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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Applications of autoencoders

Autoencoders can be successfully applied in many use cases, and hence, have gained much popularity in the world of deep learning. In this section, we will discuss the important applications and uses of autoencoders:

  • Dimensionality reduction: If you remember, in Chapter 1, Introduction to Deep Learning, we introduced the concept of the 'curse of dimensionality'. Dimensionality reduction was one of the first applications of deep learning. Autoencoders were initially studied to overcome the issues with the curse of dimensionality. We have already got a fair idea from this chapter how deep autoencoders work on higher-dimensional data to reduce the dimensionality in the final output.

  • Information Retrieval: One more important application of autoencoders is in information retrieval. Information retrieval basically means to search for some entries, which match with an entered query, in a database. Searching in high-dimensional data is generally a cumbersome task; however...