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...
Deep Learning with Hadoop
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Deep Learning with Hadoop
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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
Credits
About the Author
About the Reviewers
www.PacktPub.com
Customer Feedback
Dedication
Preface
Free Chapter
Introduction to Deep Learning
Distributed Deep Learning for Large-Scale Data
Convolutional Neural Network
Recurrent Neural Network
Restricted Boltzmann Machines
Autoencoders
Miscellaneous Deep Learning Operations using Hadoop
References
Customer Reviews