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.

Preface

Who this book is for

What this book covers

To get the most out of this book

Free Chapter

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

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

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

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

Image Classification Using Convolutional Neural Networks

Image Classification Using Convolutional Neural Networks

Convolutional layers

Image classification using the MXNet library

References/further reading

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

Natural Language Processing Using Deep Learning

Natural Language Processing Using Deep Learning

Document classification

Advanced deep learning text classification

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

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

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

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

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

Training an auto-encoder in R

In this section, we are going to train an auto-encoder in R and show you that it can be used as a dimensionality reduction technique. We will compare it with the approach we took in Chapter 2, Training a Prediction Model, where we used PCA to find the principal components in the image data. In that example, we used PCA and found that 23 factors was sufficient to explain 50% of the variance in the data. We built a neural network model using just these 23 factors to classify a dataset with either 5 or 6. We got 97.86% accuracy in that example.

We are going to follow a similar process in this example, and we will use the MINST dataset again. The following code from Chapter8/encoder.R loads the data. We will use half the data for training an auto-encoder and the other half will be used to build a classification model to evaluate how good the auto-encoder...