Major retailers are predicting everything from when their customers are pregnant to when they want a new pair of Chuck Taylors. It's a brave new world where seemingly meaningless data can be transformed into valuable insight to drive smart business decisions.But how does one exactly do data science? Do you have to hire one of these priests of the dark arts, the "data scientist," to extract this gold from your data? Nope.Data science is little more than using straight-forward steps to process raw data into actionable insight. And in Data Smart, author and data scientist John Foreman will show you how that's done within the familiar environment of a spreadsheet. Why a spreadsheet? It's comfortable! You get to look at the data every step of the way, building confidence as you learn the tricks of the trade. Plus, spreadsheets are a vendor-neutral place to learn data science without the hype. But don't let the Excel sheets fool you. This is a book for those serious about learning the analytic techniques, the math and the magic, behind big data.Each chapter will cover a different technique in a spreadsheet so you can follow along: - Mathematical optimization, including non-linear programming and genetic algorithms- Clustering via k-means, spherical k-means, and graph modularity- Data mining in graphs, such as outlier detection- Supervised AI through logistic regression, ensemble models, and bag-of-words models- Forecasting, seasonal adjustments, and prediction intervals through monte carlo simulation- Moving from spreadsheets into the R programming languageYou get your hands dirty as you work alongside John through each technique. But never fear, the topics are readily applicable and the author laces humor throughout. You'll even learn what a dead squirrel has to do with optimization modeling, which you no doubt are dying to know.

    We are living in an age where data comes in abundance, and thanks to the self-learning algorithms from the field of machine learning, we can turn this data into knowledge. Automated speech recognition on our smart phones, web search engines, e-mail spam filters, the recommendation systems of our favorite movie streaming services – machine learning makes it all possible.Thanks to the many powerful open-source libraries that have been developed in recent years, machine learning is now right at our fingertips. Python provides the perfect environment to build machine learning systems productively.This book will teach you the fundamentals of machine learning and how to utilize these in real-world applications using Python. Step-by-step, you will expand your skill set with the best practices for transforming raw data into useful information, developing learning algorithms efficiently, and evaluating results.You will discover the different problem categories that machine learning can solve and explore how to classify objects, predict continuous outcomes with regression analysis, and find hidden structures in data via clustering. You will build your own machine learning system for sentiment analysis and finally, learn how to embed your model into a web app to share with the world

    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 book covers a range of genres, including action, adventure, and puzzle games complete with professional quality sound effects and visuals. It discusses game design theory and features practical examples of how this can be applied to making games that are more fun to play. Game Maker allows games to be created using a simple drag-and-drop interface, so you don't need to have any prior coding experience. It includes an optional programming language for adding advanced features to your games, when you feel ready to do so. You can obtain more information by visiting book.gamemaker.nl. The authors include the creator of the Game Maker tool and a former professional game programmer, so you'll glean understanding from their expertise. The book also includes a DVD containing Game Maker software and all of the game projects that are created in the book—plus a host of professional-quality graphics and sound effects that you can use in your own games.

    With IDA Pro, you live in a source code-optional world. IDA can automatically analyze the millions of opcodes that make up an executable and present you with a disassembly. But at that point, your work is just beginning. With The IDA Pro Book, you'll learn how to turn that mountain of mnemonics into something you can actually use.Hailed by the creator of IDA Pro as the "long-awaited" and "information-packed" guide to IDA, The IDA Pro Book covers everything from the very first steps to advanced automation techniques. While other disassemblers slow your analysis with inflexibility, IDA invites you to customize its output for improved readability and usefulness. You'll save time and effort as you learn to:Identify known library routines, so you can focus your analysis on other areas of the code Extend IDA to support new processors and filetypes, making disassembly possible for new or obscure architectures Explore popular plug-ins that make writing IDA scripts easier, allow collaborative reverse engineering, and much more Utilize IDA's built-in debugger to tackle obfuscated code that would defeat a stand-alone disassembler You'll still need serious assembly skills to tackle the toughest executables, but IDA makes things a lot easier. Whether you're analyzing the software on a black box or conducting hard-core vulnerability research, a mastery of IDA Pro is crucial to your success. Take your skills to the next level with The IDA Pro Book.

    In this one-of-a-kind account, you'll see how the developers responsible for these flaws patched the bugs—or failed to respond at all. As you follow Klein on his journey, you'll gain deep technical knowledge and insight into how hackers approach difficult problems and experience the true joys (and frustrations) of bug hunting.Along the way you'll learn how to:Use field-tested techniques to find bugs, like identifying and tracing user input data and reverse engineering Exploit vulnerabilities like NULL pointer dereferences, buffer overflows, and type conversion flaws Develop proof of concept code that verifies the security flaw Report bugs to vendors or third party brokersA Bug Hunter's Diary is packed with real-world examples of vulnerable code and the custom programs used to find and test bugs. Whether you're hunting bugs for fun, for profit, or to make the world a safer place, you'll learn valuable new skills by looking over the shoulder of a professional bug hunter in action.

    With this hands-on book, you’ll learn Flask from the ground up by developing a complete social blogging application step-by-step. Author Miguel Grinberg walks you through the framework’s core functionality, and shows you how to extend applications with advanced web techniques such as database migration and web service communication.Rather than impose development guidelines as other frameworks do, Flask leaves the business of extensions up to you. If you have Python experience, this book shows you how to take advantage of that creative freedom.- Learn Flask’s basic application structure and write an example app- Work with must-have components—templates, databases, web forms, and email support- Use packages and modules to structure a large application that scales- Implement user authentication, roles, and profiles- Build a blogging feature by reusing templates, paginating item lists, and working with rich text- Use a Flask-based RESTful API to expose app functionality to smartphones, tablets, and other third-party clients- Learn how to run unit tests and enhance application performance- Explore options for deploying your web app to a production server

    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

    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.

    You’ll learn everything from the basics of database integration and the use of JavaScript to browser-automation tools like Selenium, and advanced topics such as NoSQL, Web Sockets, and async programming.Ideal for beginners, this book teaches a development methodology that leads to peace of mind, cleaner code, and better web apps.

    In this cookbook, prolific hacker and author Simon Monk provides more than 200 practical recipes for running this tiny low-cost computer with Linux, programming it with Python, and hooking up sensors, motors, and other hardware—including Arduino.You’ll also learn basic principles to help you use new technologies with Raspberry Pi as its ecosystem develops. Python and other code examples from the book are available on GitHub. This cookbook is ideal for programmers and hobbyists familiar with the Pi through resources such as Getting Started with Raspberry Pi (O’Reilly).Set up and manage your Raspberry PiConnect the Pi to a networkWork with its Linux-based operating systemUse the Pi’s ready-made softwareProgram Raspberry Pi with PythonControl hardware through the GPIO connectorUse Raspberry Pi to run different types of motorsWork with switches, keypads, and other digital inputsHook up sensors for taking various measurementsAttach different displays, such as an LED matrixCreate dynamic projects with Raspberry Pi and Arduino Make sure to check out 10 of the over 60 video recipes for this book at: http://razzpisampler.oreilly.com/ You can purchase all recipes at:

    Windows Presentation Foundation (WPF) is the recommended technology for creating Windows user interfaces, giving you the power to create richer and more compelling applications than you dreamed possible. Whether you want to develop traditional user interfaces or integrate 3D graphics, audio/video, animation, dynamic skinning, multi-touch, rich document support, speech recognition, or more, WPF enables you to do so in a seamless, resolution-independent manner. WPF 4 Unleashed is the authoritative book that covers it all, in a practical and approachable fashion, authored by WPF guru and Microsoft developer Adam Nathan. Covers everything you need to know about Extensible Application Markup Language (XAML) Examines the WPF feature areas in incredible depth: controls, layout, resources, data binding, styling, graphics, animation, and more Highlights the latest features, such as multi-touch, text rendering improvements, XAML language enhancements, new controls, the Visual State Manager, easing functions, and much more Delves into topics that aren't covered by most books: 3D, speech, audio/video, documents, effects Shows how to create popular UI elements, such as Galleries, ScreenTips, and more Demonstrates how to create sophisticated UI mechanisms, such as Visual Studio-like collapsible/dockable panes Explains how to create first-class custom controls for WPF Demonstrates how to create hybrid WPF software that leverages Windows Forms, DirectX, ActiveX, or other non-WPF technologies Explains how to exploit new Windows 7 features, such as Jump Lists and taskbar customizations

    You’ll learn how parallelism exploits multicore processors to speed up computation-heavy programs, and how concurrency enables you to write programs with threads for multiple interactions.Author Simon Marlow walks you through the process with lots of code examples that you can run, experiment with, and extend. Divided into separate sections on Parallel and Concurrent Haskell, this book also includes exercises to help you become familiar with the concepts presented:Express parallelism in Haskell with the Eval monad and Evaluation StrategiesParallelize ordinary Haskell code with the Par monadBuild parallel array-based computations, using the Repa libraryUse the Accelerate library to run computations directly on the GPUWork with basic interfaces for writing concurrent codeBuild trees of threads for larger and more complex programsLearn how to build high-speed concurrent network serversWrite distributed programs that run on multiple machines in a network

    The R language provides everything you need to do statistical work, but its structure can be difficult to master. This collection of concise, task-oriented recipes makes you productive with R immediately, with solutions ranging from basic tasks to input and output, general statistics, graphics, and linear regression.Each recipe addresses a specific problem, with a discussion that explains the solution and offers insight into how it works. If you're a beginner, R Cookbook will help get you started. If you're an experienced data programmer, it will jog your memory and expand your horizons. You'll get the job done faster and learn more about R in the process.Create vectors, handle variables, and perform other basic functionsInput and output dataTackle data structures such as matrices, lists, factors, and data framesWork with probability, probability distributions, and random variablesCalculate statistics and confidence intervals, and perform statistical testsCreate a variety of graphic displaysBuild statistical models with linear regressions and analysis of variance (ANOVA)Explore advanced statistical techniques, such as finding clusters in your dataWonderfully readable, R Cookbook serves not only as a solutions manual of sorts, but as a truly enjoyable way to explore the R language--one practical example at a time.--Jeffrey Ryan, software consultant and R package author

    Rust's modern, flexible types ensure your program is free of null pointer dereferences, double frees, dangling pointers, and similar bugs, all at compile time, without runtime overhead. In multi-threaded code, Rust catches data races at compile time, making concurrency much easier to use.Written by two experienced systems programmers, this book explains how Rust manages to bridge the gap between performance and safety, and how you can take advantage of it. Topics include:How Rust represents values in memory (with diagrams)Complete explanations of ownership, moves, borrows, and lifetimesCargo, rustdoc, unit tests, and how to publish your code on crates.io, Rust's public package repositoryHigh-level features like generic code, closures, collections, and iterators that make Rust productive and flexibleConcurrency in Rust: threads, mutexes, channels, and atomics, all much safer to use than in C or C++Unsafe code, and how to preserve the integrity of ordinary code that uses itExtended examples illustrating how pieces of the language fit together