Apache Spark Deep Learning Cookbook

Over 80 recipes that streamline deep learning in a distributed environment with Apache Spark

Amrith Ravindra , Ahmed Sherif



A solution-based guide to put your deep learning models into production with the power of Apache Spark

Key Features

  • Discover practical recipes for distributed deep learning with Apache Spark
  • Learn to use libraries such as Keras and TensorFlow
  • Solve problems in order to train your deep learning models on Apache Spark

Book Description

With deep learning gaining rapid mainstream adoption in modern-day industries, organizations are looking for ways to unite popular big data tools with highly efficient deep learning libraries. As a result, this will help deep learning models train with higher efficiency and speed.

With the help of the Apache Spark Deep Learning Cookbook, you’ll work through specific recipes to generate outcomes for deep learning algorithms, without getting bogged down in theory. From setting up Apache Spark for deep learning to implementing types of neural net, this book tackles both common and not so common problems to perform deep learning on a distributed environment. In addition to this, you’ll get access to deep learning code within Spark that can be reused to answer similar problems or tweaked to answer slightly different problems. You will also learn how to stream and cluster your data with Spark. Once you have got to grips with the basics, you’ll explore how to implement and deploy deep learning models, such as Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) in Spark, using popular libraries such as TensorFlow and Keras.

Who this book is for

If you’re looking for a practical and highly useful resource for implementing efficiently distributed deep learning models with Apache Spark, then the Apache Spark Deep Learning Cookbook is for you. Knowledge of the core machine learning concepts and a basic understanding of the Apache Spark framework is required to get the best out of this book. Additionally, some programming knowledge in Python is a plus.

Table of Contents

  1. Setting Up Spark for Deep Learning Development
  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

What will you learn from this book

By the end of the book, you'll have the expertise to train and deploy efficient deep learning models on Apache Spark.

What you will learn

  • Set up a fully functional Spark environment
  • Understand practical machine learning and deep learning concepts
  • Apply built-in machine learning libraries within Spark
  • Explore libraries that are compatible with TensorFlow and Keras
  • Explore NLP models such as Word2vec and TF-IDF on Spark
  • Organize dataframes for deep learning evaluation
  • Apply testing and training modeling to ensure accuracy
  • Access readily available code that may be reusable
Language English
ISBN-10 9781788474221
ISBN-13 9781788474221
No of pages 474
Book Publisher Packt Publishing
Published Date 12 Jul 2018

About Author

Author : Amrith Ravindra


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