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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RNNs are special compared to other traditional deep neural networks because of their capability to work over long sequences of vectors, and to output different sequences of vectors. RNNs are unfolded over time to work like a feed-forward neural network. The training of RNNs is performed with backpropagation of time, which is an extension of the traditional backpropagation algorithm. A special unit of RNNs, called Long short-term memory, helps to overcome the limitations of the backpropagation of time algorithm.

We also talked about the bidirectional RNN, which is an updated version of the unidirectional RNN. Unidirectional RNNs sometimes fail to predict correctly because of lack of future input information. Later, we discussed distribution of deep RNNs and their implementation with Deeplearning4j. Asynchronous stochastic gradient descent can be used for the training of the distributed RNN. In the next chapter, we will discuss another model of deep neural network, called the Restricted...