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R Deep Learning Cookbook

You're reading from   R Deep Learning Cookbook Solve complex neural net problems with TensorFlow, H2O and MXNet

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Product type Paperback
Published in Aug 2017
Publisher Packt
ISBN-13 9781787121089
Length 288 pages
Edition 1st Edition
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Authors (2):
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Achyutuni Sri Krishna Rao Achyutuni Sri Krishna Rao
Author Profile Icon Achyutuni Sri Krishna Rao
Achyutuni Sri Krishna Rao
PKS Prakash PKS Prakash
Author Profile Icon PKS Prakash
PKS Prakash
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Toc

Table of Contents (11) Chapters Close

Preface 1. Getting Started FREE CHAPTER 2. Deep Learning with R 3. Convolution Neural Network 4. Data Representation Using Autoencoders 5. Generative Models in Deep Learning 6. Recurrent Neural Networks 7. Reinforcement Learning 8. Application of Deep Learning in Text Mining 9. Application of Deep Learning to Signal processing 10. Transfer Learning

Introduction

Neural networks aim to find a non-linear relationship between input X with output y, as y=f(x). An autoencoder is a form of unsupervised neural network which tries to find a relationship between features in space such that h=f(x), which helps us learn the relationship between input space and can be used for data compression, dimensionality reduction, and feature learning.

 

An autoencoder consists of an encoder and decoder. The encoder helps encode the input x in a latent representation y, whereas a decoder converts back the y to x. Both the encoder and decoder possess a similar representation of form.

Here is a representation of a one layer autoencoder:

The coder encodes input X to h under a hidden layer contain, whereas the decoder helps to attain the original data from encoded output h. The matrices We and Wd represent the weights of the encoder and decoder...

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