And then we wonder why software is notorious for being delivered late and full of bugs, while other engineers routinely deliver finished bridges, automobiles, electrical appliances, etc., on time and with only minor defects. This book sets out to redress this imbalance. Members of my advanced development team at Adobe who took the course based on the same material all benefited greatly from the time invested. It may appear as a highly technical text intended only for computer scientists, but it should be required reading for all practicing software engineers." --Martin Newell, Adobe Fellow"The book contains some of the most beautiful code I have ever seen." --Bjarne Stroustrup, Designer of C++"I am happy to see the content of Alex's course, the development and teaching of which I strongly supported as the CTO of Silicon Graphics, now available to all programmers in this elegant little book." --Forest Baskett, General Partner, New Enterprise Associates"Paul's patience and architectural experience helped to organize Alex's mathematical approach into a tightly-structured edifice--an impressive feat!" --Robert W. Taylor, Founder of Xerox PARC CSL and DEC Systems Research Center Elements of Programming provides a different understanding of programming than is presented elsewhere. Its major premise is that practical programming, like other areas of science and engineering, must be based on a solid mathematical foundation. The book shows that algorithms implemented in a real programming language, such as C++, can operate in the most general mathematical setting. For example, the fast exponentiation algorithm is defined to work with any associative operation. Using abstract algorithms leads to efficient, reliable, secure, and economical software.This is not an easy book. Nor is it a compilation of tips and tricks for incremental improvements in your programming skills. The book's value is more fundamental and, ultimately, more critical for insight into programming. To benefit fully, you will need to work through it from beginning to end, reading the code, proving the lemmas, and doing the exercises. When finished, you will see how the application of the deductive method to your programs assures that your system's software components will work together and behave as they must.The book presents a number of algorithms and requirements for types on which they are defined. The code for these descriptions--also available on the Web--is written in a small subset of C++ meant to be accessible to any experienced programmer. This subset is defined in a special language appendix coauthored by Sean Parent and Bjarne Stroustrup.Whether you are a software developer, or any other professional for whom programming is an important activity, or a committed student, you will come to understand what the book's experienced authors have been teaching and demonstrating for years--that mathematics is good for programming, and that theory is good for practice.

    Due to their monopoly position in web browsers, and the fact web browsers have spread from PCs to phones, tablets and TVs; their status will continue to grow and grow. Despite their success, many software engineers are apprehensive about JavaScript and HTML. This apprehensiveness is not completely unfounded; both JavaScript and HTML were rushed in their early years, and driven by commercial rather than engineering interests. As a result, many dubious features crept into these languages. Due to backwards compatibility concerns, most of these features still remain. In addition, many software engineers have used these languages without ever learning them. JavaScript and HTML have low barriers to entry, and this, along with their similarity to other languages, led many software engineers to conclude that there really was nothing much to learn. If you have not used JavaScript and HTML for a number of years, or if you are a programmer or software engineer using other languages, you may be surprised at what they now offer. Browser based web applications are now capable of matching or exceeding the sophistication and scale of traditional desktop applications. In order to create complex web applications however, it is essential to learn these languages. This book takes the point of view that once you have a strong grasp of the fundamentals, the details will take care of themselves. It will not present you with long lists of APIs, or intricate details of every attribute, these can be found in reference manuals. It will focus on the details of each language that are fundamental to understanding how they work. This book will guide you through the process of developing a web application using HTML5, Javascript, jQuery and CSS. It contains the following content: 1. An introduction to the HTML5 markup language, and how it differs from HTML4 and XHTML. 2. An introduction to JavaScript, including an in-depth look at its use of objects and functions, along with the design patterns that support the development of robust web applications. 3. An introduction to jQuery selection, traversal, manipulation and events. 4. An in-depth look at the Web storage and IndexedDB APIs for client side data storage. 5. A guide to implementing offline web applications with the Application Cache API. 6. An introduction to the ways JavaScript can interact with the users file-system using the FileReader API. 7. The use of Web Workers in a web application to execute algorithms on background threads. 8. An introduction to AJAX, and the jQuery API supporting AJAX. 9. An introduction to Server Sent Events and Web Sockets. All subjects are introduced in the context of a sample web application. This book is intended for anyone with at least a superficial knowledge of HTML and programming.

    Feynman gave his famous course on computation at the California Institute of Technology, he asked Tony Hey to adapt his lecture notes into a book. Although led by Feynman, the course also featured, as occasional guest speakers, some of the most brilliant men in science at that time, including Marvin Minsky, Charles Bennett, and John Hopfield. Although the lectures are now thirteen years old, most of the material is timeless and presents a “Feynmanesque” overview of many standard and some not-so-standard topics in computer science such as reversible logic gates and quantum computers.

    With the sophisticated algorithms in this book, you can write smart programs to access interesting datasets from other web sites, collect data from users of your own applications, and analyze and understand the data once you've found it.Programming Collective Intelligence takes you into the world of machine learning and statistics, and explains how to draw conclusions about user experience, marketing, personal tastes, and human behavior in general -- all from information that you and others collect every day. Each algorithm is described clearly and concisely with code that can immediately be used on your web site, blog, Wiki, or specialized application. This book explains:Collaborative filtering techniques that enable online retailers to recommend products or media Methods of clustering to detect groups of similar items in a large dataset Search engine features -- crawlers, indexers, query engines, and the PageRank algorithm Optimization algorithms that search millions of possible solutions to a problem and choose the best one Bayesian filtering, used in spam filters for classifying documents based on word types and other features Using decision trees not only to make predictions, but to model the way decisions are made Predicting numerical values rather than classifications to build price models Support vector machines to match people in online dating sites Non-negative matrix factorization to find the independent features in a dataset Evolving intelligence for problem solving -- how a computer develops its skill by improving its own code the more it plays a game Each chapter includes exercises for extending the algorithms to make them more powerful. Go beyond simple database-backed applications and put the wealth of Internet data to work for you. "Bravo! I cannot think of a better way for a developer to first learn these algorithms and methods, nor can I think of a better way for me (an old AI dog) to reinvigorate my knowledge of the details."-- Dan Russell, Google "Toby's book does a great job of breaking down the complex subject matter of machine-learning algorithms into practical, easy-to-understand examples that can be directly applied to analysis of social interaction across the Web today. If I had this book two years ago, it would have saved precious time going down some fruitless paths."-- Tim Wolters, CTO, Collective Intellect

    This is the first book that introduces Lisp as a language for the real world.Practical Common Lisp presents a thorough introduction to Common Lisp, providing you with an overall understanding of the language features and how they work. Over a third of the book is devoted to practical examples, such as the core of a spam filter and a web application for browsing MP3s and streaming them via the Shoutcast protocol to any standard MP3 client software (e.g., iTunes, XMMS, or WinAmp). In other "practical" chapters, author Peter Seibel demonstrates how to build a simple but flexible in-memory database, how to parse binary files, and how to build a unit test framework in 26 lines of code.

    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.

    In this book, you’ll learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch. If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with hacking skills you need to get started as a data scientist. Today’s messy glut of data holds answers to questions no one’s even thought to ask. This book provides you with the know-how to dig those answers out. Get a crash course in Python Learn the basics of linear algebra, statistics, and probability—and understand how and when they're used in data science Collect, explore, clean, munge, and manipulate data Dive into the fundamentals of machine learning Implement models such as k-nearest Neighbors, Naive Bayes, linear and logistic regression, decision trees, neural networks, and clustering Explore recommender systems, natural language processing, network analysis, MapReduce, and databases

    With this concise book, you will quickly gain working knowledge of Sinatra and its minimalist approach to building both standalone and modular web applications. Sinatra serves as a lightweight wrapper around Rack middleware, with syntax that maps closely to functions exposed by HTTP verbs, which makes it ideal for web services and APIs. If you have experience building applications with Ruby, you’ll quickly learn language fundamentals and see under-the-hood techniques, with the help of several practical examples. Then you’ll get hands-on experience with Sinatra by building your own blog engine. Learn Sinatra’s core concepts, and get started by building a simple application Create views, manage sessions, and work with Sinatra route definitions Become familiar with the language’s internals, and take a closer look at Rack Use different subclass methods for building flexible and robust architectures Put Sinatra to work: build a blog that takes advantage of service hooks provided by the GitHub API

    This report provides a brief yet comprehensive introduction to Python for engineers, researchers, and data scientists who are already familiar with another programming language.Author Jake VanderPlas, an interdisciplinary research director at the University of Washington, explains Python’s essential syntax and semantics, built-in data types and structures, function definitions, control flow statements, and more, using Python 3 syntax.You’ll explore:- Python syntax basics and running Python codeBasic semantics of Python variables, objects, and operators- Built-in simple types and data structures- Control flow statements for executing code blocks conditionally- Methods for creating and using reusable functionsIterators, list comprehensions, and generators- String manipulation and regular expressions- Python’s standard library and third-party modules- Python’s core data science tools- Recommended resources to help you learn more

    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.

    It is the only book that gives a full introduction to programming in MATLAB combined with an explanation of MATLAB's powerful functions. The book differs from other texts in that it teaches programming concepts and the use of the built-in functions in MATLAB simultaneously. It presents programming concepts and MATLAB built-in functions side-by-side, giving students the ability to program efficiently and exploit the power of MATLAB to solve problems. The systematic, step-by-step approach, building on concepts throughout the book, facilitates easier learning.Starting with basic programming concepts, such as variables, assignments, input/output, selection, and loop statements, problems are introduced and solved throughout the book. The book is organized into two parts. Part I covers the programming constructs and demonstrates programming versus efficient use of built-in functions to solve problems. Part II describes the applications, including plotting, image processing, and mathematics, needed in basic problem solving. The chapters feature sections called Quick Question! as well as practice problems designed to test knowledge about the material covered. Problems are solved using both The Programming Concept and The Efficient Method, which facilitates understanding the efficient ways of using MATLAB, and also the programming concepts used in these efficient functions and operators. There are also sections on 'common pitfalls' and 'programming guidelines' that direct students towards best practice.This book is ideal for engineers learning to program and model in MATLAB, as well as undergraduates in engineering and science taking a course on MATLAB.

    Everybody wants to learn them. You're not a programmer though. How is a non technical user going to learn how to program? You do want to use macros to make your work easier but are you really going to sit down with a huge programming textbook and work your way through every. single. boring. page? Like most people, you'll start with great enthusiasm and vigor but after a few chapters, the novelty wears off. It gets boring. I'm going to try and change that and make learning macro programming entertaining and accessible to non-techies. First of all, programming Excel macros is a huge topic. Let's eat the elephant one bite at a time. Instead of sitting down with a dry, heavy text, you will read very focused, to the point topics. You can then immediately use what you learned in the real world. This is the first lesson in the series. You will learn what macros are, how to access them, a tiny bit of programming theory (just so you have a clue as to what's going on) and how to record macros. As with all my other lessons, this one has a follow along workbook that you can use to work through the exercises. The images in the lessons are based on Excel 2013 for Windows.

    You have access to so much information - but where do you start and what is actually useful?! This particular book takes you right back to formula basics; exactly what a formula is, how to create one and what formulas can do. Then you'll learn about using functions in your formulas, a useful Excel feature which is designed to make your life easier. It will give you the starting blocks that you need in order to present and make sense of a spreadsheet in a quick and easy way that will give your confidence and career a boost. Formulae include: - Basic Arithmetic - SUM - IF - AND/OR - COUNT / COUNTA - AVERAGE / MAX / MIN - VLOOKUP / HLOOKUP - Bonus - Validation

    Hamilton was reshaping the image of the vampire with her own take on the vampire mythos in her Anita Blake, Vampire Hunter fantasy novel series. While Hamilton's work draws on traditional vampire and fairy lore, her interpretation of these subjects brought new dimensions to the genres, influencing the direction of urban fantasy over the past two decades.Reading Laurell K. Hamilton focuses upon Hamilton's two bestselling series, the Anita Blake, Vampire Hunter series and the Merry Gentry series. The volume is intended as a resource for leaders of book clubs or discussion groups, containing chapters that examine Hamilton's role in the current vampire literature craze, the themes and characters in her work, and responses to Hamilton on the Internet. The book also provides a brief overview of Hamilton's life.

    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