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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Convolutional Restricted Boltzmann machines

Very high dimensional inputs, such as images or videos, put immense stress on the memory, computation, and operational requirements of traditional machine learning models. In  Chapter 3 , Convolutional Neural Network, we have shown how replacing the matrix multiplication by discrete convolutional operations with small kernel resolves these problems. Going forward, Desjardins and Bengio [123] have shown that this approach also works fine when applied to RBMs. In this section, we will discuss the functionalities of this model.

Figure 5.7 : Figure shows the observed variables or the visible units of an RBM can be associated with mini batches of image to a compute the final result. The weight connections represents a set of filters

Further, in normal RBMs, the visible units are directly related to all the hidden variables through different parameters and weights. To describe an image in terms of spatially local features ideally needs fewer parameters...