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Publicado por Packt Publishing 11/30/2023, 2023
ISBN 10: 1801070830 ISBN 13: 9781801070836
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Añadir al carritoPaperback or Softback. Condición: New. Machine Learning for Imbalanced Data: Tackle imbalanced datasets using machine learning and deep learning techniques 1.3. Book.
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Publicado por Packt Publishing Limited, GB, 2023
ISBN 10: 1801070830 ISBN 13: 9781801070836
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Añadir al carritoPaperback. Condición: New. This book will help you master the art of predicting outcomes when datasets are imbalanced and where the most important class is under-represented. You'll learn the skills you need to overcome challenges when dealing with imbalanced datasets for both traditional machine learning and deep learning models.
Publicado por Packt Publishing Limited, GB, 2023
ISBN 10: 1801070830 ISBN 13: 9781801070836
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Añadir al carritoPaperback. Condición: New. This book will help you master the art of predicting outcomes when datasets are imbalanced and where the most important class is under-represented. You'll learn the skills you need to overcome challenges when dealing with imbalanced datasets for both traditional machine learning and deep learning models.
Publicado por Packt Publishing Limited, GB, 2023
ISBN 10: 1801070830 ISBN 13: 9781801070836
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Añadir al carritoPaperback. Condición: New. This book will help you master the art of predicting outcomes when datasets are imbalanced and where the most important class is under-represented. You'll learn the skills you need to overcome challenges when dealing with imbalanced datasets for both traditional machine learning and deep learning models.
Publicado por Packt Publishing Limited, GB, 2023
ISBN 10: 1801070830 ISBN 13: 9781801070836
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Añadir al carritoPaperback. Condición: New. This book will help you master the art of predicting outcomes when datasets are imbalanced and where the most important class is under-represented. You'll learn the skills you need to overcome challenges when dealing with imbalanced datasets for both traditional machine learning and deep learning models.
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Publicado por Packt Publishing Limited, 2023
ISBN 10: 1801070830 ISBN 13: 9781801070836
Idioma: Inglés
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Añadir al carritoPAP. Condición: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
Publicado por Packt Publishing Limited, 2023
ISBN 10: 1801070830 ISBN 13: 9781801070836
Idioma: Inglés
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Publicado por Packt Publishing Limited, 2023
ISBN 10: 1801070830 ISBN 13: 9781801070836
Idioma: Inglés
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Librería: AHA-BUCH GmbH, Einbeck, Alemania
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Añadir al carritoTaschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Take your machine learning expertise to the next level with this essential guide, utilizing libraries like imbalanced-learn, PyTorch, scikit-learn, pandas, and NumPy to maximize model performance and tackle imbalanced dataKey FeaturesUnderstand how to use modern machine learning frameworks with detailed explanations, illustrations, and code samplesLearn cutting-edge deep learning techniques to overcome data imbalanceExplore different methods for dealing with skewed data in ML and DL applicationsPurchase of the print or Kindle book includes a free Elektronisches Buch in the PDF formatBook DescriptionAs machine learning practitioners, we often encounter imbalanced datasets in which one class has considerably fewer instances than the other. Many machine learning algorithms assume an equilibrium between majority and minority classes, leading to suboptimal performance on imbalanced data. This comprehensive guide helps you address this class imbalance to significantly improve model performance.Machine Learning for Imbalanced Data begins by introducing you to the challenges posed by imbalanced datasets and the importance of addressing these issues. It then guides you through techniques that enhance the performance of classical machine learning models when using imbalanced data, including various sampling and cost-sensitive learning methods.As you progress, you'll delve into similar and more advanced techniques for deep learning models, employing PyTorch as the primary framework. Throughout the book, hands-on examples will provide working and reproducible code that'll demonstrate the practical implementation of each technique.By the end of this book, you'll be adept at identifying and addressing class imbalances and confidently applying various techniques, including sampling, cost-sensitive techniques, and threshold adjustment, while using traditional machine learning or deep learning models.What you will learnUse imbalanced data in your machine learning models effectivelyExplore the metrics used when classes are imbalancedUnderstand how and when to apply various sampling methods such as over-sampling and under-samplingApply data-based, algorithm-based, and hybrid approaches to deal with class imbalanceCombine and choose from various options for data balancing while avoiding common pitfallsUnderstand the concepts of model calibration and threshold adjustment in the context of dealing with imbalanced datasetsWho this book is forThis book is for machine learning practitioners who want to effectively address the challenges of imbalanced datasets in their projects. Data scientists, machine learning engineers/scientists, research scientists/engineers, and data scientists/engineers will find this book helpful. Though complete beginners are welcome to read this book, some familiarity with core machine learning concepts will help readers maximize the benefits and insights gained from this comprehensive resource.Table of ContentsIntroduction to Data Imbalance in Machine LearningOversampling MethodsUndersampling MethodsEnsemble MethodsCost-Sensitive LearningData Imbalance in Deep LearningData-Level Deep Learning MethodsAlgorithm-Level Deep Learning TechniquesHybrid Deep Learning MethodsModel CalibrationAppendix.
Librería: Majestic Books, Hounslow, Reino Unido
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