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 the Transfer Learning model


The current recipe will cover Transfer Learning using the CIFAR-10 dataset. The previous recipe presented how to use a pretrained model. The current recipe will demonstrate how to use a pretrained model for different problem statements.

We will use another very good deep learning package, MXNET, to demonstrate the concept with another architecture, Inception. To simplify the computation, we will reduce the problem complexity from 10 classes to two classes: aeroplane and automobile. The recipe focuses on data preparation for Transfer Learning using Inception-BN.

Getting ready

The section prepares for the upcoming section for setting-up Transfer Learning model.

  1. Download the CIFAR-10 dataset from http://www.cs.toronto.edu/~kriz/cifar.html. Thedownload.cifar.datafunction from Chapter 3,Convolution Neural Networks, can be used to download the dataset.
  2. Install the imager package:
install.packages("imager")

How to do it...

The current part of the recipe will provide...