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

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Summary

This chapter showed how to get started building and training neural networks to classify data, including image recognition and physical activity data. We looked at packages that can visualize a neural network and we created a number of models to perform classification on data with 10 different categories. Although we only used some neural network packages rather than deep learning packages, our models took a long time to train and we had issues with overfitting.

Some of the basic neural network models in this chapter took a long time to train, even though we did not use all the data available. For the MNIST data, we used approx. 8,000 rows for our binary classification task and only 6,000 rows for our multi-classification task. Even so, one model took almost an hour to train. Our deep learning models will be much more complicated and should be able to process millions...