Search icon CANCEL
Subscription
0
Cart icon
Cart
Close icon
You have no products in your basket yet
Save more on your purchases!
Savings automatically calculated. No voucher code required
Arrow left icon
All Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Newsletters
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
R Deep Learning Cookbook

You're reading from  R Deep Learning Cookbook

Product type Book
Published in Aug 2017
Publisher Packt
ISBN-13 9781787121089
Pages 288 pages
Edition 1st Edition
Languages
Authors (2):
PKS Prakash PKS Prakash
Profile icon PKS Prakash
Achyutuni Sri Krishna Rao Achyutuni Sri Krishna Rao
Profile icon Achyutuni Sri Krishna Rao
View More author details
Toc

Table of Contents (17) Chapters close

Title Page
Credits
About the Authors
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface
1. Getting Started 2. Deep Learning with R 3. Convolution Neural Network 4. Data Representation Using Autoencoders 5. Generative Models in Deep Learning 6. Recurrent Neural Networks 7. Reinforcement Learning 8. Application of Deep Learning in Text Mining 9. Application of Deep Learning to Signal processing 10. Transfer Learning

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...

lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at €14.99/month. Cancel anytime