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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Background of a CNN

CNN, a particular form of deep learning models, is not a new concept, and they have been widely adopted by the vision community for a long time. The model worked well in recognizing the hand-written digit by LeCun et al in 1998 [90]. But unfortunately, due to the inability of CNNs to work with higher resolution images, its popularity has diminished with the course of time. The reason was mostly due to hardware and memory constraints, and also the lack of availability of large-scale training datasets. As the computational power increases with time, mostly due to the wide availability of CPUs and GPUs and with the generation of big data, various large-scale datasets, such as the MIT Places dataset (see Zhou et al., 2014), ImageNet [91] and so on. it became possible to train larger and complex models. This is initially shown by Krizhevsky et al [4] in their paper, Imagenet classification using deep convolutional neural networks. In that paper, they brought down the error...