More than any other language, Python was created with the philosophy of simplicity and parsimony. Now 25 years old, Python has become the primary or secondary language (after SQL) for many business users. With popularity comes diversity--and possibly dilution.This guide, collaboratively written by over a hundred members of the Python community, describes best practices currently used by package and application developers. Unlike other books for this audience, The Hitchhiker's Guide is light on reusable code and heavier on design philosophy, directing the reader to excellent sources that already exist.

    Its world-class authors provide guidance on all aspects of Bayesian data analysis and include examples of real statistical analyses, based on their own research, that demonstrate how to solve complicated problems. Changes in the new edition include:Stronger focus on MCMC Revision of the computational advice in Part III New chapters on nonlinear models and decision analysis Several additional applied examples from the authors' recent research Additional chapters on current models for Bayesian data analysis such as nonlinear models, generalized linear mixed models, and more Reorganization of chapters 6 and 7 on model checking and data collectionBayesian computation is currently at a stage where there are many reasonable ways to compute any given posterior distribution. However, the best approach is not always clear ahead of time. Reflecting this, the new edition offers a more pluralistic presentation, giving advice on performing computations from many perspectives while making clear the importance of being aware that there are different ways to implement any given iterative simulation computation. The new approach, additional examples, and updated information make Bayesian Data Analysis an excellent introductory text and a reference that working scientists will use throughout their professional life.

    Theoretical, yet applied. Statistics for Business and Economics, Eleventh Edition, gives you the best of both worlds. Using a rich array of applications from a variety of industries, McClave/Sincich/Benson clearly demonstrates how to use statistics effectively in a business environment.The book focuses on developing statistical thinking so the reader can better assess the credibility and value of inferences made from data. As consumers and future producers of statistical inferences, readers are introduced to a wide variety of data collection and analysis techniques to help them evaluate data and make informed business decisions. As with previous editions, this revision offers an abundance of applications with many new and updated exercises that draw on real business situations and recent economic events. The authors assume a background of basic algebra.

    Most of the time this math works quietly behind the scenes . . . until it doesn't. All sorts of seemingly innocuous mathematical mistakes can have significant consequences.Math is easy to ignore until a misplaced decimal point upends the stock market, a unit conversion error causes a plane to crash, or someone divides by zero and stalls a battleship in the middle of the ocean.Exploring and explaining a litany of glitches, near misses, and mathematical mishaps involving the internet, big data, elections, street signs, lotteries, the Roman Empire, and an Olympic team, Matt Parker uncovers the bizarre ways math trips us up, and what this reveals about its essential place in our world. Getting it wrong has never been more fun.

    Scripting is a way to harness and customize the power of any Unix system, and it's an essential skill for any Unix users, including system administrators and professional OS X developers. But beneath this simple promise lies a treacherous ocean of variations in Unix commands and standards.bash Cookbook teaches shell scripting the way Unix masters practice the craft. It presents a variety of recipes and tricks for all levels of shell programmers so that anyone can become a proficient user of the most common Unix shell -- the bash shell -- and cygwin or other popular Unix emulation packages. Packed full of useful scripts, along with examples that explain how to create better scripts, this new cookbook gives professionals and power users everything they need to automate routine tasks and enable them to truly manage their systems -- rather than have their systems manage them.

    do it? How should you flip your mattress to get the maximum wear out of it? How does Google search the Internet? How many people should you date before settling down? Believe it or not, math plays a crucial role in answering all of these questions and more.Math underpins everything in the cosmos, including us, yet too few of us understand this universal language well enough to revel in its wisdom, its beauty — and its joy. This deeply enlightening, vastly entertaining volume translates math in a way that is at once intelligible and thrilling. Each trenchant chapter of The Joy of x offers an “aha!” moment, starting with why numbers are so helpful, and progressing through the wondrous truths implicit in π, the Pythagorean theorem, irrational numbers, fat tails, even the rigors and surprising charms of calculus. Showing why he has won awards as a professor at Cornell and garnered extensive praise for his articles about math for the New York Times, Strogatz presumes of his readers only curiosity and common sense. And he rewards them with clear, ingenious, and often funny explanations of the most vital and exciting principles of his discipline.Whether you aced integral calculus or aren’t sure what an integer is, you’ll find profound wisdom and persistent delight in The Joy of x.

    according to an artificial intelligence trained by scientist Janelle Shane, creator of the popular blog "AI Weirdness." She creates silly AIs that learn how to name paint colors, create the best recipes, and even flirt (badly) with humans--all to understand the technology that governs so much of our daily lives.We rely on AI every day for recommendations, for translations, and to put cat ears on our selfie videos. We also trust AI with matters of life and death, on the road and in our hospitals. But how smart is AI really, and how does it solve problems, understand humans, and even drive self-driving cars?Shane delivers the answers to every AI question you've ever asked, and some you definitely haven't--like, how can a computer design the perfect sandwich? What does robot-generated Harry Potter fan-fiction look like? And is the world's best Halloween costume really "Vampire Hog Bride"?In this smart, often hilarious introduction to the most interesting science of our time, Shane shows how these programs learn, fail, and adapt--and how they reflect the best and worst of humanity. You Look Like a Thing and I Love You is the perfect book for anyone curious about what the robots in our lives are thinking.

    If you're working with a relational database--whether you're writing applications, performing administrative tasks, or generating reports--you need to know how to interact with your data. Even if you are using a tool that generates SQL for you, such as a reporting tool, there may still be cases where you need to bypass the automatic generation feature and write your own SQL statements.To help you attain this fundamental SQL knowledge, look to "Learning SQL," an introductory guide to SQL, designed primarily for developers just cutting their teeth on the language."Learning SQL" moves you quickly through the basics and then on to some of the more commonly used advanced features. Among the topics discussed: The history of the computerized databaseSQL Data Statements--those used to create, manipulate, and retrieve data stored in your database; example statements include select, update, insert, and deleteSQL Schema Statements--those used to create database objects, such as tables, indexes, and constraintsHow data sets can interact with queriesThe importance of subqueriesData conversion and manipulation via SQL's built-in functionsHow conditional logic can be used in Data StatementsBest of all, "Learning SQL" talks to you in a real-world manner, discussing various platform differences that you're likely to encounter and offering a series of chapter exercises that walk you through the learning process. Whenever possible, the book sticks to the features included in the ANSI SQL standards. This means you'll be able to apply what you learn to any of several different databases; the book covers MySQL, Microsoft SQL Server, and Oracle Database, but the features and syntax should apply just as well (perhaps with some tweaking) to IBM DB2, Sybase Adaptive Server, and PostgreSQL.Put the power and flexibility of SQL to work. With "Learning SQL" you can master this important skill and know that the SQL statements you write are indeed correct.

    But what do we really know about the design process? What leads to effective, elegant designs? The Design of Design addresses these questions. These new essays by Fred Brooks contain extraordinary insights for designers in every discipline. Brooks pinpoints constants inherent in all design projects and uncovers processes and patterns likely to lead to excellence. Drawing on conversations with dozens of exceptional designers, as well as his own experiences in several design domains, Brooks observes that bold design decisions lead to better outcomes. The author tracks the evolution of the design process, treats collaborative and distributed design, and illuminates what makes a truly great designer. He examines the nuts and bolts of design processes, including budget constraints of many kinds, aesthetics, design empiricism, and tools, and grounds this discussion in his own real-world examples--case studies ranging from home construction to IBM's Operating System/360. Throughout, Brooks reveals keys to success that every designer, design project manager, and design researcher should know.

    He sifts through over 30,000 survey submissions to uncover the world’s favourite number, and meets a mathematician who looks for universes in his garage. He attends the World Mathematical Congress in India, and visits the engineer who designed the first roller-coaster loop. Get hooked on math as Alex delves deep into humankind’s turbulent relationship with numbers, and reveals how they have shaped the world we live in.

    With it, you'll learn how to write Python programs that work with large collections of unstructured text. You'll access richly annotated datasets using a comprehensive range of linguistic data structures, and you'll understand the main algorithms for analyzing the content and structure of written communication.Packed with examples and exercises, Natural Language Processing with Python will help you: Extract information from unstructured text, either to guess the topic or identify "named entities" Analyze linguistic structure in text, including parsing and semantic analysis Access popular linguistic databases, including WordNet and treebanks Integrate techniques drawn from fields as diverse as linguistics and artificial intelligenceThis book will help you gain practical skills in natural language processing using the Python programming language and the Natural Language Toolkit (NLTK) open source library. If you're interested in developing web applications, analyzing multilingual news sources, or documenting endangered languages -- or if you're simply curious to have a programmer's perspective on how human language works -- you'll find Natural Language Processing with Python both fascinating and immensely useful.

    Building on the field of mathematical statistics, probabilistic robotics endows robots with a new level of robustness in real-world situations. This book introduces the reader to a wealth of techniques and algorithms in the field. All algorithms are based on a single overarching mathematical foundation. Each chapter provides example implementations in pseudo code, detailed mathematical derivations, discussions from a practitioner's perspective, and extensive lists of exercises and class projects. The book's Web site, www.probabilistic-robotics.org, has additional material. The book is relevant for anyone involved in robotic software development and scientific research. It will also be of interest to applied statisticians and engineers dealing with real-world sensor data.

    The book is broken into two parts, the first deals with security-related reverse engineering and the second explores the more practical aspects of reverse engineering. In addition, the author explains how to reverse engineer a third-party software library to improve interfacing and how to reverse engineer a competitor's software to build a better product. * The first popular book to show how software reverse engineering can help defend against security threats, speed up development, and unlock the secrets of competitive products * Helps developers plug security holes by demonstrating how hackers exploit reverse engineering techniques to crack copy-protection schemes and identify software targets for viruses and other malware * Offers a primer on advanced reverse-engineering, delving into disassembly-code-level reverse engineering-and explaining how to decipher assembly language

    To its adherents, it is an elegant statement about learning from experience. To its opponents, it is subjectivity run amok.In the first-ever account of Bayes' rule for general readers, Sharon Bertsch McGrayne explores this controversial theorem and the human obsessions surrounding it. She traces its discovery by an amateur mathematician in the 1740s through its development into roughly its modern form by French scientist Pierre Simon Laplace. She reveals why respected statisticians rendered it professionally taboo for 150 years—at the same time that practitioners relied on it to solve crises involving great uncertainty and scanty information (Alan Turing's role in breaking Germany's Enigma code during World War II), and explains how the advent of off-the-shelf computer technology in the 1980s proved to be a game-changer. Today, Bayes' rule is used everywhere from DNA de-coding to Homeland Security.Drawing on primary source material and interviews with statisticians and other scientists, The Theory That Would Not Die is the riveting account of how a seemingly simple theorem ignited one of the greatest controversies of all time.

    During that week, you will learn almost everything modern machine learning has to offer. The author and other practitioners have spent years learning these concepts.Companion wiki — the book has a continuously updated wiki that extends some book chapters with additional information: Q&A, code snippets, further reading, tools, and other relevant resources.Flexible price and formats — choose from a variety of formats and price options: Kindle, hardcover, paperback, EPUB, PDF. If you buy an EPUB or a PDF, you decide the price you pay!Read first, buy later — download book chapters for free, read them and share with your friends and colleagues. Only if you liked the book or found it useful in your work, study or business, then buy it.