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Deep Learning : A Practitioner's Approach - by Josh Patterson & Adam Gibson (Paperback)

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About this item

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
Number of Pages: 507
Genre: Computers + Internet
Sub-Genre: Database Management / Data Mining, Data Processing/General, Data Modeling + Design
Format: Paperback
Publisher: Oreilly & Associates Inc
Author: Josh Patterson & Adam Gibson
Language: English
Street Date: August 19, 2017
TCIN: 50100957
UPC: 9781491914250
Item Number (DPCI): 248-00-3752
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MSRPReg: $49.99 Save $7.50 (15% off)
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