistently overtime. There are no overnight success stories when it comes to SEO. But there are certainly ways to navigate the stormy online waters of Google's highly competitive search. Download SEO 2016 :: Learn Search Engine Optimization Lift the veil on Google's complex search algorithm, and understand just what it takes to rank on Google searches today, not yesterday.

    Written by Magnus Lie Hetland, author of Beginning Python, this book is sharply focused on classical algorithms, but it also gives a solid understanding of fundamental algorithmic problem-solving techniques.The book deals with some of the most important and challenging areas of programming and computer science, but in a highly pedagogic and readable manner. The book covers both algorithmic theory and programming practice, demonstrating how theory is reflected in real Python programs. Well-known algorithms and data structures that are built into the Python language are explained, and the user is shown how to implement and evaluate others himself.

    You no longer use HTML and XHTML as design tools, but strictly as ways to define the meaning and structure of web content. And Cascading Style Sheets (CSS) are no longer just something interesting to tinker with, but a reliable method for handling all matters of presentation, from fonts and colors to page layout. When you follow the standards, both the site's design and underlying code are much cleaner. But how do you keep all those HTML and XHTML tags and CSS values straight? Jennifer Niederst-Robbins, the author of our definitive guide on standards-compliant design, Web Design in a Nutshell, offers you the perfect little guide when you need answers immediately: HTML and XHTML Pocket Reference. This revised and updated new edition takes the top 20% of vital reference information from her Nutshell book, augments it judiciously, cross-references everything, and organizes it according to the most common needs of web developers. The result is a handy book that offers the bare essentials on web standards in a small, concise format that you can use carry anywhere for quick reference. This guide will literally fit into your back pocket. Inside HTML and XHTML Pocket Reference, you'll find instantly accessible alphabetical listings of every element and attribute in the HTML 4.01 and XHTML 1.0 Recommendations. This is an indispensable reference for any serious web designer, author, or programmer who needs a fast on-the-job resource when working with established web standards.

    It’s already happening in impressive ways. But as we’ve witnessed over the past 20 years, new technology also inevitably raises complex questions and broad societal concerns.” – Brad Smith and Harry Shum on The Future Computed. “As we look to a future powered by a partnership between computers and humans, it’s important that we address these challenges head on. How do we ensure that AI is designed and used responsibly? How do we establish ethical principles to protect people? How should we govern its use? And how will AI impact employment and jobs?” – Brad Smith and Harry Shum on The Future Computed. As Artificial Intelligence shows up in every aspect of our lives, Microsoft's top minds provide a guide discussing how we should prepare for the future. Whether you're a government leader crafting new laws, an entrepreneur looking to incorporate AI into your business, or a parent contemplating the future of education, this book explains the trends driving the AI revolution, identifies the complex ethics and workforce issues we all need to think about and suggests a path forward. Read more: The Future Computed: Artificial Intelligence and its role in society provides Microsoft’s perspective on where AI technology is going and the new societal issues it is raising – ensuring AI is designed and used responsibly, establishing ethical principles to protect people, and how AI will impact employment and jobs. The principles of fairness, reliability and safety, privacy and security, inclusiveness, transparency and accountability are critical to addressing the societal impacts of AI and building trust as AI becomes more and more a part of the products and services that people use at work and at home every day. A central theme in The Future Computed is that for AI to deliver on its potential drive widespread economic and social progress, the technology needs to be human-centered – combining the capabilities of computers with human capabilities to enable people to achieve more. But a human-centered approach can only be realized if researchers, policymakers, and leaders from government, business and civil society come together to develop a shared ethical framework for AI. This in turn will help foster responsible development of AI systems that will engender trust. Because in an increasingly AI-driven world the question is not what computers can do, it is what computers should do. The Future Computed also draws a few conclusions as we chart our path forward. First, the companies and countries that will fare best in the AI era will be those that embrace these changes rapidly and effectively. Second, while AI will help solve big societal problems, we must look to this future with a critical eye as there will be challenges as well as opportunities. Third, we need to act with a sense of shared responsibility because AI won’t be created by the tech sector alone. Finally, skilling-up for an AI-powered world involves more than science, technology, engineering and math. As computers behave more like humans, the social sciences and humanities will become grow in importance.