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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

Comparing principal component analysis with the Restricted Boltzmann machine


In this section, you will learn about two widely recommended dimensionality reduction techniques--Principal component analysis (PCA) and the Restricted Boltzmann machine (RBM). Consider a vector v in n-dimensional space. The dimensionality reduction technique essentially transforms the vector v into a relatively smaller (or sometimes equal) vector v' with m-dimensions (m<n). The transformation can be either linear or nonlinear.

PCA performs a linear transformation on features such that orthogonally adjusted components are generated that are later ordered based on their relative importance of variance capture. These m components can be considered as new input features, and can be defined as follows:

Vector v' =

Here, w and c correspond to weights (loading) and transformed components, respectively.

Unlike PCA, RBMs (or DBNs/autoencoders) perform non-linear transformations using connections between visible and hidden...

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