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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

Implementing an end-to-end image classification web application

Using all of the things that we learned about in the previous chapters of this book, we should now be able to implement a real-world web application that allows users to upload an image and then properly classify it.

Picking up a proper Keras model

We are going to use an existing, pre-trained Python Keras CNN model. Keras applications (https://keras.io/applications/) are a set of DL models that are available as part of the framework with pre-trained weights. Among those models is VGG16, a 16-layer CNN that was implemented by the Visual Geometry Group at the University of Oxford in 2014. This model is compatible with a TensorFlow backend. It has been trained on...

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