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
About the Authors
About the Reviewer
Customer Feedback

Performing a full run of training an RBM

Using the same RBM setup mentioned in the preceding recipe, train the RBM on the user ratings dataset (trX) using 20 hidden nodes. To keep a track of the optimization, the MSE is calculated after every batch of 1,000 rows. The following image shows the declining trend of mean squared reconstruction errors computed for 500 batches (equal to epochs):

Looking into RBM recommendations: Let's now look into the recommendations generated by RBM-based collaborative filtering for a given user ID. Here, we will look into the top-rated genres and top-recommended genres of this user ID, along with the top 10 movie recommendations.

The following image illustrates a list of top-rated genres:

The following image illustrates a list of top-recommended genres:

Getting ready

This section provides the requirements for collaborative filtering the output evaluation:

  • TensorFlow in R is installed and set up
  • The movies.dat and ratings.dat datasets are loaded in environment
  • The recipe...