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Apache Spark Deep Learning Cookbook

You're reading from   Apache Spark Deep Learning Cookbook Over 80 best practice recipes for the distributed training and deployment of neural networks using Keras and TensorFlow

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Product type Paperback
Published in Jul 2018
Publisher Packt
ISBN-13 9781788474221
Length 474 pages
Edition 1st Edition
Languages
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Authors (2):
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Ahmed Sherif Ahmed Sherif
Author Profile Icon Ahmed Sherif
Ahmed Sherif
Amrith Ravindra Amrith Ravindra
Author Profile Icon Amrith Ravindra
Amrith Ravindra
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Toc

Table of Contents (15) Chapters Close

Preface 1. Setting Up Spark for Deep Learning Development 2. Creating a Neural Network in Spark FREE CHAPTER 3. Pain Points of Convolutional Neural Networks 4. Pain Points of Recurrent Neural Networks 5. Predicting Fire Department Calls with Spark ML 6. Using LSTMs in Generative Networks 7. Natural Language Processing with TF-IDF 8. Real Estate Value Prediction Using XGBoost 9. Predicting Apple Stock Market Cost with LSTM 10. Face Recognition Using Deep Convolutional Networks 11. Creating and Visualizing Word Vectors Using Word2Vec 12. Creating a Movie Recommendation Engine with Keras 13. Image Classification with TensorFlow on Spark 14. Other Books You May Enjoy

Introduction

Convolutional neural networks (CNNs) have been enjoying a bit of resurgence in the last couple of years. They have shown great success when it comes to image recognition. This is quite relevant these days with the advent of modern smartphones as anyone now has the ability to take large volumes of pictures of objects and post them on social media sites. Just due to this phenomenon, convolutional neural networks are in high demand these days.

There are several features that make a CNN optimally perform. They require the following features:

  • A high volume of training data
  • Visual and spatial data
  • An emphasis on filtering (pooling), activation, and convoluting as opposed to a fully connected layer that is more apparent in a traditional neural network

While CNNs have gained great popularity, there are some limitations in working with them primarily due to their computational...

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