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Deep Learning with Hadoop

You're reading from   Deep Learning with Hadoop Distributed Deep Learning with Large-Scale Data

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Product type Paperback
Published in Feb 2017
Publisher Packt
ISBN-13 9781787124769
Length 206 pages
Edition 1st Edition
Languages
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Author (1):
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Dipayan Dev Dipayan Dev
Author Profile Icon Dipayan Dev
Dipayan Dev
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Toc

Table of Contents (9) Chapters Close

Preface 1. Introduction to Deep Learning FREE CHAPTER 2. Distributed Deep Learning for Large-Scale Data 3. Convolutional Neural Network 4. Recurrent Neural Network 5. Restricted Boltzmann Machines 6. Autoencoders 7. Miscellaneous Deep Learning Operations using Hadoop 1. References

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...

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