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Publicado por Manning Publications, US, 2021
ISBN 10: 1617296724 ISBN 13: 9781617296727
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Añadir al carritoPaperback. Condición: New. Most businesses are far more interested in accurate forecasting and fraud detection using their existing structured datasets than identifying cats in YouTube videos. Powerful deep learning techniques can efficiently extract insight from the kind of structured data collected by most businesses and organisations. Deep learning offers the potential to identify complex patterns and relationships hidden in data of all sorts. Deep Learning with Structured Data shows you how to apply powerful deep learning analysis techniques to the kind of structured, tabular data you'll find in the relational databases that real-world businesses depend on. Filled with practical, relevant applications, this book teaches you how deep learning can augment your existing machine learning and business intelligence systems. . The benefits and drawbacks of deep learning . Organizing data for your deep learning model . The deep learning stack . Measuring performance of your models For readers with an intermediate knowledge of Python, Jupyter notebooks, and machine learning.
Idioma: Inglés
Publicado por Manning Publications, New York, 2021
ISBN 10: 1617296724 ISBN 13: 9781617296727
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Añadir al carritoPaperback. Condición: new. Paperback. Most businesses are far more interested in accurate forecasting and fraud detection using their existing structured datasets than identifying cats in YouTube videos. Powerful deep learning techniques can efficiently extract insight from the kind of structured data collected by most businesses and organisations. Deep learning offers the potential to identify complex patterns and relationships hidden in data of all sorts. Deep Learning with Structured Data shows you how to apply powerful deep learning analysis techniques to the kind of structured, tabular data you'll find in the relational databases that real-world businesses depend on. Filled with practical, relevant applications, this book teaches you how deep learning can augment your existing machine learning and business intelligence systems. The benefits and drawbacks of deep learning Organizing data for your deep learning model The deep learning stack Measuring performance of your models For readers with an intermediate knowledge of Python, Jupyter notebooks, and machine learning. "For readers with intermediate Python and machine learning skills"--Back cover. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Publicado por Manning Publications Company, 2020
ISBN 10: 1617296724 ISBN 13: 9781617296727
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Idioma: Inglés
Publicado por Manning Publications Company, 2020
ISBN 10: 1617296724 ISBN 13: 9781617296727
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Idioma: Inglés
Publicado por Manning Publications Company, 2020
ISBN 10: 1617296724 ISBN 13: 9781617296727
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ISBN 10: 1617296724 ISBN 13: 9781617296727
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Añadir al carritoPaperback. Condición: new. Paperback. Most businesses are far more interested in accurate forecasting and fraud detection using their existing structured datasets than identifying cats in YouTube videos. Powerful deep learning techniques can efficiently extract insight from the kind of structured data collected by most businesses and organisations. Deep learning offers the potential to identify complex patterns and relationships hidden in data of all sorts. Deep Learning with Structured Data shows you how to apply powerful deep learning analysis techniques to the kind of structured, tabular data you'll find in the relational databases that real-world businesses depend on. Filled with practical, relevant applications, this book teaches you how deep learning can augment your existing machine learning and business intelligence systems. The benefits and drawbacks of deep learning Organizing data for your deep learning model The deep learning stack Measuring performance of your models For readers with an intermediate knowledge of Python, Jupyter notebooks, and machine learning. "For readers with intermediate Python and machine learning skills"--Back cover. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
Idioma: Inglés
Publicado por Manning Publications, US, 2021
ISBN 10: 1617296724 ISBN 13: 9781617296727
Librería: Rarewaves USA United, OSWEGO, IL, Estados Unidos de America
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Añadir al carritoPaperback. Condición: New. Most businesses are far more interested in accurate forecasting and fraud detection using their existing structured datasets than identifying cats in YouTube videos. Powerful deep learning techniques can efficiently extract insight from the kind of structured data collected by most businesses and organisations. Deep learning offers the potential to identify complex patterns and relationships hidden in data of all sorts. Deep Learning with Structured Data shows you how to apply powerful deep learning analysis techniques to the kind of structured, tabular data you'll find in the relational databases that real-world businesses depend on. Filled with practical, relevant applications, this book teaches you how deep learning can augment your existing machine learning and business intelligence systems. . The benefits and drawbacks of deep learning . Organizing data for your deep learning model . The deep learning stack . Measuring performance of your models For readers with an intermediate knowledge of Python, Jupyter notebooks, and machine learning.
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Añadir al carritoKartoniert / Broschiert. Condición: New. Über den AutorMark Ryan has 20 years of experience leading teams delivering IBM s premier relational database product. He holds a Master s degree in Computer Science from the University of Toronto.Klappentext.
Idioma: Inglés
Publicado por Manning Publications Dez 2020, 2020
ISBN 10: 1617296724 ISBN 13: 9781617296727
Librería: AHA-BUCH GmbH, Einbeck, Alemania
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Añadir al carritoTaschenbuch. Condición: Neu. Neuware - Deep Learning with Structured Data teaches you powerful data analysis techniques for tabular data and relational databases.Summary Deep learning offers the potential to identify complex patterns and relationships hidden in data of all sorts. Deep Learning with Structured Data shows you how to apply powerful deep learning analysis techniques to the kind of structured, tabular data you'll find in the relational databases that real-world businesses depend on. Filled with practical, relevant applications, this book teaches you how deep learning can augment your existing machine learning and business intelligence systems. Purchase of the print book includes a free Elektronisches Buch in PDF, Kindle, and ePub formats from Manning Publications. About the technology Here's a dirty secret: Half of the time in most data science projects is spent cleaning and preparing data. But there's a better way: Deep learning techniques optimized for tabular data and relational databases deliver insights and analysis without requiring intense feature engineering. Learn the skills to unlock deep learning performance with much less data filtering, validating, and scrubbing. About the book Deep Learning with Structured Data teaches you powerful data analysis techniques for tabular data and relational databases. Get started using a dataset based on the Toronto transit system. As you work through the book, you'll learn how easy it is to set up tabular data for deep learning, while solving crucial production concerns like deployment and performance monitoring. What's inside When and where to use deep learning The architecture of a Keras deep learning model Training, deploying, and maintaining models Measuring performance About the reader For readers with intermediate Python and machine learning skills. About the author Mark Ryan is a Data Science Manager at Intact Insurance. He holds a Master's degree in Computer Science from the University of Toronto. Table of Contents 1 Why deep learning with structured data 2 Introduction to the example problem and Pandas dataframes 3 Preparing the data, part 1: Exploring and cleansing the data 4 Preparing the data, part 2: Transforming the data 5 Preparing and building the model 6 Training the model and running experiments 7 More experiments with the trained model 8 Deploying the model 9 Recommended next steps.