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
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Distributed video decoding in Hadoop

Most of the popular video compression formats, such as MPEG-2 and MPEG-4, follow a hierarchical structure in the bit-stream. In this subsection, we will assume that the compression format used has a hierarchical structure for its bit-stream. For simplicity, we have divided the decoding task into two different Map-reduce jobs:

  1. Extraction of video sequence level information: From the outset, it can be easily predicted that the header information of all the video dataset can be found in the first block of the dataset. In this phase, the aim of the map-reduce job is to collect the sequence level information from the first block of the video dataset and output the result as a text file in the HDFS. The sequence header information is needed to set the format for the decoder object.

    For the video files, a new FileInputFormat should be implemented with its own record reader. Each record reader will then provide a <key, value> pair in this format to each...