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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

Starting with logistic regression

Before we delve into neural networks and deep learning models, let's take a look at logistic regression, which can be viewed as a single layer neural network. Even the sigmoid function commonly used in logistic regression is used as an activation function in neural networks.

Getting ready

Logistic regression is a supervised machine learning approach for the classification of dichotomous/ordinal (order discrete) categories.

How to do it...

Logistic regression serves as a building block for complex neural network models using sigmoid...

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