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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

Monitoring and Debugging Neural Network Training

The previous chapter focused on training Multilayer Neural Networks (MNNs), and presenting code examples for CNNs and RNNs in particular. This chapter describes how monitoring a network can be done while training is in progress and how to use this monitoring information to tune a model. DL4J provides UI facilities for monitoring and tuning purposes, and will be the centerpiece of this chapter. These facilities also work in a training context with DL4J and Apache Spark. Examples for both situations (training using DL4J only and DL4J with Spark) will be presented. A list of potential baseline steps or tips for network training will also be discussed.

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