R Deep Learning Essentials

By : Hodnett, Wiley

3.7 (3)

Buy this Book

R Deep Learning Essentials

3.7 (3)

By: Hodnett, 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

Using auto-encoders for anomaly detection

Now that we have built an auto-encoder and accessed the features of the inner layers, we will move on to an example of how auto-encoders can be used for anomaly detection. The premise here is quite simple: we take the reconstructed outputs from the decoder and see which instances have the most error, that is, which instances are the most difficult for the decoder to reconstruct. The code that is used here is in Chapter9/anomaly.R, and we will be using the UCI HAR dataset that we have already been introduced to in Chapter 2, Training a Prediction Model. If you have not already downloaded the data, go back to that chapter for instructions on how to do so.. The first part of the code loads the data, and we subset the features to only use the ones with mean, sd, and skewness in the feature names:

library(keras)
library(ggplot2)
train.x <...

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R Deep Learning Essentials

By : Hodnett, Wiley

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By: Hodnett, Wiley

Overview of this book

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