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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.

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.

Dive into hyperparameter tuning of machine learning models and focus on what hyperparameters are and how they work. This book discusses different techniques of hyperparameters tuning, from the basics to advanced methods. This is a step-by-step guide to hyperparameter optimization, starting with what hyperparameters are and how they affect different aspects of machine learning models. It then goes through some basic (brute force) algorithms of hyperparameter optimization. Further, the author addresses the problem of time and memory constraints, using distributed optimization methods. Next you’ll discuss Bayesian optimization for hyperparameter search, which learns from its previous history. The book discusses different frameworks, such as Hyperopt and Optuna, which implements sequential model-based global optimization (SMBO) algorithms. During these discussions, you’ll focus on different aspects such as creation of search spaces and distributed optimization of these libraries. Hyperparameter Optimization in Machine Learning creates an understanding of how these algorithms work and how you can use them in real-life data science problems. The final chapter summaries the role of hyperparameter optimization in automated machine learning and ends with a tutorial to create your own AutoML script. Hyperparameter optimization is tedious task, so sit back and let these algorithms do your work. What You Will Learn Discover how changes in hyperparameters affect the model’s performance. Apply different hyperparameter tuning algorithms to data science problems Work with Bayesian optimization methods to create efficient machine learning and deep learning models Distribute hyperparameter optimization using a cluster of machines Approach automated machine learning using hyperparameter optimization Who This Book Is For Professionals and students working with machine learning.

Dive into hyperparameter tuning of machine learning models and focus on what hyperparameters are and how they work. This book discusses different techniques of hyperparameters tuning, from the basics to advanced methods. This is a step-by-step guide to hyperparameter optimization, starting with what hyperparameters are and how they affect different aspects of machine learning models. It then goes through some basic (brute force) algorithms of hyperparameter optimization. Further, the author addresses the problem of time and memory constraints, using distributed optimization methods. Next you’ll discuss Bayesian optimization for hyperparameter search, which learns from its previous history. The book discusses different frameworks, such as Hyperopt and Optuna, which implements sequential model-based global optimization (SMBO) algorithms. During these discussions, you’ll focus on different aspects such as creation of search spaces and distributed optimization of these libraries. Hyperparameter Optimization in Machine Learning creates an understanding of how these algorithms work and how you can use them in real-life data science problems. The final chapter summaries the role of hyperparameter optimization in automated machine learning and ends with a tutorial to create your own AutoML script. Hyperparameter optimization is tedious task, so sit back and let these algorithms do your work. What You Will Learn Discover how changes in hyperparameters affect the model’s performance. Apply different hyperparameter tuning algorithms to data science problems Work with Bayesian optimization methods to create efficient machine learning and deep learning models Distribute hyperparameter optimization using a cluster of machines Approach automated machine learning using hyperparameter optimization Who This Book Is For Professionals and students working with machine learning.

Die UMAX Smart Scale US10C ist eine intelligente Waage der neuen Generation, die fortschrittliche Technologie zur Überwachung des Gewichts und anderer Körperparameter verwendet. Die UMAX Smart Scale analysiert die Körperzusammensetzung anhand von bis zu 14 verschiedenen Parametern wie Körperfett, Skelettmuskulatur, Viszeralfett, Wasservolumen, Protein, metabolisches Alter oder BMI und misst das Gewicht mit Hilfe der bioelektrischen Impedanzanalyse (BIA). Die Waage ist drahtlos (Bluetooth) mit einer großartigen mobilen Anwendung in tschechischer Sprache verbunden, die es Ihnen ermöglicht, die gemessenen Daten zu überprüfen, um Ihren Zustand zu kontrollieren. Überwachen Sie Ihren Körper - 14 Körperparameter Gewicht BMI Körperfett Bewertung des Körpers Fettfrei Subkutanes Fett Viszerales Fett Wassermenge Knochen Skelettmuskeln Muskelproteine Knochen Proteine BMR Metabolisches Alter Tolle Bewerbung in tschechischer Sprache Bewerbung Das Handy für iOS und Android ist komplett auf Tschechisch und liefert nicht nur aktuelle Messergebnisse, sondern auch deren Historie. Während des Wiegens werden die Ergebnisse drahtlos an die App gesendet. Die Daten werden in einem Cloud-Konto gesichert, so dass sie nicht verloren gehen können. Messen Sie die ganze Familie und folgen Sie Ihren Freunden In der App können Sie ganz einfach weitere Familienmitglieder hinzufügen und deren Details zuweisen. Anhand Ihres Gewichts erkennt die App, zu wem Ihre Körperdaten gehören. Sie können auch Freunde per einladen, ihre körperlichen Parameter in der App zu verfolgen. In ähnlicher Weise wird Ihr Freund die Ergebnisse auf Ihrem Mobiltelefon sehen.

Neural networks as the commonly used machine learning algorithms, such as artificial neural networks (ANNs) and convolutional neural networks (CNNs), have been extensively used in the GIScience domain to explore the nonlinear and complex geographic phenomena. However, there are a few studies that investigate the parameter settings of neural networks in GIScience. Moreover, the model performance of neural networks often depends on the parameter setting for a given dataset. Meanwhile, adjusting the parameter configuration of neural networks will increase the overall running time. Therefore, an automated approach is necessary for addressing these limitations in current studies. This book proposes an automated spatially explicit hyperparameter optimization approach to identify optimal or near-optimal parameter settings for neural networks in the GIScience field. Also, the approach improves the computing performance at both model and computing levels. This book is writtenfor researchers of the GIScience field as well as social science subjects.

Neural networks as the commonly used machine learning algorithms, such as artificial neural networks (ANNs) and convolutional neural networks (CNNs), have been extensively used in the GIScience domain to explore the nonlinear and complex geographic phenomena. However, there are a few studies that investigate the parameter settings of neural networks in GIScience. Moreover, the model performance of neural networks often depends on the parameter setting for a given dataset. Meanwhile, adjusting the parameter configuration of neural networks will increase the overall running time. Therefore, an automated approach is necessary for addressing these limitations in current studies. This book proposes an automated spatially explicit hyperparameter optimization approach to identify optimal or near-optimal parameter settings for neural networks in the GIScience field. Also, the approach improves the computing performance at both model and computing levels. This book is writtenfor researchers of the GIScience field as well as social science subjects.

Neural networks as the commonly used machine learning algorithms, such as artificial neural networks (ANNs) and convolutional neural networks (CNNs), have been extensively used in the GIScience domain to explore the nonlinear and complex geographic phenomena. However, there are a few studies that investigate the parameter settings of neural networks in GIScience. Moreover, the model performance of neural networks often depends on the parameter setting for a given dataset. Meanwhile, adjusting the parameter configuration of neural networks will increase the overall running time. Therefore, an automated approach is necessary for addressing these limitations in current studies. This book proposes an automated spatially explicit hyperparameter optimization approach to identify optimal or near-optimal parameter settings for neural networks in the GIScience field. Also, the approach improves the computing performance at both model and computing levels. This book is writtenfor researchers of the GIScience field as well as social science subjects.

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UMAX Smart Scale US30HRCUMAX Smart Scale US30H ist eine intelligente Waage der nächsten Generation, die fortschrittliche Technologie nutzt, um das Gewicht und andere Körperparameter zu messen. Die Smart Scale analysiert die Körperzusammensetzung, bis zu 15 verschiedene Parameter, und misst das Gewicht mittels bioelektrischer Impedanzanalyse (BIA). Die Waage ist drahtlos über Bluetooth 4.0 mit der Feelfit Health Fitness Tool Mobile App verbunden, die auch in englischer Sprache verfügbar ist und mit der Sie die Messungen überprüfen können, die Sie benötigen, um Ihre Fitness zu kontrollieren.GRUNDLEGENDE SPEZIFIKATIONENMerkmale: Gewicht, Herzfrequenz, BMI, Körperfett, Körperbewertung, fettfreies Gewicht, subkutanes Fett, viszerales Fett, Wasservolumen, Skelettmuskel, Muskelmasse, Knochenmasse, Protein, BMR, metabolisches Alter Gewichtskapazität: 0,2-180 kg Unterschied: 50 g Akku-Technologie: Li-Ion Akku-Kapazität: 200 mAh Schnittstelle: Micro USB Display: ja

The relay feedback test (RFT) has become a popular and efficient in process identification and automatic controller tuning. Non-parametric Tuning of PID Controllers couples new modifications of classical RFT with application-specific optimal tuning rules to form a non-parametric method of test-and-tuning. Test and tuning are coordinated through a set of common parameters so that a PID controller can obtain the desired gain or phase margins in a system exactly, even with unknown process dynamics. The concept of process-specific optimal tuning rules in the nonparametric setup, with corresponding tuning rules for flow, level pressure, and temperature control loops is presented in the text. Common problems of tuning accuracy based on parametric and non-parametric approaches are addressed. In addition, the text treats the parametric approach to tuning based on the modified RFT approach and the exact model of oscillations in the system under test using the locus of a perturbedrelay system (LPRS) method. Industrial loop tuning for distributed control systems using modified RFT is also described. Many of the problems of tuning rules optimization and identification with modified RFT are accompanied by MATLAB® code, downloadable from http://extras.springer.com/978-1-4471-4464-9 to allow the reader to duplicate the results. Non-parametric Tuning of PID Controllers is written for readers with previous knowledge of linear control and will be of interest to academic control researchers and graduate students and to practitioners working in a variety of chemical- mechanical- and process-engineering-related industries.

The relay feedback test (RFT) has become a popular and efficient in process identification and automatic controller tuning. Non-parametric Tuning of PID Controllers couples new modifications of classical RFT with application-specific optimal tuning rules to form a non-parametric method of test-and-tuning. Test and tuning are coordinated through a set of common parameters so that a PID controller can obtain the desired gain or phase margins in a system exactly, even with unknown process dynamics. The concept of process-specific optimal tuning rules in the nonparametric setup, with corresponding tuning rules for flow, level pressure, and temperature control loops is presented in the text. Common problems of tuning accuracy based on parametric and non-parametric approaches are addressed. In addition, the text treats the parametric approach to tuning based on the modified RFT approach and the exact model of oscillations in the system under test using the locus of a perturbedrelay system (LPRS) method. Industrial loop tuning for distributed control systems using modified RFT is also described. Many of the problems of tuning rules optimization and identification with modified RFT are accompanied by MATLAB® code, downloadable from http://extras.springer.com/978-1-4471-4464-9 to allow the reader to duplicate the results. Non-parametric Tuning of PID Controllers is written for readers with previous knowledge of linear control and will be of interest to academic control researchers and graduate students and to practitioners working in a variety of chemical- mechanical- and process-engineering-related industries.