Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Why does one smoker die of lung cancer but another live to 100? The answer is 'The Hidden Half' - those random, unknowable variables that mess up our attempts to comprehend the world.We humans are very clever creatures - but we're idiots about how clever we really are. In this entertaining and ingenious book, Blastland reveals how in our quest to make the world more understandable, we lose sight of how unexplainable it often is. The result - from GDP figures to medicine - is that experts know a lot less than they think.Filled with compelling stories from economics, genetics, business, and science, The Hidden Half is a warning that an explanation which works in one arena may not work in another. Entertaining and provocative, it will change how you view the world.(https://www.goodreads.com/book/show/44776411-the-hidden-half)

Kritisch denken --- Big Data --- Datamining

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Know how to do machine learning with Microsoft technologies. This book teaches you to do predictive, descriptive, and prescriptive analyses with Microsoft Power BI, Azure Data Lake, SQL Server, Stream Analytics, Azure Databricks, HD Insight, and more. The ability to analyze massive amounts of real-time data and predict future behavior of an organization is critical to its long-term success. Data science, and more specifically machine learning (ML), is today's game changer and should be a key building block in every company's strategy. Managing a machine learning process from business understanding, data acquisition and cleaning, modeling, and deployment in each tool is a valuable skill set.Machine Learning with Microsoft Technologies is a demo-driven book that explains how to do machine learning with Microsoft technologies. You will gain valuable insight into designing the best architecture for development, sharing, and deploying a machine learning solution. This book simplifies the process of choosing the right architecture and tools for doing machine learning based on your specific infrastructure needs and requirements. Detailed content is provided on the main algorithms for supervised and unsupervised machine learning and examples show ML practices using both R and Python languages, the main languages inside Microsoft technologies. What You'll Learn Choose the right Microsoft product for your machine learning solution Create and manage Microsoft's tool environments for development, testing, and production of a machine learning project Implement and deploy supervised and unsupervised learning in Microsoft products Set up Microsoft Power BI, Azure Data Lake, SQL Server, Stream Analytics, Azure Databricks, and HD Insight to perform machine learning Set up a data science virtual machine and test-drive installed tools, such as Azure ML Workbench, Azure ML Server Developer, Anaconda Python, Jupyter Notebook, Power BI Desktop, Cognitive Services, machine learning and data analytics tools, and more Architect a machine learning solution factoring in all aspects of self service, enterprise, deployment, and sharing Who This Book Is For Data scientists, data analysts, developers, architects, and managers who want to leverage machine learning in their products, organization, and services, and make educated, cost-saving decisions about their ML architecture and tool set.

Robotica --- Robot --- Microsoft --- Artificiële intelligentie --- Datamining

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Martin Lindstrom wordt ingehuurd door de toonaangevendste merken ter wereld om uit te vinden wat hun klanten drijft. Hij besteedt 300 nachten per jaar bij hen thuis om door zorgvuldige observatie van alle details hun verborgen verlangens te ontdekken en zo, in het ultieme geval, aanwijzingen te vinden voor een miljoenenproduct.Lindstrom voert je mee in een wereldomvattend verhaal dat ondernemende marketeers en iedereen die geïnteresseerd is in de eindeloze rijkheid van menselijk gedrag zal boeien en verbazen.Hoe een afgetrapte sneaker van een 11-jarige Duitse jongen leidde tot de ongelooflijke wederopstanding van LEGO.Hoe een magneet op een koelkast in Siberië een Amerikaanse supermarktrevolutie veroorzaakte.Hoe een knuffelbeer in de slaapkamer van een meisje een kledingretailer hielp met de optimalisatie van 1.000 winkels in 20 landen.Hoe een doodgewone armband de klantenloyaliteit met 159 procent verbeterde in minder dan een jaar.Hoe de ergonomische lay-out van het dashboard van een auto aanleiding gaf voor het redesign van de Roomba-stofzuiger.Bron : http://www.bol.com

Data --- Trends --- Trendwatching --- Merkbeleid --- Database marketing --- Datamining --- Trend

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Information systems --- Artificial intelligence. Robotics. Simulation. Graphics --- datamining --- data acquisition

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

This book provides a gentle introduction to redescription mining, a versatile data mining tool that is useful to find distinct common characterizations of the same objects and, vice versa, to identify sets of objects that admit multiple shared descriptions. It is intended for readers who are familiar with basic data analysis techniques such as clustering, frequent itemset mining, and classification. Redescription mining is defined in a general way, making it applicable to different types of data. The general framework is made more concrete through many practical examples that show the versatility of redescription mining. The book also introduces the main algorithmic ideas for mining redescriptions, together with applications from various domains. The final part of the book contains variations and extensions of the basic redescription mining problem, and discusses some future directions and open questions. .

Information systems --- Artificial intelligence. Robotics. Simulation. Graphics --- datamining --- data acquisition