This tenth edition reflects the needs of students and assures the development of practical and marketable skills. It helps them learn: how to query data and obtain results, by presenting the SQL Select. It provides a framework to help students learn this material.

    Like previous versions, this guide shows you step-by-step techniques on using MVC to best advantage, with plenty of practical tutorials to illustrate the concepts. It covers controllers, views, and models; forms and HTML helpers; data annotation and validation; membership, authorization, and security.MVC 5, the latest version of MVC, adds sophisticated features such as single page applications, mobile optimization, and adaptive rendering A team of top Microsoft MVP experts, along with visionaries in the field, provide practical advice on basic and advanced MVC topics Covers controllers, views, models, forms, data annotations, authorization and security, Ajax, routing, ASP.NET web API, dependency injection, unit testing, real-world application, and much more Professional ASP.NET MVC 5 is the comprehensive resource you need to make the best use of the updated Model-View-Controller technology.

    The following day, her story began appearing in Irish papers and soon after was splashed across the front page of the London Times, complete with a photo of Sarah and a caption calling her "brilliant." Just sixteen, she was a mathematician with an international reputation. IN CODE is a heartwarming story that will have readers cheering Sarah on. Originally published in England and cowritten with her mathematician father, David Flannery, IN CODE is "a wonderfully moving story about the thrill of the mathematical chase" (Nature) and "a paean to intellectual adventure" (Times Educational Supplement). A memoir in mathematics, it is all about how a girl next door, nurtured by her family, moved from the simple math puzzles that were the staple of dinnertime conversation to prime numbers, the Sieve of Eratosthenes, Fermat's Little Theorem, googols-and finally into her breathtaking algorithm. Parallel with each step is a modest girl's own self-discovery-her values, her burning curiosity, the joy of persistence, and, above all, her love for her family.

    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.

    What hope do we have against superintelligent machines? But we aren't really on the path to developing intelligent machines. In fact, we don't even know where that path might be.A tech entrepreneur and pioneering research scientist working at the forefront of natural language processing, Erik Larson takes us on a tour of the landscape of AI to show how far we are from superintelligence, and what it would take to get there. Ever since Alan Turing, AI enthusiasts have equated artificial intelligence with human intelligence. This is a profound mistake. AI works on inductive reasoning, crunching data sets to predict outcomes. But humans don't correlate data sets: we make conjectures informed by context and experience. Human intelligence is a web of best guesses, given what we know about the world. We haven't a clue how to program this kind of intuitive reasoning, known as abduction. Yet it is the heart of common sense. That's why Alexa can't understand what you are asking, and why AI can only take us so far.Larson argues that AI hype is both bad science and bad for science. A culture of invention thrives on exploring unknowns, not overselling existing methods. Inductive AI will continue to improve at narrow tasks, but if we want to make real progress, we will need to start by more fully appreciating the only true intelligence we know--our own.

    

    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.

    The book begins by introducing the R language, including the development environment. Focusing on practical solutions, the book also offers a crash course in practical statistics and covers elegant methods for dealing with messy and incomplete data using features of R.About the TechnologyR is a powerful language for statistical computing and graphics that can handle virtually any data-crunching task. It runs on all important platforms and provides thousands of useful specialized modules and utilities. This makes R a great way to get meaningful information from mountains of raw data.About the BookR in Action is a language tutorial focused on practical problems. It presents useful statistics examples and includes elegant methods for handling messy, incomplete, and non-normal data that are difficult to analyze using traditional methods. And statistical analysis is only part of the story. You'll also master R's extensive graphical capabilities for exploring and presenting data visually. 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. What's InsidePractical data analysis, step by stepInterfacing R with other softwareUsing R to visualize dataOver 130 graphsEight reference appendixes================================Table of ContentsPart I Getting startedIntroduction to RCreating a datasetGetting started with graphsBasic data managementAdvanced data managementPart II Basic methodsBasic graphsBasic statisticsPart III Intermediate methodsRegressionAnalysis of variancePower analysisIntermediate graphsRe-sampling statistics and bootstrappingPart IV Advanced methodsGeneralized linear modelsPrincipal components and factor analysisAdvanced methods for missing dataAdvanced graphics

    This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree- based methods, support vector machines, clustering, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform. Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.

    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.

    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.

    There is joy and beauty in mathematics, and in more than two dozen essays drawn from his popular “Good Math” blog, you’ll find concepts, proofs, and examples that are often surprising, counterintuitive, or just plain weird.Mark begins his journey with the basics of numbers, with an entertaining trip through the integers and the natural, rational, irrational, and transcendental numbers. The voyage continues with a look at some of the oddest numbers in mathematics, including zero, the golden ratio, imaginary numbers, Roman numerals, and Egyptian and continuing fractions. After a deep dive into modern logic, including an introduction to linear logic and the logic-savvy Prolog language, the trip concludes with a tour of modern set theory and the advances and paradoxes of modern mechanical computing.If your high school or college math courses left you grasping for the inner meaning behind the numbers, Mark’s book will both entertain and enlighten you.

    No statistical knowledge is required, and your programming skills can range from hobbyist to pro.Along the way, you'll learn about functional and object-oriented programming, running mathematical simulations, and rearranging complex data into simpler, more useful formats. You'll also learn to: Create artful graphs to visualize complex data sets and functions Write more efficient code using parallel R and vectorization Interface R with C/C++ and Python for increased speed or functionality Find new R packages for text analysis, image manipulation, and more Squash annoying bugs with advanced debugging techniques Whether you're designing aircraft, forecasting the weather, or you just need to tame your data, The Art of R Programming is your guide to harnessing the power of statistical computing.

    Now he's sharing his collection of antipatterns--the most common errors he's identified in those thousands of requests for help. Most developers aren't SQL experts, and most of the SQL that gets used is inefficient, hard to maintain, and sometimes just plain wrong. This book shows you all the common mistakes, and then leads you through the best fixes. What's more, it shows you what's behind these fixes, so you'll learn a lot about relational databases along the way. Each chapter in this book helps you identify, explain, and correct a unique and dangerous antipattern. The four parts of the book group the anti​patterns in terms of logical database design, physical database design, queries, and application development. The chances are good that your application's database layer already contains problems such as Index Shotgun, Keyless Entry, Fear of the Unknown, and Spaghetti Query. This book will help you and your team find them. Even better, it will also show you how to fix them, and how to avoid these and other problems in the future. SQL Antipatterns gives you a rare glimpse into an SQL expert's playbook. Now you can stamp out these common database errors once and for all. Whatever platform or programming language you use, whether you're a junior programmer or a Ph.D., SQL Antipatterns will show you how to design and build databases, how to write better database queries, and how to integrate SQL programming with your application like an expert. You'll also learn the best and most current technology for full-text search, how to design code that is resistant to SQL injection attacks, and other techniques for success.

    The vision behind Clojure is of a radically simple language framework holding together a sophisticated collection of programming features. Learning Clojure involves much more than just learning the mechanics of the language. To really get Clojure you need to understand the ideas underlying this structure of framework and features. You need this book: an accessible introduction to Clojure that focuses on the ideas behind the language as well as the practical details of writing code.