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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

DNNs overview

As stated in the previous section, a DNN is an ANN with multiple hidden layers between the input and output layers. Typically, they are feedforward networks in which data flows from the input layer to the output layer without looping back, but there are different flavors of DNNs – among them, those with the most practical applications are Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs).

CNNs

The most common use case scenarios of CNNs are all to do with image processing, but are not restricted to other types of input, whether it be audio or video. A typical use case is image classification – the network is fed with images so that it can classify the data. For example, it...

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