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

Large-scale image processing using Hadoop

We have already mentioned in the earlier chapters how the size and volume of images are increasing day by day; the need to store and process these vast amount of images is difficult for centralized computers. Let's consider an example to get a practical idea of such situations. Let's take a large-scale image of size 81025 pixels by 86273 pixels. Each pixel is composed of three values:red, green, and blue. Consider that, to store each of these values, a 32-bit precision floating point number is required. Therefore, the total memory consumption of that image can be calculated as follows:

86273 * 81025 * 3 * 32 bits = 78.12 GB

Leave aside doing any post processing on this image, as it can be clearly concluded that it is impossible for a traditional computer to even store this amount of data in its main memory. Even though some advanced computers come with higher configurations, given the return on investment, most companies do not opt for these computers...