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Librería: GreatBookPrices, Columbia, MD, Estados Unidos de America
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Librería: Ria Christie Collections, Uxbridge, Reino Unido
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Librería: GreatBookPricesUK, Woodford Green, Reino Unido
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Idioma: Inglés
Publicado por Packt Publishing Limited, 2023
ISBN 10: 1800208588 ISBN 13: 9781800208582
Librería: PBShop.store US, Wood Dale, IL, Estados Unidos de America
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Añadir al carritoPAP. Condición: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
Idioma: Inglés
Publicado por Packt Publishing Limited, 2023
ISBN 10: 1800208588 ISBN 13: 9781800208582
Librería: PBShop.store UK, Fairford, GLOS, Reino Unido
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Idioma: Inglés
Publicado por Packt Publishing Limited, 2023
ISBN 10: 1800208588 ISBN 13: 9781800208582
Librería: THE SAINT BOOKSTORE, Southport, Reino Unido
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Librería: Biblios, Frankfurt am main, HESSE, Alemania
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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 - Master reproducible ML and DL models with Python and PyTorch to achieve high performance, explainability, and real-world successKey Features:Learn how to improve performance of your models and eliminate model biasesStrategically design your machine learning systems to minimize chances of failure in productionDiscover advanced techniques to solve real-world challengesPurchase of the print or Kindle book includes a free PDF Elektronisches BuchBook Description:Debugging Machine Learning Models with Python is a comprehensive guide that navigates you through the entire spectrum of mastering machine learning, from foundational concepts to advanced techniques. It goes beyond the basics to arm you with the expertise essential for building reliable, high-performance models for industrial applications. Whether you're a data scientist, analyst, machine learning engineer, or Python developer, this book will empower you to design modular systems for data preparation, accurately train and test models, and seamlessly integrate them into larger technologies.By bridging the gap between theory and practice, you'll learn how to evaluate model performance, identify and address issues, and harness recent advancements in deep learning and generative modeling using PyTorch and scikit-learn. Your journey to developing high quality models in practice will also encompass causal and human-in-the-loop modeling and machine learning explainability. With hands-on examples and clear explanations, you'll develop the skills to deliver impactful solutions across domains such as healthcare, finance, and e-commerce.What You Will Learn:Enhance data quality and eliminate data flawsEffectively assess and improve the performance of your modelsDevelop and optimize deep learning models with PyTorchMitigate biases to ensure fairnessUnderstand explainability techniques to improve model qualitiesUse test-driven modeling for data processing and modeling improvementExplore techniques to bring reliable models to productionDiscover the benefits of causal and human-in-the-loop modelingWho this book is for:This book is for data scientists, analysts, machine learning engineers, Python developers, and students looking to build reliable, high-performance, and explainable machine learning models for production across diverse industrial applications. Fundamental Python skills are all you need to dive into the concepts and practical examples covered. Whether you're new to machine learning or an experienced practitioner, this book offers a breadth of knowledge and practical insights to elevate your modeling skills.
Librería: preigu, Osnabrück, Alemania
EUR 93,50
Cantidad disponible: 5 disponibles
Añadir al carritoTaschenbuch. Condición: Neu. Debugging Machine Learning Models with Python | Develop high-performance, low-bias, and explainable machine learning and deep learning models | Ali Madani | Taschenbuch | Englisch | 2023 | Packt Publishing | EAN 9781800208582 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.