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

Illustrating the use of a pretrained model


The current recipe will cover the set-up for using a pretrained model. We will use TensorFlow to demonstrate the recipe. The current recipe will use VGG16 architecture built using the ImageNet as dataset. The ImageNet is an open source image repository of images used for building image recognition algorithms. The database has more than 10 millions tagged images and more than 1 million images have bounding box to capture objects.

Lot of different deep learning architectures are developed using ImageNet dataset. Once of the popular one is VGG networks are convolution neural networks proposed by Zisserman and Simonyan (2014) and trained over ImageNet data with 1,000 classes. The current recipe will consider VGG16 variant of VGG architecture which is known for it's simplicity. The network uses input of 224 x 224 RGB image. The network utilizes 13 convolution layers with different width x height x depth. The maximum pooling layer is used to reduce volume...