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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

Product type Paperback

Published in Jul 2018

Publisher Packt

ISBN-13 9781788474221

Length 474 pages

Edition 1st Edition

Languages

Scala

Tools

Apache Spark

Concepts

Deep Learning

Authors (2):

Ahmed Sherif

Amrith Ravindra

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Table of Contents (15) Chapters

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

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Downloading the King County House sales dataset

We can't build a model without a dataset. We will download our data in this section.

Getting ready

Kaggle (https://www.kaggle.com/) is a platform for predictive modeling and analytics competitions in which statisticians and data miners compete to produce the best models for predicting and describing the datasets uploaded by companies and users. The King County House Sales dataset contains records of 21,613 houses sold in King County, New York between 1900 and 2015. The dataset also contains 21 different variables such as location, zip code, number of bedrooms, area of the living space, and so on, for each house.

How to do it...

The dataset can be accessed from the following website: https://www.kaggle.com/harlfoxem/housesalesprediction. The dataset is from the public records of King County and is freely available to download and use in any analysis.
Once you arrive at the website, you can click on the Download button, as shown in the following...