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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

Importing the necessary libraries


Before we begin, we require the following libraries and dependencies, which need to be imported into our Python environment. These libraries will make our tasks a lot easier, as they have readily available functions and models that can be used instead of doing that ourselves. This also makes the code more compact and readable.

Getting ready

The following libraries and dependencies will be required to create word vectors and plots and visualize the n-dimensional word vectors in a 2D space:

  • future
  • codecs
  • glob
  • multiprocessing
  • os
  • pprint
  • re
  • nltk
  • Word2Vec
  • sklearn
  • numpy
  • matplotlib
  • pandas
  • seaborn

How to do it...

The steps are as follows:

  1. Type the following commands into your Jupyter notebook to import all the required libraries:
from __future__ import absolute_import, division, print_function
import codecs
import glob
import logging
import multiprocessing
import os
import pprint
import re
import nltk
import gensim.models.word2vec as w2v
import sklearn.manifold
import numpy
as np
import...
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