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

Hands-on CNN with Spark

In the previous sections of this chapter, we went through the theory of CNNs and the GoogleNet architecture. If this is the first time you're reading about these concepts, probably you are wondering about the complexity of the Scala code to implement CNN's models, train, and evaluate them. Adopting a high-level framework like DL4J, you are going to discover how many facilities come out-of-the-box with it and that the implementation process is easier than expected.

In this section, we are going to explore a real example of CNN configuration and training using the DL4J and Spark frameworks. The training data used comes from the MNIST database (http://yann.lecun.com/exdb/mnist/). It contains images of handwritten digits, with each image labeled by an integer. It is used to benchmark the performance of ML and DL algorithms. It contains a training...