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 with Deeplearning4j

Training a RNN is not a simple task, and it can be extremely computationally demanding sometimes. With long sequences of training data involving many time steps, the training, sometimes becomes extremely difficult. As of now, you have got a better theoretical understanding of how and why backpropagation through time is primarily used for training a RNN. In this section, we will consider a practical example of the use of a RNN and its implementation using Deeplearning4j.

We now take an example to give an idea of how to do the sentiment analysis of a movie review dataset using RNN. The main problem statement of this network is to take some raw text of a movie review as input, and classify that movie review as either positive or negative based on the contents present. Each word of the raw review text is converted to vectors using the Word2Vec model, and then fed into a RNN. The example uses a large-scale dataset of raw movie reviews taken from