A classic title on the PC revolution originally published in 1986. Featuring Bill Gates, Andy Hertzfeld, Charles Simonyi, Ray Ozzie, Michael Hawley and many more.

    However, these activities can be viewed as two facets of the same field, and together they have undergone substantial development over the past ten years. In particular, Bayesian methods have grown from a specialist niche to become mainstream, while graphical models have emerged as a general framework for describing and applying probabilistic models. Also, the practical applicability of Bayesian methods has been greatly enhanced through the development of a range of approximate inference algorithms such as variational Bayes and expectation propagation. Similarly, new models based on kernels have had a significant impact on both algorithms and applications. This new textbook reflects these recent developments while providing a comprehensive introduction to the fields of pattern recognition and machine learning. It is aimed at advanced undergraduates or first-year PhD students, as well as researchers and practitioners, and assumes no previous knowledge of pattern recognition or machine learning concepts. Knowledge of multivariate calculus and basic linear algebra is required, and some familiarity with probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.

     -Byte, September 1995 I can't begin to tell you how many pleasurable hours of study and recreation they have afforded me! I have pored over them in cars, restaurants, at work, at home... and even at a Little League game when my son wasn't in the line-up. -Charles Long If you think you're a really good programmer... read [Knuth's] Art of Computer Programming... You should definitely send me a resume if you can read the whole thing. -Bill Gates It's always a pleasure when a problem is hard enough that you have to get the Knuths off the shelf. I find that merely opening one has a very useful terrorizing effect on computers. -Jonathan Laventhol This first volume in the series begins with basic programming concepts and techniques, then focuses more particularly on information structures-the representation of information inside a computer, the structural relationships between data elements and how to deal with them efficiently. Elementary applications are given to simulation, numerical methods, symbolic computing, software and system design. Dozens of simple and important algorithms and techniques have been added to those of the previous edition. The section on mathematical preliminaries has been extensively revised to match present trends in research. Ebook (PDF version) produced by Mathematical Sciences Publishers (MSP), http: //msp.org

    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 most common operation done with sed is substitution, replacing one block of text with another. awk is a complete programming language. Unlike many conventional languages, awk is "data driven" -- you specify what kind of data you are interested in and the operations to be performed when that data is found. awk does many things for you, including automatically opening and closing data files, reading records, breaking the records up into fields, and counting the records. While awk provides the features of most conventional programming languages, it also includes some unconventional features, such as extended regular expression matching and associative arrays. sed & awk describes both programs in detail and includes a chapter of example sed and awk scripts. This edition covers features of sed and awk that are mandated by the POSIX standard. This most notably affects awk, where POSIX standardized a new variable, CONVFMT, and new functions, toupper() and tolower(). The CONVFMT variable specifies the conversion format to use when converting numbers to strings (awk used to use OFMT for this purpose). The toupper() and tolower() functions each take a (presumably mixed case) string argument and return a new version of the string with all letters translated to the corresponding case. In addition, this edition covers GNU sed, newly available since the first edition. It also updates the first edition coverage of Bell Labs nawk and GNU awk (gawk), covers mawk, an additional freely available implementation of awk, and briefly discusses three commercial versions of awk, MKS awk, Thompson Automation awk (tawk), and Videosoft (VSAwk).

    In addition to giving a strong foundation in the language itself, Lubanovic shows how to use it for a range of applications in business, science, and the arts, drawing on the rich collection of open source packages developed by Python fans.It's impressive how many commercial and production-critical programs are written now in Python. Developed to be easy to read and maintain, it has proven a boon to anyone who wants applications that are quick to write but robust and able to remain in production for the long haul.This book focuses on the current version of Python, 3.x, while including sidebars about important differences with 2.x for readers who may have to deal with programs in that version.

    If you have received one of these copies, please contact the Pragmatic Bookshelf at support@pragprog.com, and we will replace it for you.Algorithms and data structures are much more than abstract concepts. Mastering them enables you to write code that runs faster and more efficiently, which is particularly important for today's web and mobile apps. This book takes a practical approach to data structures and algorithms, with techniques and real-world scenarios that you can use in your daily production code. Graphics and examples make these computer science concepts understandable and relevant. You can use these techniques with any language; examples in the book are in JavaScript, Python, and Ruby.Use Big O notation, the primary tool for evaluating algorithms, to measure and articulate the efficiency of your code, and modify your algorithm to make it faster. Find out how your choice of arrays, linked lists, and hash tables can dramatically affect the code you write. Use recursion to solve tricky problems and create algorithms that run exponentially faster than the alternatives. Dig into advanced data structures such as binary trees and graphs to help scale specialized applications such as social networks and mapping software. You'll even encounter a single keyword that can give your code a turbo boost. Jay Wengrow brings to this book the key teaching practices he developed as a web development bootcamp founder and educator.Use these techniques today to make your code faster and more scalable.

    Why not learn to talk to your computer in its own language? Whether you want to write games, start a business, or you're just curious, learning to program is a great place to start. Plus, programming is fun!Hello World! provides a gentle but thorough introduction to the world of computer programming. It's written in language a 12-year-old can follow, but anyone who wants to learn how to program a computer can use it. Even adults. Written by Warren Sande and his son, Carter, and reviewed by professional educators, this book is kid-tested and parent-approved.You don't need to know anything about programming to use the book. But you should know the basics of using a computer--e-mail, surfing the web, listening to music, and so forth. If you can start a program and save a file, you should have no trouble using this book.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.

    Topics are broken down into three major conceptual pieces: Virtualization, Concurrency, and Persistence. Includes all major components of modern systems including scheduling, virtual memory management, disk subsystems and I/O, file systems, and even a short introduction to distributed systems.

    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.""

    With its simple commands, flags, and parameters, a well-formed command-line application is the quickest way to automate a backup, a build, or a deployment and simplify your life. With this book, you'll learn specific ways to write command-line applications that are easy to use, deploy, and maintain, using a set of clear best practices and the Ruby programming language. This book is designed to make any programmer or system administrator more productive in their job. Now updated for Ruby 2.Writing a command-line application that's self-documenting, robust, adaptable and forever useful is easier than you might think. Ruby is particularly suited to this task, because it combines high-level abstractions with "close to the metal" system interaction wrapped up in a concise, readable syntax. Plus, Ruby has the support of a rich ecosystem of open source tools and libraries.Ten insightful chapters each explain and demonstrate a command-line best practice. You'll see how to use these tools to elevate the lowliest automation script to a maintainable, polished application. You'll learn how to use free, open source parsers to create user-friendly command-line interfaces as well as command suites. You'll see how to use defaults to keep options simple for everyday users, while giving advanced users options for more complex tasks. There's no reason why a command-line application should lack documentation, whether it's part of a help command or a man page; you'll find out when and how to use both. Your journey from command-line novice to pro ends with a look at valuable approaches to testing your apps, and includes some fun techniques for outside-the-box, colorful interfaces that will delight your users.With Ruby, the command line is not dead. Long live the command line.

    Jon has done a wonderful job of updating the material. I am very impressed at how fresh the new examples seem." - Steve McConnell, author, Code CompleteWhen programmers list their favorite books, Jon Bentley's collection of programming pearls is commonly included among the classics. Just as natural pearls grow from grains of sand that irritate oysters, programming pearls have grown from real problems that have irritated real programmers. With origins beyond solid engineering, in the realm of insight and creativity, Bentley's pearls offer unique and clever solutions to those nagging problems. Illustrated by programs designed as much for fun as for instruction, the book is filled with lucid and witty descriptions of practical programming techniques and fundamental design principles. It is not at all surprising that Programming Pearls has been so highly valued by programmers at every level of experience. In this revision, the first in 14 years, Bentley has substantially updated his essays to reflect current programming methods and environments. In addition, there are three new essays on (1) testing, debugging, and timing; (2) set representations; and (3) string problems. All the original programs have been rewritten, and an equal amount of new code has been generated. Implementations of all the programs, in C or C++, are now available on the Web.What remains the same in this new edition is Bentley's focus on the hard core of programming problems and his delivery of workable solutions to those problems. Whether you are new to Bentley's classic or are revisiting his work for some fresh insight, this book is sure to make your own list of favorites.

    This causes real problems for everyday users who need to make sense of all the information available, and for software engineers who want to make their text-based applications more useful and user-friendly. Whether building a search engine for a corporate website, automatically organizing email, or extracting important nuggets of information from the news, dealing with unstructured text can be daunting.Taming Text is a hands-on, example-driven guide to working with unstructured text in the context of real-world applications. It explores how to automatically organize text, using approaches such as full-text search, proper name recognition, clustering, tagging, information extraction, and summarization. This book gives examples illustrating each of these topics, as well as the foundations upon which they are built.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.

    You can think of Python as your tool to solve problems that are far beyond the capability of a spreadsheet. It is an easy-to-use and easy-to learn programming language that is freely available on Windows, Macintosh, and Linux computers. There are free downloadable copies of this book in various electronic formats and a self-paced free online course where you can explore the course materials. All the supporting materials for the book are available under open and remixable licenses. This book is designed to teach people to program even if they have no prior experience.

    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.