These topics lie at the heart of many exciting areas of contemporary science and engineering - communication, signal processing, data mining, machine learning, pattern recognition, computational neuroscience, bioinformatics, and cryptography. This textbook introduces theory in tandem with applications. Information theory is taught alongside practical communication systems, such as arithmetic coding for data compression and sparse-graph codes for error-correction. A toolbox of inference techniques, including message-passing algorithms, Monte Carlo methods, and variational approximations, are developed alongside applications of these tools to clustering, convolutional codes, independent component analysis, and neural networks. The final part of the book describes the state of the art in error-correcting codes, including low-density parity-check codes, turbo codes, and digital fountain codes -- the twenty-first century standards for satellite communications, disk drives, and data broadcast. Richly illustrated, filled with worked examples and over 400 exercises, some with detailed solutions, David MacKay's groundbreaking book is ideal for self-learning and for undergraduate or graduate courses. Interludes on crosswords, evolution, and sex provide entertainment along the way. In sum, this is a textbook on information, communication, and coding for a new generation of students, and an unparalleled entry point into these subjects for professionals in areas as diverse as computational biology, financial engineering, and machine learning.

    The artificial intelligence system, created by DeepMind, had been fed nothing but the rules of the Royal Game when it beat the world’s strongest chess engine in a prolonged match. The selection of ten games published in December 2017 created a worldwide sensation: how was it possible to play in such a brilliant and risky style and not lose a single game against an opponent of superhuman strength?For Game Changer, Matthew Sadler and Natasha Regan investigated more than two thousand previously unpublished games by AlphaZero. They also had unparalleled access to its team of developers and were offered a unique look ‘under the bonnet’ to grasp the depth and breadth of AlphaZero’s search. Sadler and Regan reveal its thinking process and tell the story of the human motivation and the techniques that created AlphaZero.Game Changer also presents a collection of lucidly explained chess games of astonishing quality. Both professionals and club players will improve their game by studying AlphaZero’s stunning discoveries in every field that matters: opening preparation, piece mobility, initiative, attacking techniques, long-term sacrifices and much more.The story of AlphaZero has a wider impact. Game Changer offers intriguing insights into the opportunities and horizons of Artificial Intelligence. Not just in solving games, but in providing solutions for a wide variety of challenges in society.With a foreword by former World Chess Champion Garry Kasparov and an introduction by DeepMind CEO Demis Hassabis.Matthew Sadler (1974) is a Grandmaster who twice won the British Championship and was awarded an individual Gold Medal at the 1996 Olympiad. He has authored several highly acclaimed books on chess and has been writing the famous ‘Sadler on Books’ column for New In Chess magazine for many years. Natasha Regan is a Women’s International Master from England who achieved a degree in mathematics from Cambridge University. Matthew Sadler and Natasha Regan won the English Chess Federation 2016 Book of the Award for their book Chess for Life.

    Each chapter is relatively self-contained and can be used as a unit of study. The algorithms are described in English and in a pseudocode designed to be readable by anyone who has done a little programming. The explanations have been kept elementary without sacrificing depth of coverage or mathematical rigor.

    With this book’s easy step-by-step process, you’ll quickly learn how to build and maintain a complete Access database, using Access 2013’s new, simpler user interface and templates. You also get practices and tips from the pros for good database design—ideal whether you’re using Access for school, business, or at home.The important stuff you need to know:Build a database with ease. Store information to track numbers, products, documents, and more.Customize the interface. Build your own forms to make data entry a snap.Find what you need fast. Search, sort, and summarize huge amounts of information.Put your data to use. Turn raw info into printed reports with attractive formatting.Share your data. Collaborate online with SharePoint and the Access web database.Dive into Access programming. Get tricks and techniques to automate common tasks.Create rich data connections. Build dynamic links with SQL Server, SharePoint, and other systems.

    This guide also helps you understand the many data-mining techniques in use today.Based on an MBA course Provost has taught at New York University over the past ten years, Data Science for Business provides examples of real-world business problems to illustrate these principles. You’ll not only learn how to improve communication between business stakeholders and data scientists, but also how participate intelligently in your company’s data science projects. You’ll also discover how to think data-analytically, and fully appreciate how data science methods can support business decision-making.Understand how data science fits in your organization—and how you can use it for competitive advantageTreat data as a business asset that requires careful investment if you’re to gain real valueApproach business problems data-analytically, using the data-mining process to gather good data in the most appropriate wayLearn general concepts for actually extracting knowledge from dataApply data science principles when interviewing data science job candidates

    The focus is on SQL-it covers all major SQL databases without getting lost in the details of any one specific product. Starting with the basics of indexing and the WHERE clause, SQL Performance Explained guides developers through all parts of an SQL statement and explains the pitfalls of object-relational mapping (ORM) tools like Hibernate. Topics covered include: Using multi-column indexes; Correctly applying SQL functions; Efficient use of LIKE queries; Optimizing join operations; Clustering data to improve performance; Pipelined execution of ORDER BY and GROUP BY; Getting the best performance for pagination queries; Understanding the scalability of databases. Its systematic structure makes SQL Performance Explained both a textbook and a reference manual that should be on every developer's bookshelf.

    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

    Mitchell covers the field of machine learning, the study of algorithms that allow computer programs to automatically improve through experience and that automatically infer general laws from specific data.

    Ready to take JavaScript beyond the browser, explore dynamic languages features and embrace evented programming? Explore the fun, growing repository of Node modules provided by npm. Work with multiple protocols, load-balanced RESTful web services, express, 0MQ, Redis, CouchDB, and more. Develop production-grade Node applications fast. JavaScript is the backbone of the modern web, powering nearly every web app's user interface. Node.js is JavaScript for the server. This book shows you how to develop small, fast, low-profile, useful, networked applications. You'll write asynchronous, non-blocking code using Node's style and patterns. You'll cluster and load balance your services with Node core features and third-party tools. You'll work with many protocols, creating RESTful web services, TCP socket clients and servers, and more. This short book packs a hefty dose of Node.js. You'll test your code's functionality and performance under load. You'll learn important aspects of Node development--from its architecture and core, to its ecosystem of third-party modules. You'll discover how Node pairs a server-side event loop with a JavaScript runtime to produce screaming fast, non-blocking concurrency. Through a series of practical programming domains, you'll use the latest available ECMAScript Harmony features and harness key Node classes such as EventEmitter and Stream. Throughout the book, you'll develop real programs that are small, fast, low-profile, and useful. Get ready to join a smart community that's rapidly advancing the state of the art in web development.What You Need: Latest stable release of Node.js, this book was written with 0.12.x in mind. The 0MQ (ZeroMQ) library, version 3.2 or higher.

    Deep learning networks can play poker better than professional poker players and defeat a world champion at Go. In this book, Terry Sejnowski explains how deep learning went from being an arcane academic field to a disruptive technology in the information economy.Sejnowski played an important role in the founding of deep learning, as one of a small group of researchers in the 1980s who challenged the prevailing logic-and-symbol based version of AI. The new version of AI Sejnowski and others developed, which became deep learning, is fueled instead by data. Deep networks learn from data in the same way that babies experience the world, starting with fresh eyes and gradually acquiring the skills needed to navigate novel environments. Learning algorithms extract information from raw data; information can be used to create knowledge; knowledge underlies understanding; understanding leads to wisdom. Someday a driverless car will know the road better than you do and drive with more skill; a deep learning network will diagnose your illness; a personal cognitive assistant will augment your puny human brain. It took nature many millions of years to evolve human intelligence; AI is on a trajectory measured in decades. Sejnowski prepares us for a deep learning future.

    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

    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.

    This book focuses on practical algorithms that have been used to solve key problems in data mining and which can be used on even the largest datasets. It begins with a discussion of the map-reduce framework, an important tool for parallelizing algorithms automatically. The authors explain the tricks of locality-sensitive hashing and stream processing algorithms for mining data that arrives too fast for exhaustive processing. The PageRank idea and related tricks for organizing the Web are covered next. Other chapters cover the problems of finding frequent itemsets and clustering. The final chapters cover two applications: recommendation systems and Web advertising, each vital in e-commerce. Written by two authorities in database and Web technologies, this book is essential reading for students and practitioners alike.

    In this one-of-a-kind text, author V. Anton Spraul breaks down the ways that programmers solve problems and teaches you what other introductory books often ignore: how to Think Like a Programmer. Each chapter tackles a single programming concept, like classes, pointers, and recursion, and open-ended exercises throughout challenge you to apply your knowledge. You'll also learn how to:Split problems into discrete components to make them easier to solve Make the most of code reuse with functions, classes, and libraries Pick the perfect data structure for a particular job Master more advanced programming tools like recursion and dynamic memory Organize your thoughts and develop strategies to tackle particular types of problems Although the book's examples are written in C++, the creative problem-solving concepts they illustrate go beyond any particular language; in fact, they often reach outside the realm of computer science. As the most skillful programmers know, writing great code is a creative art—and the first step in creating your masterpiece is learning to Think Like a Programmer.

    You'll also learn how to make your programs interactive and how to test your code safely before adding it to a project. In the second half of the book, you'll put your new knowledge into practice with three substantial projects: a Space Invaders-inspired arcade game, data visualizations with Python's super-handy libraries, and a simple web app you can deploy online.As you work through Python Crash Course, you'll learn how to: Use powerful Python libraries and tools, including matplotlib, NumPy, and PygalMake 2D games that respond to keypresses and mouse clicks, and that grow more difficult as the game progressesWork with data to generate interactive visualizationsCreate and customize simple web apps and deploy them safely onlineDeal with mistakes and errors so you can solve your own programming problemsIf you've been thinking seriously about digging into programming, Python Crash Course will get you up to speed and have you writing real programs fast. Why wait any longer? Start your engines and code!