Book Image

R Deep Learning Cookbook

By : PKS Prakash, Achyutuni Sri Krishna Rao
Book Image

R Deep Learning Cookbook

By: PKS Prakash, Achyutuni Sri Krishna Rao

Overview of this book

Deep Learning is the next big thing. It is a part of machine learning. It's favorable results in applications with huge and complex data is remarkable. Simultaneously, R programming language is very popular amongst the data miners and statisticians. This book will help you to get through the problems that you face during the execution of different tasks and Understand hacks in deep learning, neural networks, and advanced machine learning techniques. It will also take you through complex deep learning algorithms and various deep learning packages and libraries in R. It will be starting with different packages in Deep Learning to neural networks and structures. You will also encounter the applications in text mining and processing along with a comparison between CPU and GPU performance. By the end of the book, you will have a logical understanding of Deep learning and different deep learning packages to have the most appropriate solutions for your problems.
Table of Contents (17 chapters)
Title Page
Credits
About the Authors
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface

Setting up a deep RNN model


The RNN architecture is composed of input, hidden, and output layers. A RNN network can be made deep by decomposing the hidden layer into multiple groups or by adding computational nodes within RNN architecture such as including model computation such as multilayer perceptron for micro learning. The computational nodes can be added between input-hidden, hidden-hidden, and hidden-output connection. An example of a multilayer deep RNN model is shown in the following figure:

An example of two-layer Deep Recurrent Neural Network architecture

How to do it...

The RNN models in TensorFlow can easily be extended to Deep RNN models by using MultiRNNCell. The previous rnn function can be replaced with the stacked_rnnfunction to achieve a deep RNN architecture:

  1. Define the number of layers in the deep RNN architecture:
num_layers <- 3 
  1. Define a stacked_rnn function to perform multi-hidden layers deep RNN:
stacked_rnn<-function(x, weight, bias){ 
  # Unstack input into step_size...