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

Implementing a feed-forward backpropagation Neural Network


In this recipe, we will implement a willow neural network with backpropagation. The input of the neural network is the outcome of the third (or last) RBM. In other words, the reconstructed raw data (trainX) is actually used to train the neural network as a supervised classifier of (10) digits. The backpropagation technique is used to further fine-tune the performance of classification.

Getting ready

This section provides the requirements for TensorFlow.

  • The dataset is loaded and set up
  • The TensorFlow package is set up and loaded

How to do it...

This section covers the steps for setting up a feed-forward backpropagation Neural Network:

  1. Let's define the input parameters of the neural network as function parameters. The following table describes each parameter:

The neural network function will have a structure as shown in the following script:

NN_train <- function(Xdata,Ydata,Xtestdata,Ytestdata,input_size,
learning_rate=0.1,momentum = 0...