This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You’ll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you’ve learned along the way. You’ll learn how to: Wrangle—transform your datasets into a form convenient for analysis Program—learn powerful R tools for solving data problems with greater clarity and ease Explore—examine your data, generate hypotheses, and quickly test them Model—provide a low-dimensional summary that captures true "signals" in your dataset Communicate—learn R Markdown for integrating prose, code, and results

    But building a machine learning system requires that you make practical decisions: Should you collect more training data? Should you use end-to-end deep learning? How do you deal with your training set not matching your test set? and many more. Historically, the only way to learn how to make these "strategy" decisions has been a multi-year apprenticeship in a graduate program or company. This is a book to help you quickly gain this skill, so that you can become better at building AI systems.

    As more and more businesses seek to tame the massive unbounded data sets that pervade our world, streaming systems have finally reached a level of maturity sufficient for mainstream adoption. With this practical guide, data engineers, data scientists, and developers will learn how to work with streaming data in a conceptual and platform-agnostic way.Expanded from Tyler Akidau's popular blog posts Streaming 101 and Streaming 102, this book takes you from an introductory level to a nuanced understanding of the what, where, when, and how of processing real-time data streams. You'll also dive deep into watermarks and exactly-once processing with co-authors Slava Chernyak and Reuven Lax.You'll explore:How streaming and batch data processing patterns compareThe core principles and concepts behind robust out-of-order data processingHow watermarks track progress and completeness in infinite datasetsHow exactly-once data processing techniques ensure correctnessHow the concepts of streams and tables form the foundations of both batch and streaming data processingThe practical motivations behind a powerful persistent state mechanism, driven by a real-world exampleHow time-varying relations provide a link between stream processing and the world of SQL and relational algebra

    It is the technology behind photo tagging systems at Facebook and Google, self-driving cars, speech recognition systems on your smartphone, and much more.In particular, Deep learning excels at solving machine perception problems: understanding the content of image data, video data, or sound data. Here's a simple example: say you have a large collection of images, and that you want tags associated with each image, for example, "dog," "cat," etc. Deep learning can allow you to create a system that understands how to map such tags to images, learning only from examples. This system can then be applied to new images, automating the task of photo tagging. A deep learning model only has to be fed examples of a task to start generating useful results on new data.

    Mathematical concepts and computational problems are motivated by applications in computer science. The reader learns by "doing," writing programs to implement the mathematical concepts and using them to carry out tasks and explore the applications. Examples include: error-correcting codes, transformations in graphics, face detection, encryption and secret-sharing, integer factoring, removing perspective from an image, PageRank (Google's ranking algorithm), and cancer detection from cell features. A companion web site, codingthematrix.com provides data and support code. Most of the assignments can be auto-graded online. Over two hundred illustrations, including a selection of relevant "xkcd" comics. Chapters: "The Function," "The Field," "The Vector," "The Vector Space," "The Matrix," "The Basis," "Dimension," "Gaussian Elimination," "The Inner Product," "Special Bases," "The Singular Value Decomposition," "The Eigenvector," "The Linear Program"

    This comprehensive work covers both statistical and symbolic approaches to language processing; it shows how they can be applied to important tasks such as speech recognition, spelling and grammar correction, information extraction, search engines, machine translation, and the creation of spoken-language dialog agents. The following distinguishing features make the text both an introduction to the field and an advanced reference guide.- UNIFIED AND COMPREHENSIVE COVERAGE OF THE FIELDCovers the fundamental algorithms of each field, whether proposed for spoken or written language, whether logical or statistical in origin.- EMPHASIS ON WEB AND OTHER PRACTICAL APPLICATIONSGives readers an understanding of how language-related algorithms can be applied to important real-world problems.- EMPHASIS ON SCIENTIFIC EVALUATIONOffers a description of how systems are evaluated with each problem domain.- EMPERICIST/STATISTICAL/MACHINE LEARNING APPROACHES TO LANGUAGE PROCESSINGCovers all the new statistical approaches, while still completely covering the earlier more structured and rule-based methods.

    In a self-contained manner it proceeds from mathematical and theoretical considerations to actual practical computer routines. With over 100 new routines bringing the total to well over 300, plus upgraded versions of the original routines, the new edition remains the most practical, comprehensive handbook of scientific computing available today.

    Major concepts are illustrated with running examples, and major algorithms are illustrated by Pascal computer programs. No prior knowledge of GAs or genetics is assumed, and only a minimum of computer programming and mathematics background is required. 0201157675B07092001

    Peter Flach's clear, example-based approach begins by discussing how a spam filter works, which gives an immediate introduction to machine learning in action, with a minimum of technical fuss. Flach provides case studies of increasing complexity and variety with well-chosen examples and illustrations throughout. He covers a wide range of logical, geometric and statistical models and state-of-the-art topics such as matrix factorisation and ROC analysis. Particular attention is paid to the central role played by features. The use of established terminology is balanced with the introduction of new and useful concepts, and summaries of relevant background material are provided with pointers for revision if necessary. These features ensure Machine Learning will set a new standard as an introductory textbook.

    He coined the word for it--cybernetics. In God & Golem, Inc., the author concerned himself with major points in cybernetics which are relevant to religious issues.The first point he considers is that of the machine which learns. While learning is a property almost exclusively ascribed to the self-conscious living system, a computer now exists which not only can be programmed to play a game of checkers, but one which can learn from its past experience and improve on its own game. For a time, the machine was able to beat its inventor at checkers. It did win, writes the author, and it did learn to win; and the method of its learning was no different in principle from that of the human being who learns to play checkers.A second point concerns machines which have the capacity to reproduce themselves. It is our commonly held belief that God made man in his own image. The propagation of the race may also be interpreted as a function in which one living being makes another in its own image. But the author demonstrates that man has made machines which are very well able to make other machines in their own image, and these machine images are not merely pictorial representations but operative images. Can we then say: God is to Golem as man is to Machines? in Jewish legend, golem is an embryo Adam, shapeless and not fully created, hence a monster, an automation.The third point considered is that of the relation between man and machine. The concern here is ethical. render unto man the things which are man's and unto the computer the things which are the computer's, warns the author. In this section of the book, Dr. Wiener considers systems involving elements of man and machine. The book is written for the intellectually alert public and does not involve any highly technical knowledge. It is based on lectures given at Yale, at the Soci�t� Philosophique de Royaumont, and elsewhere.

    You'll systematically discover each technology in the MEAN stack as you build up an application one layer at a time, just as you'd do in a real project.Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.About the TechnologyTraditional web dev stacks use a different programming language in every layer, resulting in a complex mashup of code and frameworks. Together, the MongoDB database, the Express and AngularJS frameworks, and Node.js constitute the MEAN stack--a powerful platform that uses only one language, top to bottom: JavaScript. Developers and businesses love it because it's scalable and cost-effective. End users love it because the apps created with it are fast and responsive. It's a win-win-win!About the BookGetting MEAN with Mongo, Express, Angular, and Node teaches you how to develop web applications using the MEAN stack. First, you'll create the skeleton of a static site in Express and Node, and then push it up to a live web server. Next, you'll add a MongoDB database and build an API before using Angular to handle data manipulation and application logic in the browser. Finally you'll add an authentication system to the application, using the whole stack. When you finish, you'll have all the skills you need to build a dynamic data-driven web application.What's InsideFull-stack development using JavaScriptResponsive web techniquesEverything you need to get started with MEANBest practices for efficiency and reusabilityAbout the ReaderReaders should have some web development experience. This book is based on MongoDB 2, Express 4, Angular 1, and Node.js 4.About the AuthorSimon Holmes has been a full-stack developer since the late 1990s and runs Full Stack Training Ltd.Table of ContentsPART 1 SETTING THE BASELINEIntroducing full-stack developmentDesigning a MEAN stack architecturePART 2 BUILDING A NODE WEB APPLICATIONCreating and setting up a MEAN projectBuilding a static site with Node and ExpressBuilding a data model with MongoDB and MongooseWriting a REST API: Exposing the MongoDB database to the applicationConsuming a REST API: Using an API from inside ExpressPART 3 ADDING A DYNAMIC FRONT END WITH ANGULARAdding Angular components to an Express applicationBuilding a single-page application with Angular: FoundationsBuilding an SPA with Angular: The next levelPART 4 MANAGING AUTHENTICATION AND USER SESSIONSAuthenticating users, managing sessions, and securing APIsAPPENDIXESInstalling the stackInstalling and preparing the supporting castDealing with all of the viewsReintroducing JavaScript - available online only

    The Computer Revolution may be even more life-changing than the Industrial Revolution. We can do things with computers that could never be done before, and computers can do things for us that could never be done before.But our love of computers should not cloud our thinking about their limitations.We are told that computers are smarter than humans and that data mining can identify previously unknown truths, or make discoveries that will revolutionize our lives. Our lives may well be changed, but not necessarily for the better. Computers are very good at discovering patterns, but are uselessin judging whether the unearthed patterns are sensible because computers do not think the way humans think.We fear that super-intelligent machines will decide to protect themselves by enslaving or eliminating humans. But the real danger is not that computers are smarter than us, but that we think computers are smarter than us and, so, trust computers to make important decisions for us.The AI Delusion explains why we should not be intimidated into thinking that computers are infallible, that data-mining is knowledge discovery, and that black boxes should be trusted.

    

    These days, linger over a Web page selling lamps, and they will turn up at the advertising margins as you move around the Internet, reminding you, tempting you to make that purchase. Search engines such as Google can now look deep into the data on the Web to pull out instances of the words you are looking for. And there are pages that collect and assess information to give you a snapshot of changing political opinion. These are just basic examples of the growth of Web intelligence, as increasingly sophisticated algorithms operate on the vast and growing amount of data on the Web, sifting, selecting, comparing, aggregating, correcting; following simple but powerful rules to decide what matters. While original optimism for Artificial Intelligence declined, this new kind of machine intelligence is emerging as the Web grows ever larger and more interconnected.Gautam Shroff takes us on a journey through the computer science of search, natural language, text mining, machine learning, swarm computing, and semantic reasoning, from Watson to self-driving cars. This machine intelligence may even mimic at a basic level what happens in the brain.

    Because almost every aspect of an enterprise is now dependent on technology, the focus of IT security must shift from locking down assets to enabling the business while managing and surviving risk. This compact book discusses business risk from a broader perspective, including privacy and regulatory considerations. It describes the increasing number of threats and vulnerabilities, but also offers strategies for developing solutions. These include discussions of how enterprises can take advantage of new and emerging technologies—such as social media and the huge proliferation of Internet-enabled devices—while minimizing risk. With ApressOpen, content is freely available through multiple online distribution channels and electronic formats with the goal of disseminating professionally edited and technically reviewed content to the worldwide community. Here are some of the responses from reviewers of this exceptional work: “Managing Risk and Information Security is a perceptive, balanced, and often thought-provoking exploration of evolving information risk and security challenges within a business context.  Harkins clearly connects the needed, but often-overlooked linkage and dialog between the business and technical worlds and offers actionable strategies.   The book contains eye-opening security insights that are easily understood, even by the curious layman.” Fred Wettling, Bechtel Fellow, IS&T Ethics & Compliance Officer, Bechtel     “As disruptive technology innovations and escalating cyber threats continue to create enormous information security challenges, Managing Risk and Information Security: Protect to Enable provides a much-needed perspective. This book compels information security professionals to think differently about concepts of risk management in order to be more effective. The specific and practical guidance offers a fast-track formula for developing information security strategies which are lock-step with business priorities.” Laura Robinson, Principal, Robinson Insight Chair, Security for Business Innovation Council (SBIC) Program Director, Executive Security Action Forum (ESAF) “The mandate of the information security function is being completely rewritten. Unfortunately most heads of security haven’t picked up on the change, impeding their companies’ agility and ability to innovate. This book makes the case for why security needs to change, and shows how to get started. It will be regarded as marking the turning point in information security for years to come.” Dr. Jeremy Bergsman, Practice Manager, CEB     “The world we are responsible to protect is changing dramatically and at an accelerating pace. Technology is pervasive in virtually every aspect of our lives. Clouds, virtualization and mobile are redefining computing – and they are just the beginning of what is to come. Your security perimeter is defined by wherever your information and people happen to be.