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R Deep Learning Essentials - Second Edition

By : Mark Hodnett, Joshua F. Wiley

3.7 (3)

Buy this Book

R Deep Learning Essentials

3.7 (3)

By: Mark Hodnett, Joshua F. Wiley

Buy this Book

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.

Preface

Who this book is for

What this book covers

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Free Chapter

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

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

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

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

Image Classification Using Convolutional Neural Networks

CNNs

Convolutional layers

Image classification using the MXNet library

References/further reading

Summary

Tuning and Optimizing Models

Evaluation metrics and evaluating performance

Data preparation

Data augmentation

Tuning hyperparameters

Use case—using LIME for interpretability

Summary

Natural Language Processing Using Deep Learning

Document classification

Advanced deep learning text classification

Summary

Deep Learning Models Using TensorFlow in R

Introduction to the TensorFlow library

TensorFlow models

TensorFlow estimators and TensorFlow runs packages

Summary

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

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

The Next Level in Deep Learning

Image classification models

Deploying TensorFlow models

Summary

In this chapter, we used deep learning for image classification. We discussed the different layer types that are used in image classification: convolutional layers, pooling layers, dropout, dense layers, and the softmax activation function. We saw an R-Shiny application that shows how convolutional layers perform feature engineering on image data.

We used the MXNet deep learning library in R to create a base deep learning model which got 97.1% accuracy. We then developed a CNN deep learning model based on the LeNet architecture, which achieved over 98.3% accuracy on test data. We also used a slightly harder dataset (Fashion MNIST) and created a new model that achieved over 91% accuracy. This accuracy score was better than all of the other scores that used non-deep learning algorithms. In the next chapter, we will build on what we have covered and show you how we can take...