Book Image

Apache Spark Deep Learning Cookbook

By : Ahmed Sherif, Amrith Ravindra
Book Image

Apache Spark Deep Learning Cookbook

By: Ahmed Sherif, Amrith Ravindra

Overview of this book

Organizations these days need to integrate popular big data tools such as Apache Spark with highly efficient deep learning libraries if they’re looking to gain faster and more powerful insights from their data. With this book, you’ll discover over 80 recipes to help you train fast, enterprise-grade, deep learning models on Apache Spark. Each recipe addresses a specific problem, and offers a proven, best-practice solution to difficulties encountered while implementing various deep learning algorithms in a distributed environment. The book follows a systematic approach, featuring a balance of theory and tips with best practice solutions to assist you with training different types of neural networks such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs). You’ll also have access to code written in TensorFlow and Keras that you can run on Spark to solve a variety of deep learning problems in computer vision and natural language processing (NLP), or tweak to tackle other problems encountered in deep learning. By the end of this book, you'll have the skills you need to train and deploy state-of-the-art deep learning models on Apache Spark.
Table of Contents (21 chapters)
Title Page
Copyright and Credits
Packt Upsell
Foreword
Contributors
Preface
Index

Introduction


Deep learning is the focused study of machine learning algorithms that deploy neural networks as their main method of learning. Deep learning has exploded onto the scene just within the last couple of years. Microsoft, Google, Facebook, Amazon, Apple, Tesla and many other companies are all utilizing deep learning models in their apps, websites, and products. At the same exact time, Spark, an in-memory compute engine running on top of big data sources, has made it easy to process volumes of information at record speeds and ease. In fact, Spark has now become the leading big data development tool for data engineers, machine learning engineers, and data scientists.

Since deep learning models perform better with more data, the synergy between Spark and deep learning allowed for a perfect marriage. Almost as important as the code used to execute deep learning algorithms is the work environment that enables optimal development. Many talented minds are eager to develop neural networks to help answer important questions in their research. Unfortunately, one of the greatest barriers to the development of deep learning models is access to the necessary technical resources required to learn on big data. The purpose of this chapter is to create an ideal virtual development environment for deep learning on Spark.