Apache Spark Deep Learning Cookbook: Over 80 recipes that streamline deep learning in a distributed environment with Apache Spark

Ravindra Amrith Sherif Ahmed

ISBN 10: 1788474228 / ISBN 13: 9781788474221

Published by Packt Publishing, Limited, 2018

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Bibliographic Details

• Title: Apache Spark Deep Learning Cookbook: Over 80 recipes that streamline deep learning in a distributed environment with Apache Spark
• Publisher: Packt Publishing, Limited
• Publication Date: 2018
• Language: English
• ISBN 10: 1788474228
• ISBN 13: 9781788474221
• Binding: Soft cover
• Condition: New

About this title

Synopsis

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.

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

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

About the Authors

Ahmed Sherif is a data scientist who has worked with data in various roles since 2005. He started off with BI solutions and transitioned to data science in 2013. In 2016, he obtained a master's in Predictive Analytics from Northwestern University, where he studied the science and application of machine learning and predictive modeling using both Python and R. Lately, he has been developing machine learning and deep learning solutions on the cloud using Azure. In 2016, he published his first book, Practical Business Intelligence. He currently works as a Technology Solution Profession in Data and AI for Microsoft.

Amrith Ravindra is a machine learning enthusiast who holds degrees in electrical and industrial engineering. While pursuing his masters, he dove deeper into the world of machine learning and developed a love for data science. Graduate-level courses in engineering gave him the mathematical background to launch himself into a career in machine learning. He met Ahmed Sherif at a local data science meetup in Tampa. They decided to put their brains together to write a book on their favorite machine learning algorithms. He hopes this book will help him achieve his ultimate goal of becoming a data scientist and actively contributing to machine learning.

Michal Malohlava, creator of Sparkling Water, is a geek and the developer; Java, Linux, programming languages enthusiast who has been developing software for over 10 years. He obtained his PhD from Charles University in Prague in 2012, and post doctorate from Purdue University. During his studies, he was interested in the construction of not only distributed but also embedded and real-time, component-based systems, using model-driven methods and domain-specific languages. He participated in the design and development of various systems, including SOFA and Fractal component systems and the jPapabench control system. Now, his main interest is big data computation. He participates in the development of the H2O platform for advanced big data math and computation, and its embedding into Spark engine, published as a project called Sparkling Water.

Adnan Masood, PhD is an artificial intelligence and machine learning researcher, visiting scholar at Stanford AI Lab, software engineer, Microsoft MVP (Most Valuable Professional), and Microsoft's regional director for artificial intelligence. As chief architect of AI and machine learning at UST Global, he collaborates with Stanford AI Lab and MIT CSAIL, and leads a team of data scientists and engineers building artificial intelligence solutions to produce business value and insights that affect a range of businesses, products, and initiatives.