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Deep Learning

ebook

Although interest in machine learning has reached a high point, lofty expectations often scuttle projects before they get very far. How can machine learning—especially deep neural networks—make a real difference in your organization? This hands-on guide not only provides the most practical information available on the subject, but also helps you get started building efficient deep learning networks.

Authors Adam Gibson and Josh Patterson provide theory on deep learning before introducing their open-source Deeplearning4j (DL4J) library for developing production-class workflows. Through real-world examples, you'll learn methods and strategies for training deep network architectures and running deep learning workflows on Spark and Hadoop with DL4J.

  • Dive into machine learning concepts in general, as well as deep learning in particular
  • Understand how deep networks evolved from neural network fundamentals
  • Explore the major deep network architectures, including Convolutional and Recurrent
  • Learn how to map specific deep networks to the right problem
  • Walk through the fundamentals of tuning general neural networks and specific deep network architectures
  • Use vectorization techniques for different data types with DataVec, DL4J's workflow tool
  • Learn how to use DL4J natively on Spark and Hadoop

  • Expand title description text
    Publisher: O'Reilly Media

    Kindle Book

    • Release date: July 28, 2017

    OverDrive Read

    • ISBN: 9781491914212
    • File size: 18151 KB
    • Release date: July 28, 2017

    EPUB ebook

    • ISBN: 9781491914212
    • File size: 18102 KB
    • Release date: July 28, 2017

    Formats

    Kindle Book
    OverDrive Read
    EPUB ebook

    Languages

    English

    Although interest in machine learning has reached a high point, lofty expectations often scuttle projects before they get very far. How can machine learning—especially deep neural networks—make a real difference in your organization? This hands-on guide not only provides the most practical information available on the subject, but also helps you get started building efficient deep learning networks.

    Authors Adam Gibson and Josh Patterson provide theory on deep learning before introducing their open-source Deeplearning4j (DL4J) library for developing production-class workflows. Through real-world examples, you'll learn methods and strategies for training deep network architectures and running deep learning workflows on Spark and Hadoop with DL4J.

  • Dive into machine learning concepts in general, as well as deep learning in particular
  • Understand how deep networks evolved from neural network fundamentals
  • Explore the major deep network architectures, including Convolutional and Recurrent
  • Learn how to map specific deep networks to the right problem
  • Walk through the fundamentals of tuning general neural networks and specific deep network architectures
  • Use vectorization techniques for different data types with DataVec, DL4J's workflow tool
  • Learn how to use DL4J natively on Spark and Hadoop

  • Expand title description text