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
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In contrast to the traditional machine learning algorithms, deep learning models have the capability to address the challenges imposed by a massive amount of input data. Deep learning networks are designed to automatically extract complex representation of data from the unstructured data. This property makes deep learning a precious tool to learn the hidden information from the big data. However, due to the velocity at which the volume and varieties of data are increasing day by day, deep learning networks need to be stored and processed in a distributed manner. Hadoop, being the most widely used big data framework for such requirements, is extremely convenient in this situation. We explained the primary components of Hadoop that are essential for distributed deep learning architecture. The crucial characteristics of distributed deep learning networks were also explained in depth. Deeplearning4j, an open source distributed deep learning framework, integrates with Hadoop to achieve...