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Commentaires

Data-Directed Mathematical Modeling

Chapman & Hall/CRC Computational Science

This book is a well-recognized and leading guidebook on High Performance Computing for a broad audience of readers from industry and academia. The new edition is completely reorganized and updated to include the latest research in HPC. A new introductory chapter and a new chapter on accelerated computing have been added to the book to keep pace with recent advances.

Introduction to High Performance Computing for Scientists and Engineers, Second Edition

Graph Mining

Ob in der Datenerfassung, der Fan-Interaktion, der Trainingssteuerung oder der Sportmedizin – überall sind die Auswirkungen von Digitalisierung und Innovation im sportlichen Kontext spürbar. Dieses Handbuch greift die spannende Schnittstelle zwischen Sportwissenschaft, Digitalisierung und Innovation auf und erläutert, wie innovative Technologien den Sport revolutioniert haben und zukünftig weiter revolutionieren werden.Inhaltlich wird ein umfassendes Spektrum an Themen behandelt, wie z. B. Innovationsmanagement, künstliche Intelligenz, maschinelles Lernen, Big Data-Analyse, Augmented Reality sowie Fallstudien und Praxisbeispiele mit Bezug zum Sport und zur Sportwissenschaft. Diese sind wiederum breitgefächert und umfassen beispielsweise die Sportlehrer*innenbildung, den Schulsport, den Alterssport, sowie den Vereins- und Profisport. &nbsp;Für Trainer*innen, Profisportler*innen, Manager*innen, Lehrkräfte, Wissenschaftler*innen, Studierende und Technologiebegeisterte ist dieses Handbuch daher ein unverzichtbares Nachschlagewerk, um erfolgreich im modernen Sportumfeld zu navigieren. Der HerausgeberProf. Dr. Daniel Memmert&nbsp;ist geschäftsführender Institutsleiter und Professor am Institut für Trainingswissenschaften und Sportinformatik an der Deutschen Sporthochschule Köln. Er ist Herausgeber und Autor zahlreicher Lehrbücher mit Arbeitsschwerpunkten in der Bewegungswissenschaft sowie in der Sportpsychologie und -informatik. Sein Institut organisiert zwei Zertifikatsstudiengänge (Spielanalyse Team Köln / Sportdirektor*in im Nachwuchsleistungs- und Amateurfußball) sowie den ersten internationalen Weiterbildungs-Masterstudiengang „Spielanalyse“.

Handbuch Sport und Sportwissenschaft

Digitalisierung und Innovation im Sport und in der Sportwissenschaft

Written by a data scientist with decades of practical experience on some of the most challenging datasets, this book caters to both novices and professionals working in the fields of data science and machine learning. It enables novices to enter these fields with confidence and ease. There are no formal prerequisites. An introduction to Python is included. The book then proceeds to cover some of the language's most advanced features. The mathematical foundations that follow are meticulously explained. The discussion then proceeds to a practical consideration of data science and machine learning. The material is delivered through case studies developed on real datasets. The reader is invited to follow these case studies, and so develop experience in working with real data. Numerous solved exercises make this work suitable for self-study. The contents of this book have been taught by the author to students of different age groups and levels of experience at Imperial College London and at the Thalesians Intensive Summer School in Artificial Intelligence over the years.

Python, Data Science And Machine Learning: From Scratch To Productivity

Annals of Mathematics Studies

A comprehensive new theory of adelic line bundles on quasi-projective varieties over finitely generated fieldsThis book introduces a comprehensive theory of adelic line bundles on quasi-projective varieties over finitely generated fields, developed in both geometric and arithmetic contexts. In the geometric setting, adelic line bundles are defined as limits of line bundles on projective compactifications under the boundary topology. In the arithmetic setting, they are defined as limits of Hermitian line bundles on projective arithmetic compactifications, also under the boundary topology. After establishing these foundational definitions, the book uses the theory to explore key concepts such as intersection theory, effective sections, volumes, and positivity of adelic line bundles. It also applies these results to study height functions of algebraic points and prove an equidistribution theorem on quasi-projective varieties. This theory has broad applications in the study of numerical, dynamical, and Diophantine properties of moduli spaces, quasi-projective varieties, and varieties over finitely generated fields.

Adelic Line Bundles on Quasi-projective Varieties

Fundamentals and Applications

Graph Theory

Inside Rugby's Tactical and Data Revolution

Attacking the Space

The Internet of Things (IoT) has revolutionized the way we live, work, and interact with our surroundings. Sensor systems play a crucial role in these technological advancements, and their integration with IoT has revolutionized the way data is acquired, processed, and utilized. This book offers a structured understanding of sensor technologies, principles, applications, and their integration into IoT systems, thereby bridging the gap between sensors and IoT applications.This book goes beyond theoretical explanations and stands out as a comprehensive and practical guide with a strong focus on real-world applications. It reveals some interesting aspects of such fascinating interactions between sensors and the IoT -- in particular, cutting-edge technologies and possibilities ranging from smart homes to industrial automation and beyond. It also offers a detailed exploration of sensors and the measurement principles characteristic of sensors designed to detect and measure specific environmental parameters -- especially those equipped with the capability to convert the collected data into electronic signals, which can then be processed and communicated to other connected devices through the internet. Data-based innovation in driving technological advancements is also addressed, and Intellectual Property (IP) is explored as a key element in bridging the gap between scientific research and marketable products.This book is an indispensable guide for students, researchers, and professionals alike. Its comprehensive approach to sensor technology makes it a valuable resource for scientists, engineers, and decision makers in industry.

Sensors And The Internet Of Things: Acquisition And Use Of Big Data

Use this step-by-step guide to prepare, design and implement a simplified data governance programme built to meet the needs of your organization. Prepare, design and implement a data governance framework that works for your organization with this practical guide to harnessing data successfully.

Design a Framework that Works for Your Organization

Effective Data Governance

Tech Today

An insightful collection of the best ways to leverage enterprise data with AI and implement powerful business solutions In Generative Analytics and AI: Transforming Data into Scalable Business Solutions, leading AI strategists Paul Golding and Geoff McGrath walk you through how to implement a comprehensive data strategy and implement real-time data science solutions in your enterprise. The book explains how to transform your company's raw data into actionable insights that drive business success. You'll learn how to transform your organization's (likely sizeable) collection of data into effective solutions that reduce expenses, raise service standards, and improve your customers' experiences. Inside the book: Strategies to implement real-time situation detection, perform fast and precise analyses, and facilitate informed decision-makingTechniques to bridge the gap between data collection and storage and the implementation of on-the-ground solutions that increase revenueWays to implement cognitive operations (CogOps) in your own enterprise, operationalizing cognitive (AI) decision-making in real-time Perfect for data engineers, data scientists, and domain subject matter experts, Generative Analytics and AI is also a must-read for product managers and technical administrators seeking a hands-on guide to turning large amounts of proprietary data into actionable insights and solutions.

Data-Directed Mathematical Modeling

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