Distributed sparse representation is one of the primary keys to learn useful features in deep learning algorithms. Not only is it a coherent mode of data representation, but it also helps to capture the generation process of most of the real world dataset. In this section, we will explain how autoencoders encourage sparsity of data. We will start with introducing sparse coding. A code is termed as sparse when an input provokes the activation of a relatively small number of nodes of a neural network, which combine to represent it in a sparse way. In deep learning technology, a similar constraint is used to generate the sparse code models to implement regular autoencoders, which are trained with sparsity constants called sparse autoencoders.
Deep Learning with Hadoop
By :
Deep Learning with Hadoop
By:
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