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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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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

Basic layers of CNN

A CNN is composed of a sequence of layers, where every layer of the network goes through a differentiable function to transform itself from one volume of activation to another. Four main types of layers are used to build a CNN: Convolutional layer, Rectified Linear Units layer, Pooling layer, and Fully-connected layer. All these layers are stacked together to form a full CNN.

A regular CNN could have the following architecture:

[INPUT - CONV - RELU - POOL - FC]

However, in a deep CNN, there are generally more layers interspersed between these five basic layers.

A classic deep neural network will have the following structure:

Input -> Conv->ReLU->Conv->ReLu->Pooling->ReLU->Conv->ReLu->Pooling->Fully Connected

AlexNet, as mentioned in the earlier section, can be taken as a perfect example for this kind of structure. The architecture of AlexNet is shown in Figure 3.4. After every layer, an implicit ReLU non-linearity has been added. We...

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