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Organisationer fokuserer i stigende grad på, hvordan de kan bruge Big Data strategisk. De investerer milliarder af kroner i denne ny teknologi, men værdien og afkastet viser sig tit at være begrænset. Sådanne udfordringer kan meget vel skyldes ledernes mindset, der begrænser hvordan organisationen bruger Big Data-teknologi til at styrke interaktionen og samspillet med kunder og andre interessenter.Det første skridt på vejen mod at få succes med Big Data er derfor at identificere og udfordre sit ledelsesmæssige mindset. I denne bog identificerer og diskuterer vi fire dominerende ledelsesmæssige mindset, der hver især fører til fire unikke Big Data strategier. Når ledere inden for samme team har forskellige mindset, kan det føre til manglende mental og organisatorisk alignment og problemer med at håndtere disruption. Da mindsets ofte er ubevidste, har vi udviklet en onlinetest, der hjælper dig og dine kolleger med at identificere jeres dominerende mindset. Det vil give jer et det bedste afsæt til at vurdere og beslutte hvad der er jeres optimale strategiske vej til Big Data succes.Omtale"Bogen giver en praktisk og teoretisk indførelse i big data. (…) Bogen har mange eksempler, som understøtter bogens budskaber."5/6 stjerner- anmeldt af Henrik Ørholst, Børsen, 1. februar 2018“Det er næsten provokerende, at sammenhængen mellem lederes mindset og succesfuld innovation kan være så markant. Læs bogen og få rystet rundt på dit eget mindset.”- Camilla Laudrup, leder af DRs public outreach sekretariat
Be prepared for next semester and get set for back to school!Foreword by Steven PinkerBlending the informed analysis of The Signal and the Noise with the instructive iconoclasm of Think Like a Freak, a fascinating, illuminating, and witty look at what the vast amounts of information now instantly available to us reveals about ourselves and our worldprovided we ask the right questions.By the end of an average day in the early twenty-first century, human beings searching the internet will amass eight trillion gigabytes of data. This staggering amount of informationunprecedented in historycan tell us a great deal about who we arethe fears, desires, and behaviors that drive us, and the conscious and unconscious decisions we make. From the profound to the mundane, we can gain astonishing knowledge about the human psyche that less than twenty years ago, seemed unfathomable. Everybody Lies offers fascinating, surprising, and sometimes laugh-out-loud insights into everything from economics to ethics to sports to race to sex, gender and more, all drawn from the world of big data. What percentage of white voters didnt vote for Barack Obama because hes black? Does where you go to school effect how successful you are in life? Do parents secretly favor boy children over girls? Do violent films affect the crime rate? Can you beat the stock market? How regularly do we lie about our sex lives and whos more self-conscious about sex, men or women? Investigating these questions and a host of others, Seth Stephens-Davidowitz offers revelations that can help us understand ourselves and our lives better. Drawing on studies and experiments on how we really live and think, he demonstrates in fascinating and often funny ways the extent to which all the world is indeed a lab. With conclusions ranging from strange-but-true to thought-provoking to disturbing, he explores the power of this digital truth serum and its deeper potentialrevealing biases deeply embedded within us, information we can use to change our culture, and the questions were afraid to ask that might be essential to our healthboth emotional and physical. All of us are touched by big data everyday, and its influence is multiplying. Everybody Lies challenges us to think differently about how we see it and the world.
Generative modeling is one of the hottest topics in AI. Its now possible to teach a machine to excel at human endeavors such as painting, writing, and composing music. With this practical book, machine-learning engineers and data scientists will discover how to re-create some of the most impressive examples of generative deep learning models, such as variational autoencoders,generative adversarial networks (GANs), encoder-decoder models and world models.Author David Foster demonstrates the inner workings of each technique, starting with the basics of deep learning before advancing to some of the most cutting-edge algorithms in the field. Through tips and tricks, youll understand how to make your models learn more efficiently and become more creative.Discover how variational autoencoders can change facial expressions in photosBuild practical GAN examples from scratch, including CycleGAN for style transfer and MuseGAN for music generationCreate recurrent generative models for text generation and learn how to improve the models using attentionUnderstand how generative models can help agents to accomplish tasks within a reinforcement learning settingExplore the architecture of the Transformer (BERT, GPT-2) and image generation models such as ProGAN and StyleGAN
Learn how to use R to turn raw data into insight, knowledge, and understanding. 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. Youll 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 youve learned along the way.Youll learn how to:Wrangletransform your datasets into a form convenient for analysisProgramlearn powerful R tools for solving data problems with greater clarity and easeExploreexamine your data, generate hypotheses, and quickly test themModelprovide a low-dimensional summary that captures true "e;signals"e; in your datasetCommunicatelearn R Markdown for integrating prose, code, and results
This bestseller can help anyone whose role is to try to find specific causes for failures.It provides detailed steps for solving problems, focusing more heavily on the analytical process involved in finding the actual causes of problems. It does this using figures, diagrams, and tools useful for helping to make our thinking visible. This increases our ability to see what is truly significant and to better identify errors in our thinking. In the sections on finding root causes, this second edition now includes more examples on the use of multi-vari charts; how thought experiments can help guide data interpretation; how to enhance the value of the data collection process; cautions for analyzing data; and what to do if one can't find the causes. In its guidance on solution identification, biomimicry and TRIZ have been added as potential solution identification techniques. In addition, the appendices have been revised to include: an expanded breakdown of the 7 M's, which includes more than 50 specific possible causes; forms for tracking causes and solutions, which can help maintain alignment of actions; techniques for how to enhance the interview process; and example responses to problem situations that the reader can analyze for appropriateness.
Why laws focused on data cannot effectively protect people-and how an approach centered on human rights offers the best hope for preserving human dignity and autonomy in a cyberphysical world.Ever-pervasive technology poses a clear and present danger to human dignity and autonomy, as many have pointed out. And yet, for the past fifty years, we have been so busy protecting data that we have failed to protect people. In Beyond Data, Elizabeth Renieris argues that laws focused on data protection, data privacy, data security and data ownership have unintentionally failed to protect core human values, including privacy. And, as our collective obsession with data has grown, we have, to our peril, lost sight of what's truly at stake in relation to technological development-our dignity and autonomy as people. Far from being inevitable, our fixation on data has been codified through decades of flawed policy. Renieris provides a comprehensive history of how both laws and corporate policies enacted in the name of data privacy have been fundamentally incapable of protecting humans. Her research identifies the inherent deficiency of making data a rallying point in itself-data is not an objective truth, and what's more, its "entirely contextual and dynamic" status makes it an unstable foundation for organizing. In proposing a human rights-based framework that would center human dignity and autonomy rather than technological abstractions, Renieris delivers a clear-eyed and radically imaginative vision of the future. At once a thorough application of legal theory to technology and a rousing call to action, Beyond Data boldly reaffirms the value of human dignity and autonomy amid widespread disregard by private enterprise at the dawn of the metaverse.
Make any team or business data driven with this practical guide to overcoming common challenges and creating a data culture. Businesses are increasingly focusing on their data and analytics strategy, but a data-driven culture grounded in evidence-based decision making can be difficult to achieve. Be Data Driven outlines a step-by-step roadmap to building a data-driven organization or team, beginning with deciding on outcomes and a strategy before moving onto investing in technology and upskilling where necessary. This practical guide explains what it means to be a data-driven organization and explores which technologies are advancing data and analytics. Crucially, it also examines the most common challenges to becoming data driven, from a foundational skills gap to issues with leadership and strategy and the impact of organizational culture. With case studies of businesses who have successfully used data, Be Data Driven shows managers, leaders and data professionals how to address hurdles, encourage a data culture and become truly data driven.
This book presents a unified theory of random matrices for applications in machine learning, offering a large-dimensional data vision that exploits concentration and universality phenomena. This enables a precise understanding, and possible improvements, of the core mechanisms at play in real-world machine learning algorithms. The book opens with a thorough introduction to the theoretical basics of random matrices, which serves as a support to a wide scope of applications ranging from SVMs, through semi-supervised learning, unsupervised spectral clustering, and graph methods, to neural networks and deep learning. For each application, the authors discuss small- versus large-dimensional intuitions of the problem, followed by a systematic random matrix analysis of the resulting performance and possible improvements. All concepts, applications, and variations are illustrated numerically on synthetic as well as real-world data, with MATLAB and Python code provided on the accompanying website.