Weighing in from the cutting-edge frontiers of science, today´s most forward-thinking minds explore the rise of ´´machines that think.´´ Stephen Hawking recently made headlines by noting, ´´The development of full artificial intelligence could spell the end of the human race.´´ Others, conversely, have trumpeted a new age of ´´superintelligence´´ in which smart devices will exponentially extend human capacities. No longer just a matter of science-fiction fantasy (2001, Blade Runner, The Terminator, Her, etc.), it is time to seriously consider the reality of intelligent technology, many forms of which are already being integrated into our daily lives. In that spirit, John Brockman, publisher of Edge. org (´´the world´s smartest website´´ - The Guardian), asked the world´s most influential scientists, philosophers, and artists one of today´s most consequential questions: What do you think about machines that think?
If you know how to program with Python and also know a little about probability, you?re ready to tackle Bayesian statistics. With this book, you´ll learn how to solve statistical problems with Python code instead of mathematical notation, and use discrete probability distributions instead of continuous mathematics. Once you get the math out of the way, the Bayesian fundamentals will become clearer, and you?ll begin to apply these techniques to real-world problems. Bayesian statistical methods are becoming more common and more important, but not many resources are available to help beginners. Based on undergraduate classes taught by author Allen Downey, this book?s computational approach helps you get a solid start. * Use your existing programming skills to learn and understand Bayesian statistics * Work with problems involving estimation, prediction, decision analysis, evidence, and hypothesis testing * Get started with simple examples, using coins, M&Ms, Dungeons & Dragons dice, paintball, and hockey * Learn computational methods for solving real-world problems, such as interpreting SAT scores, simulating kidney tumors, and modeling the human microbiome.
- Vor welchen Herausforderungen steht das Management großer Projekte? - Wie sinnvoll sind agile Skalierungsschablonen? - Welche Voraussetzungen sollten für eine erfolgreiche Skalierung erfüllt sein? - Welche Skills brauchen Mitarbeiter in agilen Projekten? - Wie wird aus einem Projekt eine fraktal skalierte Organisation? Viele Mitarbeiter an vielen Standorten sollen gemeinsam ein Projekt agil abwickeln - was ist dabei zu berücksichtigen? In kleinen Teams hat sich Scrum als Weg für erfolgreiche Produktentwicklung längst etabliert, doch jetzt geht es um andere Dimensionen. Unter dem Druck der Digitalisierung beginnen selbst Großkonzerne, die Erfahrungen aus agilen Pilotprojekten auf immer größere Teile der Organisation zu übertragen. Agile Skalierungsframeworks versprechen schnelle und einfache Lösungen, diese vorgefertigten Strukturen führen aber nicht zum eigentlichen Ziel: dem agilen Unternehmen. Boris Gloger beschreibt in diesem Buch einen anderen Weg, der auf seinen eigenen Erfahrungen basiert. Bei der Skalierung von Scrum geht es nicht um die Multiplikation einer Methode, sondern um einen neuen Blick auf das große Projekt als fraktal skalierte Organisation. Gefragt sind entkoppelte Produktarchitekturen, das konsequente Denken aus der Sicht des Kunden, das Projektmanagement-Office als umsichtiger ScrumMaster, die Lust auf frische Skills, gestützt von modernen Infrastrukturen. Und schließlich braucht es eine Führung, die ihre wichtigste Aufgabe darin sieht, Zusammenarbeit über alle Ebenen hinweg zu ermöglichen. AUS DEM INHALT// Die Umfeldbedingungen des Skalierens // Kommunikations- und Produktarchitektur // Die passende Infrastruktur // Skills und Professionalität // Produktentwicklung // Good Practices für das skalierte Scrum-Projekt // Die fraktal skalierte Organisation Extra: E-Book inside
Key Features: * Following the data science process data science, step by step ? Discovering the world of data as a wilderness to be explored, wrangled, and studied Learning to foresee problems at each stage of a data science project ? Dealing with the uncertainty inherent in working with data ? Understanding concepts of data, software, and statistics in ways accessible even for beginners ? Engaging in some of the most relevant best practices in software, statistics, and scientific thinking Readers should have some familiarity with a programming language and basic statistics, but need not be experts. Important foundational concepts will be reviewed briefly in the book. ABOUT THE TECHNOLOGY Data science is a set of concepts and techniques for extracting meaning and clarity from stored data sets or data streams.
The future is here: Self-driving cars are on the streets, an algorithm gives you movie and TV recommendations, IBM´s Watson triumphed on Jeopardy over puny human brains, computer programs can be trained to play Atari games. But how do all these things work? In this book, Sean Gerrish offers an engaging and accessible overview of the breakthroughs in artificial intelligence and machine learning that have made today´s machines so smart. Gerrish outlines some of the key ideas that enable intelligent machines to perceive and interact with the world. He describes the software architecture that allows self-driving cars to stay on the road and to navigate crowded urban environments; the million-dollar Netflix competition for a better recommendation engine (which had an unexpected ending); and how programmers trained computers to perform certain behaviors by offering them treats, as if they were training a dog. He explains how artificial neural networks enable computers to perceive the world¿and to play Atari video games better than humans. He explains Watson´s famous victory on Jeopardy, and he looks at how computers play games, describing AlphaGo and Deep Blue, which beat reigning world champions at the strategy games of Go and chess. Computers have not yet mastered everything, however; Gerrish outlines the difficulties in creating intelligent agents that can successfully play video games like StarCraft that have evaded solution¿at least for now.
If you know how to program, you have the skills to turn data into knowledge, using tools of probability and statistics. This concise introduction shows you how to perform statistical analysis computationally, rather than mathematically, with programs written in Python. By working with a single case study throughout this thoroughly revised book, you?ll learn the entire process of exploratory data analysis?from collecting data and generating statistics to identifying patterns and testing hypotheses. You?ll explore distributions, rules of probability, visualization, and many other tools and concepts. New chapters on regression, time series analysis, survival analysis, and analytic methods will enrich your discoveries. * Develop an understanding of probability and statistics by writing and testing code * Run experiments to test statistical behavior, such as generating samples from several distributions * Use simulations to understand concepts that are hard to grasp mathematically * Import data from most sources with Python, rather than rely on data that?s cleaned and formatted for statistics tools * Use statistical inference to answer questions about real-world data
Examines how increasingly complicated computer technology has outpaced the ability of the average consumer to use it without frustration and calls for changes that will bring computer technology in line with how average people think.
Jack the Ripper and legacy codebases have more in common than you´d think. Inspired by forensic psychology methods, you´ll learn strategies to predict the future of your codebase, assess refactoring direction, and understand how your team influences the design. With its unique blend of forensic psychology and code analysis, this book arms you with the strategies you need, no matter what programming language you use.
Multidimensional data is data that exists and changes in more than one dimension, by time, or spatially, or both, sometimes dynamically. Think here of tracking hurricane data in order to project the storm´s path, for just one example. As spatial and other multidimensional data structures become increasingly important for the applications in game programming, data mining, bioinformatics, and many other areas--including astronomy, geographic information systems, physics, etc., the need for a comprehensive book on the subject is paramount. This book is truly a life´s work by the author who is clearly the best person for the job.