Book Image

R Deep Learning Essentials - Second Edition

By : Mark Hodnett, Joshua F. Wiley
Book Image

R Deep Learning Essentials - Second Edition

By: Mark Hodnett, Joshua F. Wiley

Overview of this book

Deep learning is a powerful subset of machine learning that is very successful in domains such as computer vision and natural language processing (NLP). This second edition of R Deep Learning Essentials will open the gates for you to enter the world of neural networks by building powerful deep learning models using the R ecosystem. This book will introduce you to the basic principles of deep learning and teach you to build a neural network model from scratch. As you make your way through the book, you will explore deep learning libraries, such as Keras, MXNet, and TensorFlow, and create interesting deep learning models for a variety of tasks and problems, including structured data, computer vision, text data, anomaly detection, and recommendation systems. You’ll cover advanced topics, such as generative adversarial networks (GANs), transfer learning, and large-scale deep learning in the cloud. In the concluding chapters, you will learn about the theoretical concepts of deep learning projects, such as model optimization, overfitting, and data augmentation, together with other advanced topics. By the end of this book, you will be fully prepared and able to implement deep learning concepts in your research work or projects.

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Table of Contents (13 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Get in touch
Free Chapter
1
Getting Started with Deep Learning
Getting Started with Deep Learning
What is deep learning?
A conceptual overview of neural networks
Deep neural networks
Some common myths about deep learning
Setting up your R environment
Summary
2
Training a Prediction Model
Training a Prediction Model
Neural networks in R
The problem of overfitting data – the consequences explained
Use case – building and applying a neural network
Summary
3
Deep Learning Fundamentals
Deep Learning Fundamentals
Building neural networks from scratch in R
Using regularization to overcome overfitting
Use case – improving out-of-sample model performance using dropout
Summary
4
Training Deep Prediction Models
Training Deep Prediction Models
Getting started with deep feedforward neural networks
Activation functions
Introduction to the MXNet deep learning library
Use case – using MXNet for classification and regression
Summary
5
Image Classification Using Convolutional Neural Networks
Image Classification Using Convolutional Neural Networks
CNNs
Convolutional layers
Image classification using the MXNet library
References/further reading
Summary
6
Tuning and Optimizing Models
Tuning and Optimizing Models
Evaluation metrics and evaluating performance
Data preparation
Data augmentation
Tuning hyperparameters
Use case—using LIME for interpretability
Summary
7
Natural Language Processing Using Deep Learning
Natural Language Processing Using Deep Learning
Document classification
Advanced deep learning text classification
Summary
8
Deep Learning Models Using TensorFlow in R
Deep Learning Models Using TensorFlow in R
Introduction to the TensorFlow library
TensorFlow models
TensorFlow estimators and TensorFlow runs packages
Summary
9
Anomaly Detection and Recommendation Systems
Anomaly Detection and Recommendation Systems
What is unsupervised learning?
How do auto-encoders work?
Training an auto-encoder in R
Using auto-encoders for anomaly detection
Use case – collaborative filtering
Summary
10
Running Deep Learning Models in the Cloud
Running Deep Learning Models in the Cloud
Setting up a local computer for deep learning
Using AWS for deep learning
Using Azure for deep learning
Using Google Cloud for deep learning
Using Paperspace for deep learning
Summary
11
The Next Level in Deep Learning
The Next Level in Deep Learning
Image classification models
Deploying TensorFlow models
Other deep learning topics
Additional deep learning resources
Summary
12
Other Books You May Enjoy
Other Books You May Enjoy
Leave a review - let other readers know what you think

Deploying TensorFlow models

Historically, one of the perceived disadvantages of using R for data science projects was the difficulty in deploying machine learning models built in R. This often meant that companies used R mainly as a prototyping tool to build models which were then rewritten in another language, such as Java and .NET. It is also one of the main reasons cited for companies switching to Python for data science as Python has more glue code, which allows it to interface with other programming languages.

Thankfully, this is changing. One interesting new product from RStudio, called RStudio Connect, allows companies to create a platform for sharing R-Shiny applications, reports in R Markdown, dashboards, and models. This allows companies to serve machine learning models using a REST interface.

The TensorFlow (and Keras) models we have created in this book can be deployed...

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