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

    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"

    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.

    A young girl submerged into a galactic conflict. A single chance to help win the war. The daughter of a royal family, Brie Northstar believes she knows what to expect from life. She's wrong. During a routine shuttle trip to one of the outer planets, Brie's transport is destroyed and she finds herself thrust into a centuries old conflict between three warring planets, a conflict she is suddenly in the middle of. Yet somehow this young teenager from Athelon has skills and abilities she shouldn't have - she can't have. Now separated from her family with only her wits - and her newfound abilities - to help she travels from planet to planet, risking her life to prevent galactic destruction and unite a rebellion to save the galaxy. For the first time ever - the complete nine book War of the Three Planets series is available in a single omnibus for a ridiculously low price. If you love Ender's Game you will love War of the Three Planets.

    The tree is decorated, the mistletoe is in place, and the crab puffs are to die for. Nothing can spoil this celebration…except for some wayward magic and a pesky ghost who crashes the party.SPIRITS OF BOURBON STREETIt’s Halloween on Bourbon Street, and white witch Jade Calhoun is ready to party. But when a sexy spirit traps everyone in the past, she’s forced to forgo the cocktails in order to save those she loves...again.A MIRACLE ON BOURBON STREETIt’s Christmas Eve and Jade Calhoun’s ready for a quiet, magical night with her husband Kane. But when Santa demons and energy-stealing elves start wreaking havoc, it’s going to take a miracle on Bourbon Street to save Christmas.A BOURBON STREET VALENTINEIt’s Valentine’s Day and magic’s in the air. Only Kat Hart never expected her handmade jewelry to be at the center of everything. So when her night out at Cupid’s Ball turns into a competition to be a handsome stranger’s wife, it’s up to her to break the spell…or she might get more than she bargained for.SPIRITS, HURRICANES, AND THE KREWE OF GHOULIt’s Halloween and Pyper Rayne’s all vamped up, ready to play the vampire bride. And so is Ida May, her resident ghost. But when the “vampires” they're partying with start to appear to be real, there’s more at stake than just a little bite.KISSED BY TEMPTATIONThe last place sex-witch Mati Ballintine wants to be is at a holiday sorority party. But when the high angel from the council orders her to protect a new angel recruit, she has no choice but to go and grit her teeth through the frat boys’ advances.
The night turns from bad to worse when a demon appears and is hell-bent on taking the angel to Hell. Mati and her incubus boyfriend Vaughn will need to combine forces to not only save the angel, but everyone else the demon leaves in his wake.

    Before his success as an author Goodkind worked primarily as a painter, as well as doing carpentry and woodworking. Goodkind is a proponent of Ayn Rand's philosophical approach of Objectivism, with references to Rand's ideas and novels referenced in his works.The Sword of Truth series sold twenty-five million copies worldwide and was translated into more than twenty languages. It was adapted into a television series called Legend of the Seeker, premiering November 1, 2008 but canceled after two seasons in May, 2010.

    This is the currently used textbook for "Probabilistic Systems Analysis," an introductory probability course at the Massachusetts Institute of Technology, attended by a large number of undergraduate and graduate students. The book covers the fundamentals of probability theory (probabilistic models, discrete and continuous random variables, multiple random variables, and limit theorems), which are typically part of a first course on the subject. It also contains, a number of more advanced topics, from which an instructor can choose to match the goals of a particular course. These topics include transforms, sums of random variables, least squares estimation, the bivariate normal distribution, and a fairly detailed introduction to Bernoulli, Poisson, and Markov processes. The book strikes a balance between simplicity in exposition and sophistication in analytical reasoning. Some of the more mathematically rigorous analysis has been just intuitively explained in the text, but is developed in detail (at the level of advanced calculus) in the numerous solved theoretical problems. The book has been widely adopted for classroom use in introductory probability courses within the USA and abroad.

    They are established tools in a wide range of industrial applications, including search engines, DNA sequencing, stock market analysis, and robot locomotion, and their use is spreading rapidly. People who know the methods have their choice of rewarding jobs. This hands-on text opens these opportunities to computer science students with modest mathematical backgrounds. It is designed for final-year undergraduates and master's students with limited background in linear algebra and calculus. Comprehensive and coherent, it develops everything from basic reasoning to advanced techniques within the framework of graphical models. Students learn more than a menu of techniques, they develop analytical and problem-solving skills that equip them for the real world. Numerous examples and exercises, both computer based and theoretical, are included in every chapter. Resources for students and instructors, including a MATLAB toolbox, are available online.

    Lucid Intervals Strategic Moves Bel-Air Dead

    He lived alone in the Frank Church Wilderness for the winter of 2016, at almost 60 year of age, in a cabin with wood stove and an axe. He shares his sabbatical in this book; the conclusion of the 21st Century Mountain Man non-fiction adventure trilogy.

    And yet there were parts of this biography that made him wince.” For The Golden Ham is candid biography. To it Mr. Bishop brought his painstaking interest in detail, his reporter’s curiosity, his layman’s interest in the world of the theater, and his detachment. And most important, he began and ended his job with Jackie Gleason’s guarantee that nothing Bishop wrote would be censored. The result is a kind of theatrical biography that is entirely new and, like Gleason himself, is made up of a great deal of a great many things. As Bishop says: “There are several Jackie Gleasons. I know some of them. There is Gleason the comedian. Millions know him, and he’s a great talent. Then there is Gleason the producer and Gleason the writer. Some people know these...Gleason the businessman-second-rate, but he thinks he’s good at it – and then there is Gleason the thinker (apt and fast) and Gleason the man (fat, out of shape, but light on his feet) and Gleason the tenement-house kid from Brooklyn (nervy and not a bit surprised that he’s on top) and Gleason the lover, Gleason the musician, Gleason the moody, and Gleason the lonely, tormented soul.” This is a book about Jackie Gleason. If you like him, it may make you like him more, or less, depending on the kind of person you are. If you never liked him, it may change your mind a little. If you never had any special attitude toward Jackie Gleason, you will have one by the time you have finished this book.Gleason, a 4-pack-a-day smoker, passed away at his home in Florida on June 24, 1987.

    Readers are provided once again with an instructive mix of mathematics, physics, statistics, and information theory.All the essential topics in information theory are covered in detail, including entropy, data compression, channel capacity, rate distortion, network information theory, and hypothesis testing. The authors provide readers with a solid understanding of the underlying theory and applications. Problem sets and a telegraphic summary at the end of each chapter further assist readers. The historical notes that follow each chapter recap the main points.The Second Edition features: * Chapters reorganized to improve teaching * 200 new problems * New material on source coding, portfolio theory, and feedback capacity * Updated referencesNow current and enhanced, the Second Edition of Elements of Information Theory remains the ideal textbook for upper-level undergraduate and graduate courses in electrical engineering, statistics, and telecommunications.

    

    Contains 640 problems including solutions; additional practice problems with answers; explanations of complex variable theory; coverage of applications of complex variables in engineering, physics, and elsewhere, with accompanying sample problems and solutions.

    When we use consumer products from Google, Microsoft, Facebook, Apple, or Baidu, we are often interacting with a deep learning system. In this volume in the MIT Press Essential Knowledge series, computer scientist John Kelleher offers an accessible and concise but comprehensive introduction to the fundamental technology at the heart of the artificial intelligence revolution.Kelleher explains that deep learning enables data-driven decisions by identifying and extracting patterns from large datasets; its ability to learn from complex data makes deep learning ideally suited to take advantage of the rapid growth in big data and computational power. Kelleher also explains some of the basic concepts in deep learning, presents a history of advances in the field, and discusses the current state of the art. He describes the most important deep learning architectures, including autoencoders, recurrent neural networks, and long short-term networks, as well as such recent developments as Generative Adversarial Networks and capsule networks. He also provides a comprehensive (and comprehensible) introduction to the two fundamental algorithms in deep learning: gradient descent and backpropagation. Finally, Kelleher considers the future of deep learning—major trends, possible developments, and significant challenges.