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HANDS-ON MACHINE LEARNING WITH SCIKIT-LEARN AND By Aurelien Geron
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HANDS-ON MACHINE LEARNING WITH SCIKIT-LEARN AND By Aurelien Geron
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HANDS-ON MACHINE LEARNING WITH SCIKIT-LEARN AND By Aurelien Geron

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    Book Title
    Hands-On Machine Learning with Scikit-Learn and TensorFlow:
    ISBN-10
    1491962291
    ISBN
    9781491962299

    Über dieses Produkt

    Product Identifiers

    Publisher
    O'reilly Media, Incorporated
    ISBN-10
    1491962291
    ISBN-13
    9781491962299
    eBay Product ID (ePID)
    227662629

    Product Key Features

    Number of Pages
    572 Pages
    Publication Name
    Hands-On Machine Learning with Scikit-Learn and TensorFlow : Concepts, Tools, and Techniques to Build Intelligent Systems
    Language
    English
    Publication Year
    2017
    Subject
    Intelligence (Ai) & Semantics, Data Processing, Computer Vision & Pattern Recognition
    Type
    Textbook
    Subject Area
    Computers
    Author
    Aurélien Géron
    Format
    Trade Paperback

    Dimensions

    Item Height
    1.1 in
    Item Weight
    34.8 Oz
    Item Length
    9.2 in
    Item Width
    7.1 in

    Additional Product Features

    Intended Audience
    Trade
    LCCN
    2018-418542
    Illustrated
    Yes
    Synopsis
    Graphics in this book are printed in black and white . Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how. By using concrete examples, minimal theory, and two production-ready Python frameworks--scikit-learn and TensorFlow--author Aur lien G ron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You'll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you've learned, all you need is programming experience to get started. Explore the machine learning landscape, particularly neural nets Use scikit-learn to track an example machine-learning project end-to-end Explore several training models, including support vector machines, decision trees, random forests, and ensemble methods Use the TensorFlow library to build and train neural nets Dive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learning Learn techniques for training and scaling deep neural nets Apply practical code examples without acquiring excessive machine learning theory or algorithm details, Graphics in this book are printed in black and white . Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how. By using concrete examples, minimal theory, and two production-ready Python frameworks--scikit-learn and TensorFlow--author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You'll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you've learned, all you need is programming experience to get started. Explore the machine learning landscape, particularly neural nets Use scikit-learn to track an example machine-learning project end-to-end Explore several training models, including support vector machines, decision trees, random forests, and ensemble methods Use the TensorFlow library to build and train neural nets Dive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learning Learn techniques for training and scaling deep neural nets Apply practical code examples without acquiring excessive machine learning theory or algorithm details
    LC Classification Number
    Q325.5

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