Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Free Learning
Arrow right icon
Deep Learning with R Cookbook
Deep Learning with R Cookbook

Deep Learning with R Cookbook: Over 45 unique recipes to delve into neural network techniques using R 3.5.x

Arrow left icon
Profile Icon Gupta Profile Icon Ansari Profile Icon Sarkar
Arrow right icon
€18.99 per month
Full star icon Full star icon Full star icon Full star icon Full star icon 5 (3 Ratings)
Paperback Feb 2020 328 pages 1st Edition
eBook
€20.98 €29.99
Paperback
€36.99
Subscription
Free Trial
Renews at €18.99p/m
Arrow left icon
Profile Icon Gupta Profile Icon Ansari Profile Icon Sarkar
Arrow right icon
€18.99 per month
Full star icon Full star icon Full star icon Full star icon Full star icon 5 (3 Ratings)
Paperback Feb 2020 328 pages 1st Edition
eBook
€20.98 €29.99
Paperback
€36.99
Subscription
Free Trial
Renews at €18.99p/m
eBook
€20.98 €29.99
Paperback
€36.99
Subscription
Free Trial
Renews at €18.99p/m

What do you get with a Packt Subscription?

Free for first 7 days. $19.99 p/m after that. Cancel any time!
Product feature icon Unlimited ad-free access to the largest independent learning library in tech. Access this title and thousands more!
Product feature icon 50+ new titles added per month, including many first-to-market concepts and exclusive early access to books as they are being written.
Product feature icon Innovative learning tools, including AI book assistants, code context explainers, and text-to-speech.
Product feature icon Thousands of reference materials covering every tech concept you need to stay up to date.
Subscribe now
View plans & pricing
Table of content icon View table of contents Preview book icon Preview Book

Deep Learning with R Cookbook

Working with Convolutional Neural Networks

Convolutional Neural Networks (CNNs) are the most popular and widely used deep neural networks for computer vision problems. They are used in a variety of applications including image classification, face recognition, document analysis, medical image analysis, action recognition, and natural language processing. In this chapter, we will focus on learning convolutional operations, and concepts such as padding and strides, to optimize CNNs. The idea behind this chapter is to make you well versed with the functioning of the CNN and learn techniques such as data augmentation and batch normalization to fine-tune your network and prevent overfitting. We will also provide a brief discussion about how we can leverage transfer learning to boost model performance. 

In this chapter, we will cover the following recipes:

    ...

Introduction to convolutional operations

The generic architecture of CNN is comprised of convolutional layers followed by fully connected layers. Like other neural networks, a CNN also contains input, hidden and output layers, but it works by restructuring the data into tensors that consist of the image, and the width and height of the image. In CNN, each volume in one layer is connected only to a spatially relevant region in the next layer to ensure that when the number of layers increases, each neuron has a local influence on its specific location. A CNN may also contain pooling layers along with few fully connected layers.

The following is an example of a simple CNN with convolution and pooling layers. In this recipe, we will work with convolution layers. We will introduce the concept of pooling layers in the Getting familiar with pooling layers recipe of...

Understanding strides and padding

In this recipe, we will learn about two key configuration hyperparameters of CNN, which are strides and padding. Strides are used mainly to reduce the size of the output volume. Padding is another technique that lets us preserve the dimensions of the input volume in the output volume, thus enabling us to extract the low-level features efficiently.

Strides: Stride, in very simple terms, means the step of the convolution operation. Stride specifies the amount by which filters convolve around the input. For example, if we specify the value of stride argument as 1, that means the filter will shift one unit at a time over the input matrix. 

Strides can be used for multiple purposes, primarily the following:

  • To avoid feature overlapping
  • To achieve smaller spatial dimensionality of the output volume

In the following diagram, you...

Getting familiar with pooling layers

CNNs use pooling layers to reduce the size of the representation, to speed up the computation of the network, and to ensure robust feature extraction. The pooling layer is mostly stacked on top of the convolutional layer and this layer heavily downsizes the input dimension to reduce the computation in the network and also reduce overfitting.

There are two most commonly used types of pooling techniques :

  • Max pooling: This type of pooling does downsampling by dividing the input matrix into pooling regions followed by computing the max values of each region.

Here's an example:

  • Average poolingThis type of pooling does downsampling by dividing the input matrix into pooling regions followed by computing the average values of each region. 

Here's an example:

In this recipe, we will learn how...

Implementing transfer learning

Transfer learning helps us solve a new problem using fewer examples by using information gained from solving other related tasks. It is a technique where we reuse a learned model trained on a different dataset to solve a similar but different problem. In transfer learning, we extend the learning of a pre-trained model in our network and build a new model to solve a new learning problem. The keras library in R provides many pre-trained models; we will be using one such model called as VGG16 to train our network.

Getting ready

We will start by importing the keras library into our environment:

library(keras)

In this example, we will work with a subset of the Dogs versus Cats dataset from...

Left arrow icon Right arrow icon
Download code icon Download Code

Key benefits

  • Understand the intricacies of R deep learning packages to perform a range of deep learning tasks
  • Implement deep learning techniques and algorithms for real-world use cases
  • Explore various state-of-the-art techniques for fine-tuning neural network models

Description

Deep learning (DL) has evolved in recent years with developments such as generative adversarial networks (GANs), variational autoencoders (VAEs), and deep reinforcement learning. This book will get you up and running with R 3.5.x to help you implement DL techniques. The book starts with the various DL techniques that you can implement in your apps. A unique set of recipes will help you solve binomial and multinomial classification problems, and perform regression and hyperparameter optimization. To help you gain hands-on experience of concepts, the book features recipes for implementing convolutional neural networks (CNNs), recurrent neural networks (RNNs), and Long short-term memory (LSTMs) networks, as well as sequence-to-sequence models and reinforcement learning. You’ll then learn about high-performance computation using GPUs, along with learning about parallel computation capabilities in R. Later, you’ll explore libraries, such as MXNet, that are designed for GPU computing and state-of-the-art DL. Finally, you’ll discover how to solve different problems in NLP, object detection, and action identification, before understanding how to use pre-trained models in DL apps. By the end of this book, you’ll have comprehensive knowledge of DL and DL packages, and be able to develop effective solutions for different DL problems.

Who is this book for?

This deep learning book is for data scientists, machine learning practitioners, deep learning researchers and AI enthusiasts who want to learn key tasks in deep learning domains using a recipe-based approach. A strong understanding of machine learning and working knowledge of the R programming language is mandatory.

What you will learn

  • Work with different datasets for image classification using CNNs
  • Apply transfer learning to solve complex computer vision problems
  • Use RNNs and their variants such as LSTMs and Gated Recurrent Units (GRUs) for sequence data generation and classification
  • Implement autoencoders for DL tasks such as dimensionality reduction, denoising, and image colorization
  • Build deep generative models to create photorealistic images using GANs and VAEs
  • Use MXNet to accelerate the training of DL models through distributed computing

Product Details

Country selected
Publication date, Length, Edition, Language, ISBN-13
Publication date : Feb 21, 2020
Length: 328 pages
Edition : 1st
Language : English
ISBN-13 : 9781789805673
Category :
Languages :
Concepts :
Tools :

What do you get with a Packt Subscription?

Free for first 7 days. $19.99 p/m after that. Cancel any time!
Product feature icon Unlimited ad-free access to the largest independent learning library in tech. Access this title and thousands more!
Product feature icon 50+ new titles added per month, including many first-to-market concepts and exclusive early access to books as they are being written.
Product feature icon Innovative learning tools, including AI book assistants, code context explainers, and text-to-speech.
Product feature icon Thousands of reference materials covering every tech concept you need to stay up to date.
Subscribe now
View plans & pricing

Product Details

Publication date : Feb 21, 2020
Length: 328 pages
Edition : 1st
Language : English
ISBN-13 : 9781789805673
Category :
Languages :
Concepts :
Tools :

Packt Subscriptions

See our plans and pricing
Modal Close icon
€18.99 billed monthly
Feature tick icon Unlimited access to Packt's library of 7,000+ practical books and videos
Feature tick icon Constantly refreshed with 50+ new titles a month
Feature tick icon Exclusive Early access to books as they're written
Feature tick icon Solve problems while you work with advanced search and reference features
Feature tick icon Offline reading on the mobile app
Feature tick icon Simple pricing, no contract
€189.99 billed annually
Feature tick icon Unlimited access to Packt's library of 7,000+ practical books and videos
Feature tick icon Constantly refreshed with 50+ new titles a month
Feature tick icon Exclusive Early access to books as they're written
Feature tick icon Solve problems while you work with advanced search and reference features
Feature tick icon Offline reading on the mobile app
Feature tick icon Choose a DRM-free eBook or Video every month to keep
Feature tick icon PLUS own as many other DRM-free eBooks or Videos as you like for just €5 each
Feature tick icon Exclusive print discounts
€264.99 billed in 18 months
Feature tick icon Unlimited access to Packt's library of 7,000+ practical books and videos
Feature tick icon Constantly refreshed with 50+ new titles a month
Feature tick icon Exclusive Early access to books as they're written
Feature tick icon Solve problems while you work with advanced search and reference features
Feature tick icon Offline reading on the mobile app
Feature tick icon Choose a DRM-free eBook or Video every month to keep
Feature tick icon PLUS own as many other DRM-free eBooks or Videos as you like for just €5 each
Feature tick icon Exclusive print discounts

Frequently bought together


Stars icon
Total 102.97
Hands-On Mathematics for Deep Learning
€32.99
Hands-On Deep Learning with R
€32.99
Deep Learning with R Cookbook
€36.99
Total 102.97 Stars icon
Banner background image

Table of Contents

10 Chapters
Understanding Neural Networks and Deep Neural Networks Chevron down icon Chevron up icon
Working with Convolutional Neural Networks Chevron down icon Chevron up icon
Recurrent Neural Networks in Action Chevron down icon Chevron up icon
Implementing Autoencoders with Keras Chevron down icon Chevron up icon
Deep Generative Models Chevron down icon Chevron up icon
Handling Big Data Using Large-Scale Deep Learning Chevron down icon Chevron up icon
Working with Text and Audio for NLP Chevron down icon Chevron up icon
Deep Learning for Computer Vision Chevron down icon Chevron up icon
Implementing Reinforcement Learning Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

Customer reviews

Rating distribution
Full star icon Full star icon Full star icon Full star icon Full star icon 5
(3 Ratings)
5 star 100%
4 star 0%
3 star 0%
2 star 0%
1 star 0%
Karan Bhanot Nov 04, 2020
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Disclaimer: One of the coordinators from the Publisher asked me to review this book and sent me a review copy. I promise to be completely honest about my thoughts on this book.Overview:Even though I’ve been working with R for more than a year, this book was my first introduction to deep learning in R (I do have experience in building deep learning models in other languages). This is a great book for anyone who knows about the basics of Deep learning and is looking to use R for developing models and deep learning projects. The book has a wide range of deep learning topics including convolutional neural networks, auto-encoders, NLP etc. The best part is that it explains and then provides the code to do the specific task being talked about which makes following along very easy.Things I like:There are a lot of things that I like about this book. I love how they describe about the various ways to work with deep learning. They discuss about the Tensorflow API, followed by how Microsoft Azure/Google Cloud can be used to do deep learning on the cloud detailing each step to get started and much more.The book is great at elaborating deep learning concepts through practical examples. For instance, the book details about using LSTMs for text generation, uses images to show how computer vision works and also explains how the model is generated. The layers of the model being defined are always highlighted and a section explains each layer used in the model.The authors aptly elaborate on the required theoretical background and explanation of various models. Keep in mind that some basics are required to be known before you can go ahead and understand these models properly.Things I didn’t like:Some of the code blocks are really long and I had trouble following along. Sometimes the code snippets extend beyond a page which makes it a little more troublesome. I quickly found a way around that. They provide a link to their GitHub repository which has all the codes which can be used to follow along in a much easier form.Things I’d like to see:The book can have a couple of chapters with the basics of machine, deep learning and R at the very start, so this book can be extended to complete beginners as well.
Amazon Verified review Amazon
Khushboo Oct 09, 2020
Full star icon Full star icon Full star icon Full star icon Full star icon 5
To be honest, I have always been a python user. But, being a statistician, I thought it is important to be able to code in R too. After reading and applying concepts of this book, I am sure that it will definitely give you a head start when it comes to design and deploy neural networks algorithm. The flow of a chapter is well designed, starting from understanding the basic concept then move on to how can we implement it. There is a section of "see more" where you can find more useful resources for a particular topic and "Tips" are also very useful to keep in mind. This book covers almost everything you can do with neural nets in R and made my learning task easier as I did not have to make a google search every time I code. Now, I think R is simple to understand compared to Python. :D
Amazon Verified review Amazon
Mehrnaz Mar 03, 2021
Full star icon Full star icon Full star icon Full star icon Full star icon 5
I like how the book covers different hot topics such as CNN, RNN, reinforcement learning, CV, NLP, ... The other good feature of this book is explaining installation details which is not covered in a lot of books. This is important to me because a lot of books assume that you magically have everything that you need on your computer or you have a cs background, while a lot of people struggle with them. Lastly, going step by step with examples helps to have a better understanding of the concepts.
Amazon Verified review Amazon
Get free access to Packt library with over 7500+ books and video courses for 7 days!
Start Free Trial

FAQs

What is included in a Packt subscription? Chevron down icon Chevron up icon

A subscription provides you with full access to view all Packt and licnesed content online, this includes exclusive access to Early Access titles. Depending on the tier chosen you can also earn credits and discounts to use for owning content

How can I cancel my subscription? Chevron down icon Chevron up icon

To cancel your subscription with us simply go to the account page - found in the top right of the page or at https://subscription.packtpub.com/my-account/subscription - From here you will see the ‘cancel subscription’ button in the grey box with your subscription information in.

What are credits? Chevron down icon Chevron up icon

Credits can be earned from reading 40 section of any title within the payment cycle - a month starting from the day of subscription payment. You also earn a Credit every month if you subscribe to our annual or 18 month plans. Credits can be used to buy books DRM free, the same way that you would pay for a book. Your credits can be found in the subscription homepage - subscription.packtpub.com - clicking on ‘the my’ library dropdown and selecting ‘credits’.

What happens if an Early Access Course is cancelled? Chevron down icon Chevron up icon

Projects are rarely cancelled, but sometimes it's unavoidable. If an Early Access course is cancelled or excessively delayed, you can exchange your purchase for another course. For further details, please contact us here.

Where can I send feedback about an Early Access title? Chevron down icon Chevron up icon

If you have any feedback about the product you're reading, or Early Access in general, then please fill out a contact form here and we'll make sure the feedback gets to the right team. 

Can I download the code files for Early Access titles? Chevron down icon Chevron up icon

We try to ensure that all books in Early Access have code available to use, download, and fork on GitHub. This helps us be more agile in the development of the book, and helps keep the often changing code base of new versions and new technologies as up to date as possible. Unfortunately, however, there will be rare cases when it is not possible for us to have downloadable code samples available until publication.

When we publish the book, the code files will also be available to download from the Packt website.

How accurate is the publication date? Chevron down icon Chevron up icon

The publication date is as accurate as we can be at any point in the project. Unfortunately, delays can happen. Often those delays are out of our control, such as changes to the technology code base or delays in the tech release. We do our best to give you an accurate estimate of the publication date at any given time, and as more chapters are delivered, the more accurate the delivery date will become.

How will I know when new chapters are ready? Chevron down icon Chevron up icon

We'll let you know every time there has been an update to a course that you've bought in Early Access. You'll get an email to let you know there has been a new chapter, or a change to a previous chapter. The new chapters are automatically added to your account, so you can also check back there any time you're ready and download or read them online.

I am a Packt subscriber, do I get Early Access? Chevron down icon Chevron up icon

Yes, all Early Access content is fully available through your subscription. You will need to have a paid for or active trial subscription in order to access all titles.

How is Early Access delivered? Chevron down icon Chevron up icon

Early Access is currently only available as a PDF or through our online reader. As we make changes or add new chapters, the files in your Packt account will be updated so you can download them again or view them online immediately.

How do I buy Early Access content? Chevron down icon Chevron up icon

Early Access is a way of us getting our content to you quicker, but the method of buying the Early Access course is still the same. Just find the course you want to buy, go through the check-out steps, and you’ll get a confirmation email from us with information and a link to the relevant Early Access courses.

What is Early Access? Chevron down icon Chevron up icon

Keeping up to date with the latest technology is difficult; new versions, new frameworks, new techniques. This feature gives you a head-start to our content, as it's being created. With Early Access you'll receive each chapter as it's written, and get regular updates throughout the product's development, as well as the final course as soon as it's ready.We created Early Access as a means of giving you the information you need, as soon as it's available. As we go through the process of developing a course, 99% of it can be ready but we can't publish until that last 1% falls in to place. Early Access helps to unlock the potential of our content early, to help you start your learning when you need it most. You not only get access to every chapter as it's delivered, edited, and updated, but you'll also get the finalized, DRM-free product to download in any format you want when it's published. As a member of Packt, you'll also be eligible for our exclusive offers, including a free course every day, and discounts on new and popular titles.