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    Automated Machine Learning. Hyperparameter optimization, neural architecture search, and algorithm selection with cloud platforms Adnan Masood, Ahmed Sherif

    (ebook) (audiobook) (audiobook) Książka w języku 1
    Automated Machine Learning. Hyperparameter optimization, neural architecture search, and algorithm selection with cloud platforms Adnan Masood, Ahmed Sherif - okladka książki

    Automated Machine Learning. Hyperparameter optimization, neural architecture search, and algorithm selection with cloud platforms Adnan Masood, Ahmed Sherif - okladka książki

    Automated Machine Learning. Hyperparameter optimization, neural architecture search, and algorithm selection with cloud platforms Adnan Masood, Ahmed Sherif - audiobook MP3

    Automated Machine Learning. Hyperparameter optimization, neural architecture search, and algorithm selection with cloud platforms Adnan Masood, Ahmed Sherif - audiobook CD

    Autorzy:
    Adnan Masood, Ahmed Sherif
    Wydawnictwo:
    Packt Publishing (Z chęcią przeczytam książkę w języku polskim)
    Ocena:
    Bądź pierwszym, który oceni tę książkę
    Stron:
    312
    Dostępne formaty:
         PDF
         ePub
         Mobi
    Every machine learning engineer deals with systems that have hyperparameters, and the most basic task in automated machine learning (AutoML) is to automatically set these hyperparameters to optimize performance. The latest deep neural networks have a wide range of hyperparameters for their architecture, regularization, and optimization, which can be customized effectively to save time and effort.
    This book reviews the underlying techniques of automated feature engineering, model and hyperparameter tuning, gradient-based approaches, and much more. You'll discover different ways of implementing these techniques in open source tools and then learn to use enterprise tools for implementing AutoML in three major cloud service providers: Microsoft Azure, Amazon Web Services (AWS), and Google Cloud Platform. As you progress, you’ll explore the features of cloud AutoML platforms by building machine learning models using AutoML. The book will also show you how to develop accurate models by automating time-consuming and repetitive tasks in the machine learning development lifecycle.
    By the end of this machine learning book, you’ll be able to build and deploy AutoML models that are not only accurate, but also increase productivity, allow interoperability, and minimize feature engineering tasks.

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    O autorach książki

    Adnan Masood, PhD is an artificial intelligence and machine learning researcher, visiting scholar at Stanford AI Lab, software engineer, Microsoft MVP (Most Valuable Professional), and Microsoft's regional director for artificial intelligence. As chief architect of AI and machine learning at UST Global, he collaborates with Stanford AI Lab and MIT CSAIL, and leads a team of data scientists and engineers building artificial intelligence solutions to produce business value and insights that affect a range of businesses, products, and initiatives.
    Ahmed Sherif is a data scientist who has worked with data in various roles since 2005. He started off with BI solutions and transitioned to data science in 2013. In 2016, he obtained a master's in Predictive Analytics from Northwestern University, where he studied the science and application of machine learning and predictive modeling using both Python and R. Lately, he has been developing machine learning and deep learning solutions on the cloud using Azure. In 2016, he published his first book, Practical Business Intelligence. He currently works as a Technology Solution Profession in Data and AI for Microsoft.

    Adnan Masood, Ahmed Sherif - pozostałe książki

    Packt Publishing - inne książki

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