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R Deep Learning Projects

You're reading from R Deep Learning Projects Master the techniques to design and develop neural network models in R

Product type Paperback

Published in Feb 2018

Publisher Packt

ISBN-13 9781788478403

Length 258 pages

Edition 1st Edition

Languages

R

Tools

Keras

Concepts

Deep Learning

Authors (2):

Yuxi (Hayden) Liu

Pablo Maldonado

View More author details

Table of Contents (7) Chapters

Preface

1. Handwritten Digit Recognition Using Convolutional Neural Networks

2. Traffic Sign Recognition for Intelligent Vehicles FREE CHAPTER

3. Fraud Detection with Autoencoders

4. Text Generation Using Recurrent Neural Networks

5. Sentiment Analysis with Word Embeddings

6. Other Books You May Enjoy

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RNNs from scratch in R

The purpose of this section is to show you how you can implement recurrent neural networks from bare bones in R. This is perhaps not the optimal solution for a number of reasons, but it is a great way to get started in deep learning.

There are many plug and play frameworks like H2O, MXNet, TensorFlow, or Keras, that have compatibility with R. Our goal is to focus on the understanding of the algorithm rather than a particular API, although we will include an example using Keras. This is for two reasons, at the time of writing, the compatibility with R suffers from growing pains and we encountered many errors and issues with the different packages. On the other hand, even the stable versions of such packages have ever-changing APIs. We will focus on this section in building a very simple recurrent neural network from scratch, using simple tools from...

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