Book Image

Hands-On Deep Learning with Apache Spark

By : Guglielmo Iozzia
Book Image

Hands-On Deep Learning with Apache Spark

By: Guglielmo Iozzia

Overview of this book

Deep learning is a subset of machine learning where datasets with several layers of complexity can be processed. Hands-On Deep Learning with Apache Spark addresses the sheer complexity of technical and analytical parts and the speed at which deep learning solutions can be implemented on Apache Spark. The book starts with the fundamentals of Apache Spark and deep learning. You will set up Spark for deep learning, learn principles of distributed modeling, and understand different types of neural nets. You will then implement deep learning models, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory (LSTM) on Spark. As you progress through the book, you will gain hands-on experience of what it takes to understand the complex datasets you are dealing with. During the course of this book, you will use popular deep learning frameworks, such as TensorFlow, Deeplearning4j, and Keras to train your distributed models. By the end of this book, you'll have gained experience with the implementation of your models on a variety of use cases.
Table of Contents (19 chapters)
Appendix A: Functional Programming in Scala
Appendix B: Image Data Preparation for Spark

The Apache Spark Ecosystem

Apache Spark ( is an open source, fast cluster-computing platform. It was originally created by AMPLab at the University of California, Berkeley. Its source code was later donated to the Apache Software Foundation ( Spark comes with a very fast computation speed because data is loaded into distributed memory (RAM) across a cluster of machines. Not only can data be quickly transformed, but also cached on demand for a variety of use cases. Compared to Hadoop MapReduce, it runs programs up to 100 times faster when the data fits in memory, or 10 times faster on disk. Spark provides support for four programming languages: Java, Scala, Python, and R. This book covers the Spark APIs (and deep learning frameworks) for Scala ( and Python ( only.

This chapter will cover the following topics:

  • Apache Spark fundamentals
  • Getting Spark
  • Resilient Distributed Dataset (RDD) programming
  • Spark SQL, Datasets, and DataFrames
  • Spark Streaming
  • Cluster mode using a different manager