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 Long short-term memory based sequence model


In sequence learning the objective is to capture short-term and long-term memory. The short-term memory is captured very well by standard RNN, however, they are not very effective in capturing long-term dependencies as the gradient vanishes (or explodes rarely) within an RNN chain over time.

Note

The gradient vanishes when the weights have small values that on multiplication vanish over time, whereas in contrast, scenarios where weights have large values keep increasing over time and lead to divergence in the learning process. To deal with the issue Long Short Term Memory (LSTM) is proposed.

How to do it...

The RNN models in TensorFlow can easily be extended to LSTM models by using BasicLSTMCell. The previous rnn function can be replaced with the lstm function to achieve an LSTM architecture:

# LSTM implementation 
lstm<-function(x, weight, bias){ 
  # Unstack input into step_size 
  x = tf$unstack(x, step_size, 1) 
   
  # Define a...