9781491914250-1491914254-Deep Learning: A Practitioner's Approach

Deep Learning: A Practitioner's Approach

ISBN-13: 9781491914250
ISBN-10: 1491914254
Edition: 1
Author: Adam Gibson, Josh Patterson
Publication date: 2017
Publisher: Oreilly & Associates Inc
Format: Paperback 507 pages
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Book details

ISBN-13: 9781491914250
ISBN-10: 1491914254
Edition: 1
Author: Adam Gibson, Josh Patterson
Publication date: 2017
Publisher: Oreilly & Associates Inc
Format: Paperback 507 pages

Summary

Deep Learning: A Practitioner's Approach (ISBN-13: 9781491914250 and ISBN-10: 1491914254), written by authors Adam Gibson, Josh Patterson, was published by Oreilly & Associates Inc in 2017. With an overall rating of 4.3 stars, it's a notable title among other Data Modeling & Design (Databases & Big Data, Data Mining, Data Processing) books. You can easily purchase or rent Deep Learning: A Practitioner's Approach (Paperback) from BooksRun, along with many other new and used Data Modeling & Design books and textbooks. And, if you're looking to sell your copy, our current buyback offer is $1.19.

Description

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
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