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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

Deep learning is the focused study of machine learning algorithms that deploy neural networks as their main method of learning. Deep learning has exploded onto the scene just within the last couple of years. Microsoft, Google, Facebook, Amazon, Apple, Tesla and many other companies are all utilizing deep learning models in their apps, websites, and products. At the same exact time, Spark, an in-memory compute engine running on top of big data sources, has made it easy to process volumes of information at record speeds and ease. In fact, Spark has now become the leading big data development tool for data engineers, machine learning engineers, and data scientists.

Since deep learning models perform better with more data, the synergy between Spark and deep learning allowed for a perfect marriage. Almost as important as the code used to execute deep learning algorithms is the work environment that enables optimal development. Many talented minds are eager to develop neural networks to help answer important questions in their research. Unfortunately, one of the greatest barriers to the development of deep learning models is access to the necessary technical resources required to learn on big data. The purpose of this chapter is to create an ideal virtual development environment for deep learning on Spark.

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