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Hands-On Deep Learning with Apache Spark

You're reading from   Hands-On Deep Learning with Apache Spark Build and deploy distributed deep learning applications on Apache Spark

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Product type Paperback
Published in Jan 2019
Publisher Packt
ISBN-13 9781788994613
Length 322 pages
Edition 1st Edition
Languages
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Author (1):
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Guglielmo Iozzia Guglielmo Iozzia
Author Profile Icon Guglielmo Iozzia
Guglielmo Iozzia
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Table of Contents (19) Chapters Close

Preface 1. The Apache Spark Ecosystem FREE CHAPTER 2. Deep Learning Basics 3. Extract, Transform, Load 4. Streaming 5. Convolutional Neural Networks 6. Recurrent Neural Networks 7. Training Neural Networks with Spark 8. Monitoring and Debugging Neural Network Training 9. Interpreting Neural Network Output 10. Deploying on a Distributed System 11. NLP Basics 12. Textual Analysis and Deep Learning 13. Convolution 14. Image Classification 15. What's Next for Deep Learning? 16. Other Books You May Enjoy Appendix A: Functional Programming in Scala 1. Appendix B: Image Data Preparation for Spark

Image preprocessing

The approach described in this section, image preprocessing into batches of files, relies on the ND4J FileBatch class (https://static.javadoc.io/org.nd4j/nd4j-common/1.0.0-beta3/org/nd4j/api/loader/FileBatch.html), which is available starting from the 1.0.0-beta3 release of that library. This class can store the raw content of multiple files in byte arrays (one per file), including their original paths. A FileBatch object can be stored to disk in ZIP format. This can reduce the number of disk reads that are required (because of fewer files) and network transfers when reading from remote storage (because of the ZIP compression). Typically, the original image files that are used to train a CNN make use of an efficient (in terms of space and network) compression format (such as JPEG or PNG). But when it comes to a cluster, there is the need to minimize disk reads...

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