Book Image

Advanced Deep Learning with R

By : Bharatendra Rai

Book Image

Advanced Deep Learning with R

By: Bharatendra Rai

Overview of this book

Deep learning is a branch of machine learning based on a set of algorithms that attempt to model high-level abstractions in data. Advanced Deep Learning with R will help you understand popular deep learning architectures and their variants in R, along with providing real-life examples for them. This deep learning book starts by covering the essential deep learning techniques and concepts for prediction and classification. You will learn about neural networks, deep learning architectures, and the fundamentals for implementing deep learning with R. The book will also take you through using important deep learning libraries such as Keras-R and TensorFlow-R to implement deep learning algorithms within applications. You will get up to speed with artificial neural networks, recurrent neural networks, convolutional neural networks, long short-term memory networks, and more using advanced examples. Later, you'll discover how to apply generative adversarial networks (GANs) to generate new images; autoencoder neural networks for image dimension reduction, image de-noising and image correction and transfer learning to prepare, define, train, and model a deep neural network. By the end of this book, you will be ready to implement your knowledge and newly acquired skills for applying deep learning algorithms in R through real-world examples.

Preface

Who this book is for

What this book covers

To get the most out of this book

Free Chapter

Section 1: Revisiting Deep Learning Basics

Section 1: Revisiting Deep Learning Basics

Revisiting Deep Learning Architecture and Techniques

Revisiting Deep Learning Architecture and Techniques

Deep learning with R

Process of developing a deep network model

Deep learning techniques with R and RStudio

Section 2: Deep Learning for Prediction and Classification

Section 2: Deep Learning for Prediction and Classification

Deep Neural Networks for Multi-Class Classification

Deep Neural Networks for Multi-Class Classification

Cardiotocogram dataset

Preparing the data for model building

Creating and fitting a deep neural network model

Model evaluation and predictions

Performance optimization tips and best practices

Deep Neural Networks for Regression

Deep Neural Networks for Regression

Understanding the Boston Housing dataset

Preparing the data

Creating and fitting a deep neural network model for regression

Model evaluation and prediction

Performance optimization tips and best practices

Section 3: Deep Learning for Computer Vision

Section 3: Deep Learning for Computer Vision

Image Classification and Recognition

Image Classification and Recognition

Handling image data

Data preparation

Creating and fitting the model

Model evaluation and prediction

Performance optimization tips and best practices

Image Classification Using Convolutional Neural Networks

Image Classification Using Convolutional Neural Networks

Data preparation

Layers in the convolutional neural networks

Fitting the model

Model evaluation and prediction

Performance optimization tips and best practices

Applying Autoencoder Neural Networks Using Keras

Applying Autoencoder Neural Networks Using Keras

Types of autoencoders

Dimension reduction autoencoders

Denoising autoencoders

Image correction

Image Classification for Small Data Using Transfer Learning

Image Classification for Small Data Using Transfer Learning

Using a pretrained model to identify an image

Working with the CIFAR10 dataset

Image classification with CNN

Classifying images using the pretrained RESNET50 model

Model evaluation and prediction

Performance optimization tips and best practices

Creating New Images Using Generative Adversarial Networks

Creating New Images Using Generative Adversarial Networks

Generative adversarial network overview

Processing MNIST image data

Developing the generator network

Developing the discriminator network

Training the network

Reviewing results

Performance optimization tips and best practices

Section 4: Deep Learning for Natural Language Processing

Section 4: Deep Learning for Natural Language Processing

Deep Networks for Text Classification

Deep Networks for Text Classification

Preparing the data for model building

Developing deep neural networks

Model evaluation and prediction

Performance optimization tips and best practices

Text Classification Using Recurrent Neural Networks

Text Classification Using Recurrent Neural Networks

Preparing data for model building

Developing a recurrent neural network model

Fitting the model

Model evaluation and prediction

Performance optimization tips and best practices

Text classification Using Long Short-Term Memory Network

Text classification Using Long Short-Term Memory Network

Why do we use LSTM networks?

Preparing text data for model building

Creating a long short-term memory network model

Fitting the LSTM model

Evaluating model performance

Performance optimization tips and best practices

Text Classification Using Convolutional Recurrent Neural Networks

Text Classification Using Convolutional Recurrent Neural Networks

Working with the reuter_50_50 dataset

Preparing the data for model building

Developing the model architecture

Compiling and fitting the model

Evaluating the model and predicting classes

Performance optimization tips and best practices

Section 5: The Road Ahead

Section 5: The Road Ahead

Tips, Tricks, and the Road Ahead

Tips, Tricks, and the Road Ahead

TensorBoard for training performance visualization

Visualizing deep network models with LIME

Visualizing model training with tfruns

Early stopping of network training

Other Books You May Enjoy

Other Books You May Enjoy

Leave a review - let other readers know what you think

Customer Reviews

5 star

0

4 star

0

3 star

0

2 star

0

1 star

0

To get the most out of this book

The following are a few ideas for how you can get the most out of this book:

All examples in this book use R codes. So before getting started with it, you should havea good foundation in the R language. As per Confucius, "I hear and I forget. I see and I remember. I do and I understand." This is true for this book, too. A hands-on approach of working with the codes while going through the chapters will be very useful in understanding the deep learning models.

All the codes in this book were successfully run on a Mac computer that had 8 GB of RAM. However, if you are working with a much larger dataset compared to what has been used in this book for illustration purposes, more powerful computing resources may be required in order to develop deep learning models. It will also be helpful to have a good foundation in statistical methods.

Download the example code files

You can download the example code files for this book from your account at www.packt.com. If you purchased this book elsewhere, you can visit www.packtpub.com/support and register to have the files emailed directly to you.

You can download the code files by following these steps:

Log in or register at www.packt.com.
Select the Support tab.
Click on Code Downloads.
Enter the name of the book in the Search box and follow the onscreen instructions.

Once the file is downloaded, please make sure that you unzip or extract the folder using the latest version of:

WinRAR/7-Zip for Windows
Zipeg/iZip/UnRarX for Mac
7-Zip/PeaZip for Linux

The code bundle for the book is also hosted on GitHub at https://github.com/PacktPublishing/Advanced-Deep-Learning-with-R. In case there's an update to the code, it will be updated on the existing GitHub repository.

We also have other code bundles from our rich catalog of books and videos available at https://github.com/PacktPublishing/. Check them out!

Download the color images

We also provide a PDF file that has color images of the screenshots/diagrams used in this book. You can download it here: https://static.packt-cdn.com/downloads/9781789538779_ColorImages.pdf.

Conventions used

There are a number of text conventions used throughout this book.

CodeInText: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. Here is an example: "We store the accuracy and loss values while fitting the model in model_three."

A block of code is set as follows:

model %>% 
   compile(loss = 'binary_crossentropy', 
   optimizer = 'adam',
   metrics = 'accuracy')

Bold: Indicates a new term, an important word, or words that you see onscreen. For example, words in menus or dialog boxes appear in the text like this. Here is an example: "Recurrent neural networks (RNNs) are well suited to working with data involving such sequences."

Warnings or important notes appear like this.

Tips and tricks appear like this.