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Sailer U: Digitales Controlling

Digitales Controlling

Technologien, Trends, Transformation

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“.

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.

Handbuch Sport und Sportwissenschaft

Digitalisierung und Innovation im Sport und in der Sportwissenschaft

The Laws of Thought

20 Lessons from the Fieldwork

Data Science for Teams

A complete guide to deep neural networks – the technology behind AI – covering fundamental and advanced techniques to apply machine learning in real-world scenarios. Deep Learning Crash Course goes beyond the basics of machine learning to delve into modern techniques and applications of great interest right now, and whose popularity will only grow in the future. The book covers topics such as generative models (the technology behind deep fakes), self-supervised learning, attention mechanisms (the tech behind ChatGPT), graph neural networks (the tech behind AlphaFold), and deep reinforcement learning (the tech behind AlphaGo). This book bridges the gap between theory and practice, helping readers gain the confidence to apply deep learning in their work.

A complete guide to deep neural networks – the technology behind AI – covering fundamental and advanced techniques to apply machine learning in real-world scenarios. Deep Learning Crash Course goes beyond the basics of machine learning to delve into modern techniques and applications of great interest right now, and whose popularity will only grow in the future. The book covers topics such as generative models (the technology behind deep fakes), self-supervised learning, attention mechanisms (the tech behind ChatGPT), graph neural networks (the tech behind AlphaFold), and deep reinforcement learning (the tech behind AlphaGo). This book bridges the gap between theory and practice, helping readers gain the confidence to apply deep learning in their work.

Deep Learning Crash Course

Machine Learning For Dummies

This companion volume to Artificial Intelligence: A Guide for Everyone offers a comprehensive exploration of AI analytics, catering to individuals of all backgrounds and expertise levels. It seeks to demystify AI analytics by exploring core concepts, explaining various stages (Descriptive, Diagnostic, Predictive, and Prescriptive), and highlighting their limitations. Through domain-specific applications, it illustrates how AI is utilized across industries for innovation and efficiency, providing dedicated subsections for healthcare, finance, customer support, and more.Artificial Intelligence: Analytics, Platforms, and Risks thoroughly examines the AI infrastructure and ecosystem, offering insights into frameworks, platforms, tools, key players, and the critical hardware and software components supporting AI applications. It also addresses the multifaceted risks and challenges of AI, such as bad data, model selection bias, job displacement, weaponization, and ethical dilemmas, along with strategies to mitigate these risks.&nbsp;Additionally, it discusses the hurdles in AI adoption, including roadblocks, myths, planning complexities, resource requirements, and data and model challenges. Looking ahead, the book explores the future of AI, highlighting catalysts for technological progress, the transformative impact across industries, and emerging trends shaping the field. With the aim of empowering readers to navigate the complexities of AI, harness its potential, and contribute to its responsible and ethical advancement, this book serves as a comprehensive guide to AI technologies.

This companion volume to Artificial Intelligence for Everyone offers a comprehensive exploration of AI analytics, catering to individuals of all backgrounds and expertise levels. It seeks to demystify AI analytics by exploring core concepts, explaining various stages (descriptive, predictive, prescriptive), and highlighting their limitations. Through domain-specific applications, it illustrates how AI is utilized across industries for innovation and efficiency, providing dedicated subsections for healthcare, finance, customer support, and more.Artificial Intelligence: Analytics, Platforms, and Risks thoroughly examines the AI infrastructure and ecosystem, offering insights into frameworks, platforms, tools, key players, and the critical hardware and software components supporting AI applications. It also addresses the multifaceted risks and challenges of AI, such as bad data, model selection bias, job displacement, weaponization, and ethical dilemmas, along with strategies to mitigate these risks. Additionally, it discusses the hurdles in AI adoption, including roadblocks, myths, planning complexities, resource requirements, and data and model challenges. Looking ahead, the book explores the future of AI, highlighting catalysts for technological progress, the transformative impact across industries, and emerging trends shaping the field. With the aim of empowering readers to navigate the complexities of AI, harness its potential, and contribute to its responsible and ethical advancement, this book serves as a comprehensive guide to AI technologies.

Artificial Intelligence: Analytics, Platforms, and Risks

Learn how to manage the risks and opportunities of agentic AI with this practical guide to ensuring the safe and beneficial developments of advanced AI systems.

Principles and Responsible Practices

Safer Agentic AI

Encyclopedia of Optimization

This book is the first one that shows how to accurately and efficiently solve time-varying problems in real-time or online using continuous- or discrete-time zeroing dynamics. The authors provide a comprehensive treatment of the theory of both static and dynamic neural networks. They develop, analyze, model, simulate, and compare zeroing dynamics models for the online solution of numerous time-varying problems, such as root finding, nonlinear equation solving, matrix inversion, matrix square root finding, quadratic optimization, and inequality solving.

Neural networks and neural dynamics are powerful approaches for the online solution of mathematical problems arising in many areas of science, engineering, and business. Compared with conventional gradient neural networks that only deal with static problems of constant coefficient matrices and vectors, the authors’ new method called zeroing dynamics solves time-varying problems. Zeroing Dynamics, Gradient Dynamics, and Newton Iterations is the first book that shows how to accurately and efficiently solve time-varying problems in real-time or online using continuous- or discrete-time zeroing dynamics. The book brings together research in the developing fields of neural networks, neural dynamics, computer mathematics, numerical algorithms, time-varying computation and optimization, simulation and modeling, analog and digital hardware, and fractals.The authors provide a comprehensive treatment of the theory of both static and dynamic neural networks. Readers will discover how novel theoretical results have been successfully applied to many practical problems. The authors develop, analyze, model, simulate, and compare zeroing dynamics models for the online solution of numerous time-varying problems, such as root finding, nonlinear equation solving, matrix inversion, matrix square root finding, quadratic optimization, and inequality solving.

Zeroing Dynamics, Gradient Dynamics, and Newton Iterations

Chapman & Hall/CRC Data Mining and Knowledge Discovery Series

As data continues to grow exponentially, knowledge of data science and machine learning has become more crucial than ever. Machine learning has grown exponentially, however, the abundance of resources can be overwhelming

As data continues to grow exponentially, knowledge of data science and machine learning has become more crucial than ever. Machine learning has grown exponentially, however, the abundance of resources can be overwhelming

Digitales Controlling

Bibliografische Angaben

Schlagworte

Bibliografische Angaben

Schlagworte

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