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    Machine Learning for Imbalanced Data. Tackle imbalanced datasets using machine learning and deep learning techniques

    (ebook) (audiobook) (audiobook) Książka w języku 1
    Autorzy:
    Kumar Abhishek, Dr. Mounir Abdelaziz
    Machine Learning for Imbalanced Data. Tackle imbalanced datasets using machine learning and deep learning techniques Kumar Abhishek, Dr. Mounir Abdelaziz - okladka książki

    Machine Learning for Imbalanced Data. Tackle imbalanced datasets using machine learning and deep learning techniques Kumar Abhishek, Dr. Mounir Abdelaziz - okladka książki

    Machine Learning for Imbalanced Data. Tackle imbalanced datasets using machine learning and deep learning techniques Kumar Abhishek, Dr. Mounir Abdelaziz - audiobook MP3

    Machine Learning for Imbalanced Data. Tackle imbalanced datasets using machine learning and deep learning techniques Kumar Abhishek, Dr. Mounir Abdelaziz - audiobook CD

    Wydawnictwo:
    Packt Publishing (Z chęcią przeczytam książkę w języku polskim)
    Ocena:
    Bądź pierwszym, który oceni tę książkę
    Stron:
    344
    Dostępne formaty:
         PDF
         ePub
    As 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.

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    O autorach książki

    Kumar Abhishek is a seasoned Senior Machine Learning Engineer at Expedia Group, US, specializing in risk analysis and fraud detection for Expedia brands. With over a decade of experience at companies such as Microsoft, Amazon, and a Bay Area startup, Kumar holds an MS in Computer Science from the University of Florida.
    Dr. Mounir Abdelaziz is a deep learning researcher specializing in computer vision applications. He holds a Ph.D. in computer science and technology from Central South University, China. During his Ph.D. journey, he developed innovative algorithms to address practical computer vision challenges. He has also authored numerous research articles in the field of few-shot learning for image classification.

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