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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 FREE CHAPTER 2. Creating a Neural Network in Spark 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

Identifying the target variable of the logistic regression model

A logistic regression model operates as a classification algorithm aiming to predict a binary outcome. In this section, we will specify the best column within the dataset to predict whether an incoming call to the operator is related to fire or non-fire incidents.

Getting ready

We will visualize many of the data points in this section, which will require the following:

  1. Ensuring that matplotlib is installed by executing pip install matplotlib at the command line.
  2. Running import matplotlib.pyplot as plt as well as ensuring graphs are viewed within cells by running %matplotlib inline.

Additionally, there will be some manipulation of functions within pyspark...

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