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.
Table of Contents (13 chapters)

What this book covers

Chapter 1, Getting Started with Deep Learning, gives an introduction to deep learning and neural networks. It also gives a brief introduction on how to set up your R environment.

Chapter 2, Training a Prediction Model, begins with building neural network models using the existing packages in R. This chapter also discusses overfitting, which is an issue in most deep learning models.

Chapter 3, Deep Learning Fundamentals, teaches how to build a neural network in R from scratch. We then show how our code relates to MXNet, a deep learning library.

Chapter 4, Training Deep Prediction Models, looks at activations and introduces the MXNet library. We then build a deep learning prediction model for a real-life example. We will take a raw dataset of transactional data and develop a data pipeline to create a model that predicts which customers will return in the next 14 days.

Chapter 5, Image Classification Using Convolutional Neural Networks, looks at image classification tasks. First, we will introduce some of the core concepts, such as convolutional and pooling layers, and then we will show how to use these layers to classify images.

Chapter 6, Tuning and Optimizing Models, discusses how to tune and optimize deep learning models. We look at tuning hyperparameters and using data augmentation.

Chapter 7, Natural Language Processing Using Deep Learning, shows how to use deep learning for Natural Language Processing (NLP) tasks. We show how deep learning algorithms outperform traditional NLP techniques, while also being much easier to develop.

Chapter 8, Deep Learning Models Using TensorFlow in R, looks at using the TensorFlow API in R. We also look at some additional packages available within TensorFlow that make developing TensorFlow models simpler and help in hyperparameter selection.

Chapter 9, Anomaly Detection and Recommendation Systems, shows how we can use deep learning models to create embeddings that are lower order representations of data. We then show how to use embeddings for anomaly detection and to create a recommendation system.

Chapter 10, Running Deep Learning Models in the Cloud, covers how to use AWS, Azure, and Google Cloud services to train deep learning models. This chapter shows how to train your models at low-cost in the cloud.

Chapter 11, The Next Level in Deep Learning, concludes the book with an end-to-end solution for image classification. We take a set of image files, train a model, reuse that model for transfer learning and then show how to deploy that model to production. We also briefly discuss Generative Adversarial Networks (GANs) and reinforcement learning.