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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

Hands-on RNNs with Spark

Let's start now being hands-on with RNNs. This section is divided into two parts—the first one is about using DL4J to implement a network, while the second one will introduce using both DL4J and Spark for the same purpose. As with CNNs, you will discover that, thanks to the DL4J framework, lots of high-level facilities come out-of-the-box with it, so that the implementation process is easier than you might expect.

RNNs with DL4J

The first example presented in this chapter is an LSTM which, after the training, will recite the following characters once the first character of the learning string has been used as input for it.

The dependencies for this example are the following:

  • Scala 2.11...
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