Through the book’s many examples, you’ll quickly learn how OCaml stands out as a tool for writing fast, succinct, and readable systems code.Real World OCaml takes you through the concepts of the language at a brisk pace, and then helps you explore the tools and techniques that make OCaml an effective and practical tool. In the book’s third section, you’ll delve deep into the details of the compiler toolchain and OCaml’s simple and efficient runtime system.Learn the foundations of the language, such as higher-order functions, algebraic data types, and modulesExplore advanced features such as functors, first-class modules, and objectsLeverage Core, a comprehensive general-purpose standard library for OCamlDesign effective and reusable libraries, making the most of OCaml’s approach to abstraction and modularityTackle practical programming problems from command-line parsing to asynchronous network programmingExamine profiling and interactive debugging techniques with tools such as GNU gdb

    Corporations & government agencies around the world are pouring billions into achieving AI’s Holy Grail—human-level intelligence. Once AI has attained it, scientists argue, it will have survival drives much like our own. We may be forced to compete with a rival more cunning, more powerful & more alien than we can imagine. Thru profiles of tech visionaries, industry watchdogs & groundbreaking AI systems, James Barrat's Our Final Invention explores the perils of the heedless pursuit of advanced AI. Until now, human intelligence has had no rival. Can we coexist with beings whose intelligence dwarfs our own? Will they allow us to?

    We nevertheless muddle through even in the absence of theories of how to act. But how do we do it?In Probably Approximately Correct, computer scientist Leslie Valiant presents a masterful synthesis of learning and evolution to show how both individually and collectively we not only survive, but prosper in a world as complex as our own. The key is “probably approximately correct” algorithms, a concept Valiant developed to explain how effective behavior can be learned. The model shows that pragmatically coping with a problem can provide a satisfactory solution in the absence of any theory of the problem. After all, finding a mate does not require a theory of mating. Valiant’s theory reveals the shared computational nature of evolution and learning, and sheds light on perennial questions such as nature versus nurture and the limits of artificial intelligence.Offering a powerful and elegant model that encompasses life’s complexity, Probably Approximately Correct has profound implications for how we think about behavior, cognition, biological evolution, and the possibilities and limits of human and machine intelligence.

    Erlang is gaining widespread adoption with the advent of multi-core processors and their new scalable approach to concurrency. With this guide you'll learn how to write complex concurrent programs in Erlang, regardless of your programming background or experience. Written by leaders of the international Erlang community -- and based on their training material -- Erlang Programming focuses on the language's syntax and semantics, and explains pattern matching, proper lists, recursion, debugging, networking, and concurrency. This book helps you:Understand the strengths of Erlang and why its designers included specific features Learn the concepts behind concurrency and Erlang's way of handling it Write efficient Erlang programs while keeping code neat and readable Discover how Erlang fills the requirements for distributed systems Add simple graphical user interfaces with little effort Learn Erlang's tracing mechanisms for debugging concurrent and distributed systems Use the built-in Mnesia database and other table storage features Erlang Programming provides exercises at the end of each chapter and simple examples throughout the book.

    In each essay, contributors present a notable software architecture, and analyze what makes it innovative and ideal for its purpose. Some of the engineers in this book reveal how they developed a specific project, including decisions they faced and tradeoffs they made. Others take a step back to investigate how certain architectural aspects have influenced computing as a whole. With this book, you'll discover:How Facebook's architecture is the basis for a data-centric application ecosystem The effect of Xen's well-designed architecture on the way operating systems evolve How community processes within the KDE project help software architectures evolve from rough sketches to beautiful systems How creeping featurism has helped GNU Emacs gain unanticipated functionality The magic behind the Jikes RVM self-optimizable, self-hosting runtime Design choices and building blocks that made Tandem the choice platform in high-availability environments for over two decades Differences and similarities between object-oriented and functional architectural views How architectures can affect the software's evolution and the developers' engagement Go behind the scenes to learn what it takes to design elegant software architecture, and how it can shape the way you approach your own projects, with Beautiful Architecture.

    Dyson traces the course of the information revolution, illuminating the lives and work of visionaries - from the time of Thomas Hobbes to the time of John von Neumann - who foresaw the development of artificial intelligence, artificial life, and artificial mind. This book derives both its title and its outlook from Samuel Butler's 1863 essay "Darwin Among the Machines." Observing the beginnings of miniaturization, self-reproduction, and telecommunication among machines, Butler predicted that nature's intelligence, only temporarily subservient to technology, would resurface to claim our creations as her own. Weaving a cohesive narrative among his brilliant predecessors, Dyson constructs a straightforward, convincing, and occasionally frightening view of the evolution of mind in the global network, on a level transcending our own. Dyson concludes that we are in the midst of an experiment that echoes the prehistory of human intelligence and the origins of life. Just as the exchange of coded molecular instructions brought life as we know it to the early earth's primordial soup, and as language and mind combined to form the culture in which we live, so, in the digital universe, are computer programs and worldwide networks combining to produce an evolutionary theater in which the distinctions between nature and technology are increasingly obscured. Nature, believes Dyson, is on the side of the machines.

    If you're a reasonably proficient programmer who can think logically, you have all the background you'll need. Stepanov and Rose introduce the relevant abstract algebra and number theory with exceptional clarity. They carefully explain the problems mathematicians first needed to solve, and then show how these mathematical solutions translate to generic programming and the creation of more effective and elegant code. To demonstrate the crucial role these mathematical principles play in many modern applications, the authors show how to use these results and generalized algorithms to implement a real-world public-key cryptosystem. As you read this book, you'll master the thought processes necessary for effective programming and learn how to generalize narrowly conceived algorithms to widen their usefulness without losing efficiency. You'll also gain deep insight into the value of mathematics to programming--insight that will prove invaluable no matter what programming languages and paradigms you use. You will learn aboutHow to generalize a four thousand-year-old algorithm, demonstrating indispensable lessons about clarity and efficiencyAncient paradoxes, beautiful theorems, and the productive tension between continuous and discreteA simple algorithm for finding greatest common divisor (GCD) and modern abstractions that build on itPowerful mathematical approaches to abstractionHow abstract algebra provides the idea at the heart of generic programmingAxioms, proofs, theories, and models: using mathematical techniques to organize knowledge about your algorithms and data structuresSurprising subtleties of simple programming tasks and what you can learn from themHow practical implementations can exploit theoretical knowledge

    They can navigate more data than a doctor or lawyer and act with greater precision. For many years we’ve taken solace in the notion that they can’t create. But now that algorithms can learn and adapt, does the future of creativity belong to machines, too?It is hard to imagine a better guide to the bewildering world of artificial intelligence than Marcus du Sautoy, a celebrated Oxford mathematician whose work on symmetry in the ninth dimension has taken him to the vertiginous edge of mathematical understanding. In The Creativity Code he considers what machine learning means for the future of creativity. The Pollockizer can produce drip paintings in the style of Jackson Pollock, Botnik spins off fanciful (if improbable) scenes inspired by J. K. Rowling, and the music-composing algorithm Emmy managed to fool a panel of Bach experts. But do these programs just mimic, or do they have what it takes to create? Du Sautoy argues that to answer this question, we need to understand how the algorithms that drive them work―and this brings him back to his own subject of mathematics, with its puzzles, constraints, and enticing possibilities.While most recent books on AI focus on the future of work, The Creativity Code moves us to the forefront of creative new technologies and offers a more positive and unexpected vision of our future cohabitation with machines. It challenges us to reconsider what it means to be human―and to crack the creativity code.

    But facing the sea change that AI will bring can be paralyzing. How should companies set strategies, governments design policies, and people plan their lives for a world so different from what we know? In the face of such uncertainty, many analysts either cower in fear or predict an impossibly sunny future.But in Prediction Machines, three eminent economists recast the rise of AI as a drop in the cost of prediction. With this single, masterful stroke, they lift the curtain on the AI-is-magic hype and show how basic tools from economics provide clarity about the AI revolution and a basis for action by CEOs, managers, policy makers, investors, and entrepreneurs.When AI is framed as cheap prediction, its extraordinary potential becomes clear: Prediction is at the heart of making decisions under uncertainty. Our businesses and personal lives are riddled with such decisions. Prediction tools increase productivity--operating machines, handling documents, communicating with customers. Uncertainty constrains strategy. Better prediction creates opportunities for new business structures and strategies to compete. Penetrating, fun, and always insightful and practical, Prediction Machines follows its inescapable logic to explain how to navigate the changes on the horizon. The impact of AI will be profound, but the economic framework for understanding it is surprisingly simple.

    Major retailers are predicting everything from when their customers are pregnant to when they want a new pair of Chuck Taylors. It's a brave new world where seemingly meaningless data can be transformed into valuable insight to drive smart business decisions.But how does one exactly do data science? Do you have to hire one of these priests of the dark arts, the "data scientist," to extract this gold from your data? Nope.Data science is little more than using straight-forward steps to process raw data into actionable insight. And in Data Smart, author and data scientist John Foreman will show you how that's done within the familiar environment of a spreadsheet. Why a spreadsheet? It's comfortable! You get to look at the data every step of the way, building confidence as you learn the tricks of the trade. Plus, spreadsheets are a vendor-neutral place to learn data science without the hype. But don't let the Excel sheets fool you. This is a book for those serious about learning the analytic techniques, the math and the magic, behind big data.Each chapter will cover a different technique in a spreadsheet so you can follow along: - Mathematical optimization, including non-linear programming and genetic algorithms- Clustering via k-means, spherical k-means, and graph modularity- Data mining in graphs, such as outlier detection- Supervised AI through logistic regression, ensemble models, and bag-of-words models- Forecasting, seasonal adjustments, and prediction intervals through monte carlo simulation- Moving from spreadsheets into the R programming languageYou get your hands dirty as you work alongside John through each technique. But never fear, the topics are readily applicable and the author laces humor throughout. You'll even learn what a dead squirrel has to do with optimization modeling, which you no doubt are dying to know.

    Introduction to Artificial Intelligence presents an introduction to the science of reasoning processes in computers, and the research approaches and results of the past two decades. You'll find lucid, easy-to-read coverage of problem-solving methods, representation and models, game playing, automated understanding of natural languages, heuristic search theory, robot systems, heuristic scene analysis and specific artificial-intelligence accomplishments. Related subjects are also included: predicate-calculus theorem proving, machine architecture, psychological simulation, automatic programming, novel software techniques, industrial automation and much more.A supplementary section updates the original book with major research from the decade 1974-1984. Abundant illustrations, diagrams and photographs enhance the text, and challenging practice exercises at the end of each chapter test the student's grasp of each subject.The combination of introductory and advanced material makes Introduction to Artificial Intelligence ideal for both the layman and the student of mathematics and computer science. For anyone interested in the nature of thought, it will inspire visions of what computer technology might produce tomorrow.

    This book covers relevant geometric principles and how to represent objects algebraically so they can be computed and applied. Recent major developments in the theory and practice of scene reconstruction are described in detail in a unified framework. Richard Hartley and Andrew Zisserman provide comprehensive background material and explain how to apply the methods and implement the algorithms. First Edition HB (2000): 0-521-62304-9

    With it has come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics. It should be a valuable resource for statisticians and anyone interested in data mining in science or industry. The book's coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting—the first comprehensive treatment of this topic in any book. Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. They are prominent researchers in this area: Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie wrote much of the statistical modeling software in S-PLUS and invented principal curves and surfaces. Tibshirani proposed the Lasso and is co-author of the very successful An Introduction to the Bootstrap. Friedman is the co-inventor of many data-mining tools including CART, MARS, and projection pursuit.

    Whether you use Linux, Unix, or Windows, this newly revised edition describes the essential practices previously handed down only from mentor to protege. This wonderfully lucid, often funny cornucopia of information introduces beginners to advanced frameworks valuable for their entire career, yet is structured to help even the most advanced experts through difficult projects.The book's four major sections build your knowledge with the foundational elements of system administration. These sections guide you through better techniques for upgrades and change management, catalog best practices for IT services, and explore various management topics. Chapters are divided into The Basics and The Icing. When you get the Basics right it makes every other aspect of the job easier--such as automating the right things first. The Icing sections contain all the powerful things that can be done on top of the basics to wow customers and managers.Inside, you'll find advice on topics such asThe key elements your networks and systems need in order to make all other services run better Building and running reliable, scalable services, including web, storage, email, printing, and remote access Creating and enforcing security policies Upgrading multiple hosts at one time without creating havoc Planning for and performing flawless scheduled maintenance windows Managing superior helpdesks and customer care Avoiding the -temporary fix- trap Building data centers that improve server uptime Designing networks for speed and reliability Web scaling and security issues Why building a backup system isn't about backups Monitoring what you have and predicting what you will need How technically oriented workers can maintain their job's technical focus (and avoid an unwanted management role) Technical management issues, including morale, organization building, coaching, and maintaining positive visibility Personal skill techniques, including secrets for getting more done each day, ethical dilemmas, managing your boss, and loving your job System administration salary negotiation It's no wonder the first edition received Usenix SAGE's 2005 Outstanding Achievement Award!This eagerly anticipated second edition updates this time-proven classic:Chapters reordered for easier navigationThousands of updates and clarifications based on reader feedbackPlus three entirely new chapters: Web Services, Data Storage, and Documentation

    There can be so much information available that you can't even decide where to start. Or worse, you start down the path of learning and quickly discover too many concepts, commands, and nuances that aren't explained. This kind of experience is frustrating and leaves you with more questions than answers.Python Programming for Beginners doesn't make any assumptions about your background or knowledge of Python or computer programming. You need no prior knowledge to benefit from this book. You will be guided step by step using a logical and systematic approach. As new concepts, commands, or jargon are encountered they are explained in plain language, making it easy for anyone to understand. Here is what you will learn by reading Python Programming for Beginners: When to use Python 2 and when to use Python 3. How to install Python on Windows, Mac, and Linux. Screenshots included. How to prepare your computer for programming in Python. The various ways to run a Python program on Windows, Mac, and Linux. Suggested text editors and integrated development environments to use when coding in Python. How to work with various data types including strings, lists, tuples, dictionaries, booleans, and more. What variables are and when to use them. How to perform mathematical operations using Python. How to capture input from a user. Ways to control the flow of your programs. The importance of white space in Python. How to organize your Python programs -- Learn what goes where. What modules are, when you should use them, and how to create your own. How to define and use functions. Important built-in Python functions that you'll use often. How to read from and write to files. The difference between binary and text files. Various ways of getting help and find Python documentation. Much more... Every single code example in the book is available to download, providing you with all the Python code you need at your fingertips! Scroll up, click the Buy Now With 1 Click button and get started learning Python today!