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

    To understand it fully, we must look beneath the surface. The positive side is that technology is making machines smarter. However, the deeper view explained in this book shows that AI is also making a growing number of people cognitively and psychologically dependent on digital networks. Whether you are a social media fanatic, a diehard AI aficionado, or a paranoid sceptic, it is impossible to escape the ubiquitous impact of AI. Artificial Intelligence is the brains bringing together quantum computing, nanotechnology, medical technology, brain-machine interface, robotics, aerospace, 5G, Internet of Things, and more. It is amplifying human ingenuity and disrupting the foundations of healthcare, military, entertainment, education, marketing and manufacturing.Artificial Intelligence and The Future of Power argues that this AI-driven revolution will have an unequal impact on different segments of humanity. There will be new winners and losers, new haves and have-nots, resulting in an unprecedented concentration of wealth and power. After analyzing society’s vulnerabilities to the impending tsunami, the book raises troubling questions that provoke immediate debate: Is the world headed toward digital colonization by USA and China? Will depopulation become eventually unavoidable?Artificial Intelligence and The Future of Power is a wakeup call to action, compelling public intellectuals to be better informed and more engaged. It educates the social segments most at risk and wants them to demand a seat at the table where policies on Artificial Intelligence are being formulated.

    It provides students with skills that will enable them to make productive use of computational techniques, including some of the tools and techniques of "data science" for using computation to model and interpret data. The book is based on an MIT course (which became the most popular course offered through MIT's OpenCourseWare) and was developed for use not only in a conventional classroom but in in a massive open online course (or MOOC) offered by the pioneering MIT--Harvard collaboration edX.Students are introduced to Python and the basics of programming in the context of such computational concepts and techniques as exhaustive enumeration, bisection search, and efficient approximation algorithms. The book does not require knowledge of mathematics beyond high school algebra, but does assume that readers are comfortable with rigorous thinking and not intimidated by mathematical concepts. Although it covers such traditional topics as computational complexity and simple algorithms, the book focuses on a wide range of topics not found in most introductory texts, including information visualization, simulations to model randomness, computational techniques to understand data, and statistical techniques that inform (and misinform) as well as two related but relatively advanced topics: optimization problems and dynamic programming.Introduction to Computation and Programming Using Python can serve as a stepping-stone to more advanced computer science courses, or as a basic grounding in computational problem solving for students in other disciplines.

    Using video links, IP Factly's Amazing Animal series has been designed to encourage and bolster independent reading. The animals are accompanied by pictures and facts plus video links so children can see the animals and how they behave.

    Authors Drew Conway and John Myles White help you understand machine learning and statistics tools through a series of hands-on case studies, instead of a traditional math-heavy presentation.Each chapter focuses on a specific problem in machine learning, such as classification, prediction, optimization, and recommendation. Using the R programming language, you'll learn how to analyze sample datasets and write simple machine learning algorithms. "Machine Learning for Hackers" is ideal for programmers from any background, including business, government, and academic research.Develop a naive Bayesian classifier to determine if an email is spam, based only on its textUse linear regression to predict the number of page views for the top 1,000 websitesLearn optimization techniques by attempting to break a simple letter cipherCompare and contrast U.S. Senators statistically, based on their voting recordsBuild a "whom to follow" recommendation system from Twitter data

    Simply stated, it asks whether every problem whose solution can be quickly checked by computer can also be quickly solved by computer. The Golden Ticket provides a nontechnical introduction to P-NP, its rich history, and its algorithmic implications for everything we do with computers and beyond. Lance Fortnow traces the history and development of P-NP, giving examples from a variety of disciplines, including economics, physics, and biology. He explores problems that capture the full difficulty of the P-NP dilemma, from discovering the shortest route through all the rides at Disney World to finding large groups of friends on Facebook. The Golden Ticket explores what we truly can and cannot achieve computationally, describing the benefits and unexpected challenges of this compelling problem.

    Tesla's rare article, 'The Problem of Increasing Human Energy with Special Reference to the Harnessing of the Sun's Energy' is included. This lengthy article was originally publishing in the June 1900 issue of The Century Illustrated Monthly Magazine and it was the outline for Tesla's master blueprint for the world. Tesla's fantastic vision of the future, including wireless power, anti-gravity, free energy and highly advanced solar power. Also included are some of the papers, patents and material collected on Tesla at the Colorado Spring Tesla Symposiums. Tesla was the inventor of the radio before Marconi, and when he demonstrated wireless remote control to the US Navy in the late 1890s, they replied that it was too advanced! Tesla was an eccentric visionary and probably the greatest inventor who ever lived. His basic inventions power the world of today. What was his vision of tomorrow? Find out in The Tesla Papers.

    But scientists in the late 20th century have found patterns in things formerly thought to be chaotic; their theories help explain the unstable irregular yet highly structured features of everyday experience. It now seems likely that randomness and chaos play an essential role in the evolution of the living world-and of intelligence itself. Script by Dr. Roger White.

    For the first time, William Poundstone reveals the toughest questions used at Microsoft and other Fortune 500 companies -- and supplies the answers. He traces the rise and controversial fall of employer-mandated IQ tests, the peculiar obsessions of Bill Gates (who plays jigsaw puzzles as a competitive sport), the sadistic mind games of Wall Street (which reportedly led one job seeker to smash a forty-third-story window), and the bizarre excesses of today's hiring managers (who may start off your interview with a box of Legos or a game of virtual Russian roulette). How Would You Move Mount Fuji? is an indispensable book for anyone in business. Managers seeking the most talented employees will learn to incorporate puzzle interviews in their search for the top candidates. Job seekers will discover how to tackle even the most brain-busting questions, and gain the advantage that could win the job of a lifetime. And anyone who has ever dreamed of going up against the best minds in business may discover that these puzzles are simply a lot of fun. Why are beer cans tapered on the end, anyway?

    Hailed as "unsinkable, " the "Kursk" was on maneuvers when mysterious explosions rocked the sub, causing it to sink to the bottom of the sea with its 118-man crew. This in-depth look at the disaster reveals previously unreleased information from family members of the deceased as well as from government officials.

    Not only are all of our business, scientific, and government transactions now computerized, but the widespread use of digital cameras, publication tools, and bar codes also generate data. On the collection side, scanned text and image platforms, satellite remote sensing systems, and the World Wide Web have flooded us with a tremendous amount of data. This explosive growth has generated an even more urgent need for new techniques and automated tools that can help us transform this data into useful information and knowledge.Like the first edition, voted the most popular data mining book by KD Nuggets readers, this book explores concepts and techniques for the discovery of patterns hidden in large data sets, focusing on issues relating to their feasibility, usefulness, effectiveness, and scalability. However, since the publication of the first edition, great progress has been made in the development of new data mining methods, systems, and applications. This new edition substantially enhances the first edition, and new chapters have been added to address recent developments on mining complex types of data- including stream data, sequence data, graph structured data, social network data, and multi-relational data.A comprehensive, practical look at the concepts and techniques you need to know to get the most out of real business dataUpdates that incorporate input from readers, changes in the field, and more material on statistics and machine learningDozens of algorithms and implementation examples, all in easily understood pseudo-code and suitable for use in real-world, large-scale data mining projectsComplete classroom support for instructors at www.mkp.com/datamining2e companion site

    This is the story of a technical genius who, against all odds, created a series of machines that revolutionized the computing industry. Chronicling each major breakthrough, Murray takes us behind the scenes to witness late-night brainstorming sessions, miraculous eleventh-hour fixes, and flashes of insight when bold new ideas were cooked up. Drawing from rare in-depth interviews with Seymour Cray, Murray gives us an unparalleled portrait of the man and his methods, reporting not only Cray's personal reflections, but the recollections of his closest colleagues and the truth behind the rumors.

    This highly anticipated fourth edition of the most ...Download Link : readmeaway.com/download?i=0128042915            0128042915 Data Mining: Practical Machine Learning Tools and Techniques (Morgan Kaufmann Series in Data Management Systems) PDF by Ian H. WittenRead Data Mining: Practical Machine Learning Tools and Techniques (Morgan Kaufmann Series in Data Management Systems) PDF from Morgan Kaufmann,Ian H. WittenDownload Ian H. Witten's PDF E-book Data Mining: Practical Machine Learning Tools and Techniques (Morgan Kaufmann Series in Data Management Systems)

    Divided into two parts, the first part of the book introduces the programming language Prolog, while the second part teaches Artificial Intelligence using Prolog as a tool for the implementation of AI techniques. Prolog has its roots in logic, however the main aim of this book is to teach Prolog as a practical programming tool. This text therefore concentrates on the art of using the basic mechanisms of Prolog to solve interesting problems. The third edition has been fully revised and extended to provide an even greater range of applications, which further enhance its value as a self-contained guide to Prolog, AI or AI Programming for students and professional programmers alike.

    Loosely based on neuron behavior inside of human brains, these systems are rapidly catching up with the intelligence of their human creators, defeating the world champion Go player, achieving superhuman performance on video games, driving cars, translating languages, and sometimes even helping law enforcement fight crime. Deep Learning is a revolution that is changing every industry across the globe.Grokking Deep Learning is the perfect place to begin your deep learning journey. Rather than just learn the “black box” API of some library or framework, you will actually understand how to build these algorithms completely from scratch. You will understand how Deep Learning is able to learn at levels greater than humans. You will be able to understand the “brain” behind state-of-the-art Artificial Intelligence. Furthermore, unlike other courses that assume advanced knowledge of Calculus and leverage complex mathematical notation, if you’re a Python hacker who passed high-school algebra, you’re ready to go. And at the end, you’ll even build an A.I. that will learn to defeat you in a classic Atari game.