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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Natural language processing using Hadoop

The exponential growth of information in the Web has increased the intensity of diffusion of large-scale unstructured natural language textual resources. Hence, in the last few years, the interest to extract, process, and share this information has increased substantially. Processing these sources of knowledge within a stipulated time frame has turned out to be a major challenge for various research and commercial industries. In this section, we will describe the process used to crawl the web documents, discover the information and run natural language processing in a distributed manner using Hadoop.

To design architecture for natural language processing (NLP), the first task to be performed is the extraction of annotated keywords and key phrases from the large-scale unstructured data. To perform the NLP on a distributed architecture, the Apache Hadoop framework can be chosen for its efficient and scalable solution, and also to improve the failure...