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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Understanding convolution

To understand the concept of convolution, let us take an example to determine the position of a lost mobile phone with the help of a laser sensor. Let's say the current location of the mobile phone at time t can be given by the laser as f (t). The laser gives different readings of the location for all the values of t. The laser sensors are generally noisy in nature, which is undesirable for this scenario. Therefore, to derive a less noisy measurement of the location of the phone, we need to calculate the average various measurements. Ideally, the more the measurements, the greater the accuracy of the location. Hence, we should undergo a weighted average, which provides more weight to the measurements.

A weighted function can be given by the function w (b), where b denotes the age of the measurement. To derive a new function that will provide a better estimate of the location of the mobile phone, we need to take the average of the weight at every moment.

The new function...