Book Image

Deep Learning with Hadoop

By : Dipayan Dev
Book Image

Deep Learning with Hadoop

By: Dipayan Dev

Overview of this book

This book will teach you how to deploy large-scale dataset in deep neural networks with Hadoop for optimal performance. Starting with understanding what deep learning is, and what the various models associated with deep neural networks are, this book will then show you how to set up the Hadoop environment for deep learning. In this book, you will also learn how to overcome the challenges that you face while implementing distributed deep learning with large-scale unstructured datasets. The book will also show you how you can implement and parallelize the widely used deep learning models such as Deep Belief Networks, Convolutional Neural Networks, Recurrent Neural Networks, Restricted Boltzmann machines and autoencoder using the popular deep learning library Deeplearning4j. Get in-depth mathematical explanations and visual representations to help you understand the design and implementations of Recurrent Neural network and Denoising Autoencoders with Deeplearning4j. To give you a more practical perspective, the book will also teach you the implementation of large-scale video processing, image processing and natural language processing on Hadoop. By the end of this book, you will know how to deploy various deep neural networks in distributed systems using Hadoop.
Table of Contents (16 chapters)
Deep Learning with Hadoop
About the Author
About the Reviewers
Customer Feedback

Implementation using Deeplearning4j

This section of the chapter will provide a basic idea of how to write the code for RBMs and DBNs using Deeplearning4j. Readers will be able to learn the syntax for using the various hyperparameters mentioned in this chapter.

To implement RBMs and DBNs using Deeplearning4j, the whole idea is very simple. The overall implementation can be split into three core phases: loading data or preparation of the data, network configuration, and training and evaluation of the model.

In this section, we will first discuss RBMs on IrisDataSet, and then we will come to the implementation of DBNs.

Restricted Boltzmann machines

For the building and training of RBMs, first we need to define and initialize the hyperparameter needed for the model:

Nd4j.MAX_SLICES_TO_PRINT = -1;       
Nd4j.MAX_ELEMENTS_PER_SLICE = -1;       
final int numRows = 4;       
final int numColumns = 1;       
int outputNum = 10;       
int numSamples ...