The algorithms you'll use most often as a programmer have already been discovered, tested, and proven. If you want to take a hard pass on Knuth's brilliant but impenetrable theories and the dense multi-page proofs you'll find in most textbooks, this is the book for you. This fully-illustrated and engaging guide makes it easy for you to learn how to use algorithms effectively in your own programs.Grokking Algorithms is a disarming take on a core computer science topic. In it, you'll learn how to apply common algorithms to the practical problems you face in day-to-day life as a programmer. You'll start with problems like sorting and searching. As you build up your skills in thinking algorithmically, you'll tackle more complex concerns such as data compression or artificial intelligence. Whether you're writing business software, video games, mobile apps, or system utilities, you'll learn algorithmic techniques for solving problems that you thought were out of your grasp. For example, you'll be able to:Write a spell checker using graph algorithmsUnderstand how data compression works using Huffman codingIdentify problems that take too long to solve with naive algorithms, and attack them with algorithms that give you an approximate answer insteadEach carefully-presented example includes helpful diagrams and fully-annotated code samples in Python. By the end of this book, you will know some of the most widely applicable algorithms as well as how and when to use them.

    The field is ready for a text that not only demonstrates how to use the algorithms that make up machine learning methods, but also provides the background needed to understand how and why these algorithms work. Machine Learning: An Algorithmic Perspective is that text.Theory Backed up by Practical ExamplesThe book covers neural networks, graphical models, reinforcement learning, evolutionary algorithms, dimensionality reduction methods, and the important area of optimization. It treads the fine line between adequate academic rigor and overwhelming students with equations and mathematical concepts. The author addresses the topics in a practical way while providing complete information and references where other expositions can be found. He includes examples based on widely available datasets and practical and theoretical problems to test understanding and application of the material. The book describes algorithms with code examples backed up by a website that provides working implementations in Python. The author uses data from a variety of applications to demonstrate the methods and includes practical problems for students to solve.Highlights a Range of Disciplines and ApplicationsDrawing from computer science, statistics, mathematics, and engineering, the multidisciplinary nature of machine learning is underscored by its applicability to areas ranging from finance to biology and medicine to physics and chemistry. Written in an easily accessible style, this book bridges the gaps between disciplines, providing the ideal blend of theory and practical, applicable knowledge."

    It is impossible to understand the spread of an epidemic, a computer virus, large-scale blackouts, or massive extinctions without taking into account the network structure that underlies all these phenomena. In this Very Short Introduction, Guido Caldarelli and Michele Catanzaro discuss the nature and variety of networks, using everyday examples from society, technology, nature, and history to explain and understand the science of network theory. They show the ubiquitous role of networks; how networks self-organize; why the rich get richer; and how networks can spontaneously collapse. They conclude by highlighting how the findings of complex network theory have very wide and important applications in genetics, ecology, communications, economics, and sociology.

    Whether you are a student, software developer, tech entrepreneur, or researcher in computer science, this authoritative and self-contained book tells you everything you need to know about the new global money for the Internet age.How do Bitcoin and its block chain actually work? How secure are your bitcoins? How anonymous are their users? Can cryptocurrencies be regulated? These are some of the many questions this book answers. It begins by tracing the history and development of Bitcoin and cryptocurrencies, and then gives the conceptual and practical foundations you need to engineer secure software that interacts with the Bitcoin network as well as to integrate ideas from Bitcoin into your own projects. Topics include decentralization, mining, the politics of Bitcoin, altcoins and the cryptocurrency ecosystem, the future of Bitcoin, and more.An essential introduction to the new technologies of digital currencyCovers the history and mechanics of Bitcoin and the block chain, security, decentralization, anonymity, politics and regulation, altcoins, and much moreFeatures an accompanying website that includes instructional videos for each chapter, homework problems, programming assignments, and lecture slidesAlso suitable for use with the authors' Coursera online courseElectronic solutions manual (available only to professors)

    Difficult issues need to be figured out, such as scalability, consistency, reliability, efficiency, and maintainability. In addition, we have an overwhelming variety of tools, including relational databases, NoSQL datastores, stream or batch processors, and message brokers. What are the right choices for your application? How do you make sense of all these buzzwords?In this practical and comprehensive guide, author Martin Kleppmann helps you navigate this diverse landscape by examining the pros and cons of various technologies for processing and storing data. Software keeps changing, but the fundamental principles remain the same. With this book, software engineers and architects will learn how to apply those ideas in practice, and how to make full use of data in modern applications. Peer under the hood of the systems you already use, and learn how to use and operate them more effectively Make informed decisions by identifying the strengths and weaknesses of different tools Navigate the trade-offs around consistency, scalability, fault tolerance, and complexity Understand the distributed systems research upon which modern databases are built Peek behind the scenes of major online services, and learn from their architectures

    Mike Loukides kicked things off in June 2010 with “What is data science?” and from there we’ve pursued the various threads and themes that naturally emerged. Now, roughly a year later, we can look back over all we’ve covered and identify a number of core data areas: Data issues -- The opportunities and ambiguities of the data space are evident in discussions around privacy, the implications of data-centric industries, and the debate about the phrase “data science” itself. The application of data: products and processes – A “data product” can emerge from virtually any domain, including everything from data startups to established enterprises to media/journalism to education and research. Data science and data tools -- The tools and technologies that drive data science are of course essential to this space, but the varied techniques being applied are also key to understanding the big data arena.The business of data – Take a closer look at the actions connected to data -- the finding, organizing, and analyzing that provide organizations of all sizes with the information they need to compete.

    Gigabytes, exabytes (that’s one quintillion bytes) of data are sitting on servers across the world. So how can we start to access this explosion of information, this “big data,” and what can it tell us?   Erez Aiden and Jean-Baptiste Michel are two young scientists at Harvard who started to ask those questions. They teamed up with Google to create the Ngram Viewer, a Web-based tool that can chart words throughout the massive Google Books archive, sifting through billions of words to find fascinating cultural trends. On the day that the Ngram Viewer debuted in 2010, more than one million queries were run through it.   On the front lines of Big Data, Aiden and Michel realized that this big dataset—the Google Books archive that contains remarkable information on the human experience—had huge implications for looking at our shared human history. The tool they developed to delve into the data has enabled researchers to track how our language has evolved over time, how art has been censored, how fame can grow and fade, how nations trend toward war. How we remember and how we forget. And ultimately, how Big Data is changing the game for the sciences, humanities, politics, business, and our culture.

    No longer. A new generation of tools and massive amounts of available data make it easy for anyone to create visualizations that communicate ideas far more effectively than generic spreadsheet charts ever could.What’s more, building good charts is quickly becoming a need-to-have skill for managers. If you’re not doing it, other managers are, and they’re getting noticed for it and getting credit for contributing to your company’s success.In Good Charts, dataviz maven Scott Berinato provides an essential guide to how visualization works and how to use this new language to impress and persuade. Dataviz today is where spreadsheets and word processors were in the early 1980s—on the cusp of changing how we work. Berinato lays out a system for thinking visually and building better charts through a process of talking, sketching, and prototyping.This book is much more than a set of static rules for making visualizations. It taps into both well-established and cutting-edge research in visual perception and neuroscience, as well as the emerging field of visualization science, to explore why good charts (and bad ones) create “feelings behind our eyes.” Along the way, Berinato also includes many engaging vignettes of dataviz pros, illustrating the ideas in practice.Good Charts will help you turn plain, uninspiring charts that merely present information into smart, effective visualizations that powerfully convey ideas.

    It's bad for society, especially now. . . . Call me crotchety, but I can't help asking, whatever happened to the social contract?'The get-rich-quick exuberance of the late nineties may have temporarily blinded us to how dependent we are on one another. Subsequent events serve as reminders that the strength of our economy and the security of our society rest on the bonds that connect us. But what, specifically, are these bonds? What do we owe one another as members of the same society?With his characteristic humor, humanity, and candor, one of the nation's most distinguished public leaders and thinkers delivers a fresh vision of politics by returning to basic American values: workers should share in the success of their companies; those who work should not have to live in poverty; and everyone should have access to an education that will better their chances in life.An insider who knows how the economy and government really work, Reich combines realistic solutions with democratic ideals. Businesses do have civic responsibilities, and government must stem a widening income gap that threatens to stratify our nation. And everyone must get involved to help return us to a society that works for everyone.

    But what if you could have your computer do them for you?In "Automate the Boring Stuff with Python," you'll learn how to use Python to write programs that do in minutes what would take you hours to do by hand no prior programming experience required. Once you've mastered the basics of programming, you'll create Python programs that effortlessly perform useful and impressive feats of automation to: Search for text in a file or across multiple filesCreate, update, move, and rename files and foldersSearch the Web and download online contentUpdate and format data in Excel spreadsheets of any sizeSplit, merge, watermark, and encrypt PDFsSend reminder emails and text notificationsFill out online formsStep-by-step instructions walk you through each program, and practice projects at the end of each chapter challenge you to improve those programs and use your newfound skills to automate similar tasks.Don't spend your time doing work a well-trained monkey could do. Even if you've never written a line of code, you can make your computer do the grunt work. Learn how in "Automate the Boring Stuff with Python.""

    Kathryn Schulz, editor of Grist magazine, argues that error is the fundamental human condition and should be celebrated as such. Guiding the reader through the history and psychology of error, from Socrates to Alan Greenspan, Being Wrong will change the way you perceive screw-ups, both of the mammoth and daily variety, forever.

    Conventional wisdom long held that big name, highly athletic hitters and young pitchers with rocket arms were the ticket to success. But Beane and his staff, buoyed by massive amounts of carefully interpreted statistical data, believed that wins could be had by more affordable methods such as hitters with high on-base percentage and pitchers who get lots of ground outs. Given this information and a tight budget, Beane defied tradition and his own scouting department to build winning teams of young affordable players and inexpensive castoff veterans. Lewis was in the room with the A's top management as they spent the summer of 2002 adding and subtracting players and he provides outstanding play-by-play. In the June player draft, Beane acquired nearly every prospect he coveted (few of whom were coveted by other teams) and at the July trading deadline he engaged in a tense battle of nerves to acquire a lefty reliever. Besides being one of the most insider accounts ever written about baseball, Moneyball is populated with fascinating characters. We meet Jeremy Brown, an overweight college catcher who most teams project to be a 15th round draft pick (Beane takes him in the first). Sidearm pitcher Chad Bradford is plucked from the White Sox triple-A club to be a key set-up man and catcher Scott Hatteberg is rebuilt as a first baseman. But the most interesting character is Beane himself. A speedy athletic can't-miss prospect who somehow missed, Beane reinvents himself as a front-office guru, relying on players completely unlike, say, Billy Beane. Lewis, one of the top nonfiction writers of his era (Liar's Poker, The New New Thing), offers highly accessible explanations of baseball stats and his roadmap of Beane's economic approach makes Moneyball an appealing reading experience for business people and sports fans alike. --John Moe

    Verification that algorithms work is emphasized more than their complexity. An effective use of examples, and huge number of interesting exercises, demonstrate the topics of trees and distance, matchings and factors, connectivity and paths, graph coloring, edges and cycles, and planar graphs. For those who need to learn to make coherent arguments in the fields of mathematics and computer science.

    It has sold 30,000 copies in each edition and will continue to reach a huge academic audience.In this renowned book, Everett M. Rogers, professor and chair of the Department of Communication & Journalism at the University of New Mexico, explains how new ideas spread via communication channels over time. Such innovations are initially perceived as uncertain and even risky. To overcome this uncertainty, most people seek out others like themselves who have already adopted the new idea. Thus the diffusion process consists of a few individuals who first adopt an innovation, then spread the word among their circle of acquaintances--a process which typically takes months or years. But there are exceptions: use of the Internet in the 1990s, for example, may have spread more rapidly than any other innovation in the history of humankind. Furthermore, the Internet is changing the very nature of diffusion by decreasing the importance of physical distance between people. The fifth edition addresses the spread of the Internet, and how it has transformed the way human beings communicate and adopt new ideas.

    It is the basis for a new approach to artificial intelligence that aims to program computers to use example data or past experience to solve a given problem. In this volume in the MIT Press Essential Knowledge series, Ethem Alpayd�n offers a concise and accessible overview of the new AI. This expanded edition offers new material on such challenges facing machine learning as privacy, security, accountability, and bias. Alpayd�n, author of a popular textbook on machine learning, explains that as Big Data has gotten bigger, the theory of machine learning--the foundation of efforts to process that data into knowledge--has also advanced. He describes the evolution of the field, explains important learning algorithms, and presents example applications. He discusses the use of machine learning algorithms for pattern recognition; artificial neural networks inspired by the human brain; algorithms that learn associations between instances; and reinforcement learning, when an autonomous agent learns to take actions to maximize reward. In a new chapter, he considers transparency, explainability, and fairness, and the ethical and legal implications of making decisions based on data.