eva bartz (28 Ergebnisse)

PAP. Zustand: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Zustand: New. New Book. Shipped from UK. Established seller since 2000.

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Paperback. Zustand: New. New paperback. An unread/unopened copy. May have VERY minor rubbing and scuffing from shelf wear.; 100% Satisfaction Guaranteed! Ships same or next business day!

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Paperback. Zustand: Brand New. 340 pages. 9.25x6.10x0.72 inches. In Stock.

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Zustand: New. Food is more than just nutrition. Its preparation, presentation and consumption is a multifold communicative practice which includes the meal s design and its whole field of experience. How is food represented in cookbooks, product packaging or in paintings.

Paperback. Zustand: Brand New. 310 pages. 8.86x5.83x0.98 inches. In Stock.

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Hardcover. Zustand: Brand New. 340 pages. 9.25x6.10x9.21 inches. In Stock.

Hardcover. Zustand: Brand New. 168 pages. 9.26x6.10x9.21 inches. In Stock.

Taschenbuch. Zustand: Neu. Neuware - Food is more than just nutrition. Its preparation, presentation and consumption is a multifold communicative practice which includes the meal's design and its whole field of experience. How is food represented in cookbooks, product packaging or in paintings How is dining semantically charged How is the sensuality of eating treated in different cultural contexts In order to acknowledge the material and media-related aspects of eating as a cultural praxis, experts from media studies, art history, literary studies, philosophy, experimental psychology, anthropology, food studies, cultural studies and design studies share their specific approaches.

Taschenbuch. Zustand: Neu. Hyperparameter Tuning for Machine and Deep Learning with R | A Practical Guide | Eva Bartz (u. a.) | Taschenbuch | xvii | Englisch | 2022 | Springer | EAN 9789811951725 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.

Taschenbuch. Zustand: Neu. Food - Media - Senses | Interdisciplinary Approaches | Christina Bartz (u. a.) | Taschenbuch | Großformatiges Paperback. Klappenbroschur | 330 S. | Englisch | 2023 | transcript | EAN 9783837664799 | Verantwortliche Person für die EU: transcript Verlag, Gero Wierichs, Hermannstr. 26, 33602 Bielefeld, live[at]transcript-verlag[dot]de | Anbieter: preigu.

Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This open access book provides a wealth of hands-on examples that illustrate how hyperparameter tuning can be applied in practice and gives deep insights into the working mechanisms of machine learning (ML) and deep learning (DL) methods. The aim of the book is to equip readers with the ability to achieve better results with significantly less time, costs, effort and resources using the methods described here.The case studies presented in this book can be run on a regular desktop or notebook computer. No high-performance computing facilities are required. The idea for the book originated in a study conducted by Bartz & Bartz GmbH for the Federal Statistical Office of Germany (Destatis). Building on that study, the book is addressed to practitioners in industry as well as researchers, teachers and students in academia. The content focuses on the hyperparameter tuning of ML and DL algorithms, and is divided into two main parts: theory (Part I) and application (Part II).Essential topics covered include: a survey of important model parameters; four parameter tuning studies and one extensive global parameter tuning study; statistical analysis of the performance of ML and DL methods based on severity; and a new, consensus-ranking-based way to aggregate and analyze results from multiple algorithms. The book presents analyses of more than 30 hyperparameters from six relevant ML and DL methods, and provides source code so that users can reproduce the results. Accordingly, it serves as a handbook and textbook alike.

Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering.

Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This open access book provides a wealth of hands-on examples that illustrate how hyperparameter tuning can be applied in practice and gives deep insights into the working mechanisms of machine learning (ML) and deep learning (DL) methods. The aim of the book is to equip readers with the ability to achieve better results with significantly less time, costs, effort and resources using the methods described here.The case studies presented in this book can be run on a regular desktop or notebook computer. No high-performance computing facilities are required. The idea for the book originated in a study conducted by Bartz & Bartz GmbH for the Federal Statistical Office of Germany (Destatis). Building on that study, the book is addressed to practitioners in industry as well as researchers, teachers and students in academia. The content focuses on the hyperparameter tuning of ML and DL algorithms, and is divided into two main parts: theory (Part I) and application (Part II).Essential topics covered include: a survey of important model parameters; four parameter tuning studies and one extensive global parameter tuning study; statistical analysis of the performance of ML and DL methods based on severity; and a new, consensus-ranking-based way to aggregate and analyze results from multiple algorithms. The book presents analyses of more than 30 hyperparameters from six relevant ML and DL methods, and provides source code so that users can reproduce the results. Accordingly, it serves as a handbook and textbook alike.

Taschenbuch. Zustand: Neu. Online Machine Learning | A Practical Guide with Examples in Python | Eva Bartz (u. a.) | Taschenbuch | Machine Learning: Foundations, Methodologies, and Applications | xiii | Englisch | 2025 | Springer | EAN 9789819970094 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.

Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering.

Zustand: Sehr gut. Zustand: Sehr gut | Seiten: 344 | Sprache: Englisch | Produktart: Bücher | This open access book provides a wealth of hands-on examples that illustrate how hyperparameter tuning can be applied in practice and gives deep insights into the working mechanisms of machine learning (ML) and deep learning (DL) methods. The aim of the book is to equip readers with the ability to achieve better results with significantly less time, costs, effort and resources using the methods described here. The case studies presented in this book can be run on a regular desktop or notebook computer. No high-performance computing facilities are required. The idea for the book originated in a study conducted by Bartz & Bartz GmbH for the Federal Statistical Office of Germany (Destatis). Building on that study, the book is addressed to practitioners in industry as well as researchers, teachers and students in academia. The content focuses on the hyperparameter tuning of ML and DL algorithms, and is divided into two main parts: theory (Part I) and application (Part II).Essential topics covered include: a survey of important model parameters; four parameter tuning studies and one extensive global parameter tuning study; statistical analysis of the performance of ML and DL methods based on severity; and a new, consensus-ranking-based way to aggregate and analyze results from multiple algorithms. The book presents analyses of more than 30 hyperparameters from six relevant ML and DL methods, and provides source code so that users can reproduce the results. Accordingly, it serves as a handbook and textbook alike.

Zustand: Hervorragend. Zustand: Hervorragend | Seiten: 344 | Sprache: Englisch | Produktart: Bücher | This open access book provides a wealth of hands-on examples that illustrate how hyperparameter tuning can be applied in practice and gives deep insights into the working mechanisms of machine learning (ML) and deep learning (DL) methods. The aim of the book is to equip readers with the ability to achieve better results with significantly less time, costs, effort and resources using the methods described here. The case studies presented in this book can be run on a regular desktop or notebook computer. No high-performance computing facilities are required. The idea for the book originated in a study conducted by Bartz & Bartz GmbH for the Federal Statistical Office of Germany (Destatis). Building on that study, the book is addressed to practitioners in industry as well as researchers, teachers and students in academia. The content focuses on the hyperparameter tuning of ML and DL algorithms, and is divided into two main parts: theory (Part I) and application (Part II).Essential topics covered include: a survey of important model parameters; four parameter tuning studies and one extensive global parameter tuning study; statistical analysis of the performance of ML and DL methods based on severity; and a new, consensus-ranking-based way to aggregate and analyze results from multiple algorithms. The book presents analyses of more than 30 hyperparameters from six relevant ML and DL methods, and provides source code so that users can reproduce the results. Accordingly, it serves as a handbook and textbook alike.

Zustand: Wie Neu. Zustandsbeschreibung: leichte Lagerspuren. Eine praxisorientierte Einführung. Hrsg. von Thomas Bartz-Beielstein und Eva Bartz. Dieses Buch beschreibt Theorie und Anwendungen aus dem Bereich des Online Maschine Learnings (OML), wobei der Fokus auf Verfahren des überwachten Lernens liegt. Es werden Verfahren zur Drifterkennung und -behandlung beschrieben. Verfahren zur nachträglichen Aktualisierung der Modelle sowie Methoden zur Modellbewertung werden dargestellt. Besondere Anforderungen aus der amtlichen Statistik (unbalancierte Daten, Interpretierbarkeit, etc.) werden berücksichtigt. Aktuelle und mögliche Anwendungen werden aufgelistet. Ein Überblick über die verfügbaren Software-Tools wird gegeben. Anhand von zwei Studien ("simulierten Anwendungen") werden Vor- und Nachteile des OML-Einsatz in der Praxis experimentell analysiert. XV,154 Seiten mit 40 Farb- und 12 s/w-Abb., broschiert (Springer Vieweg 2024). Statt EUR 32,99. Gewicht: 315 g - Softcover/Taschenbuch.

Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - Dieses Buch beschreibt Theorie und Anwendungen aus dem Bereich des Online Maschine Learnings (OML), wobei der Fokus auf Verfahren des überwachten Lernens liegt. Es werden Verfahren zur Drifterkennung und -behandlung beschrieben. Verfahren zur nachträglichen Aktualisierung der Modelle sowie Methoden zur Modellbewertung werden dargestellt. Besondere Anforderungen aus der amtlichen Statistik (unbalancierte Daten, Interpretierbarkeit, etc.) werden berücksichtigt. Aktuelle und mögliche Anwendungen werden aufgelistet. Ein Überblick über die verfügbaren Software-Tools wird gegeben. Anhand von zwei Studien ('simulierten Anwendungen') werden Vor- und Nachteile des OML-Einsatz in der Praxis experimentell analysiert.Das Buch eignet sich als Handbuch für Experten, Lehrbuch für Anfänger und wissenschaftliche Publikation, da es den neuesten Stand der Forschung wiedergibt. Es kann auch als OML-Consulting dienen, indem Entscheider und Praktiker OML anpassen und für ihre Anwendung einsetzen, um abzuwägen, ob die Vorteile die Kosten aufwiegen.

Taschenbuch. Zustand: Neu. Online Machine Learning | Eine praxisorientierte Einführung | Thomas Bartz-Beielstein (u. a.) | Taschenbuch | xiii | Deutsch | 2024 | Springer Fachmedien Wiesbaden | EAN 9783658461614 | Verantwortliche Person für die EU: Springer Vieweg in Springer Science + Business Media, Abraham-Lincoln-Str. 46, 65189 Wiesbaden, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.