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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

Downloading stock market data for Apple


There are many resources for downloading stock market data for Apple. For our purposes, we will be using the Yahoo! Finance website.

Getting ready

This section will require initializing a Spark cluster that will be used for all recipes in this chapter. A Spark notebook can be initialized in the terminal using sparknotebook, as seen in the following screenshot:

A SparkSession can be initialized in a Jupyter notebook using the following script:

spark = SparkSession.builder \
    .master("local") \
    .appName("StockMarket") \
    .config("spark.executor.memory", "6gb") \
    .getOrCreate()

How to do it...

The following section walks through the steps for downloading historical stock market data for Apple.

  1. Visit the following website to track the daily historical adjusted closing stock value for Apple, which has a stock ticker value of AAPL: https://finance.yahoo.com/quote/AAPL/history
  2. Set and apply the following parameters to the Historical Data tab:
    1. Time Period...
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