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)

Introduction to the MXNet deep learning library

The deep learning libraries we will use in this book are MXNet, Keras, and TensorFlow. Keras is a frontend API, which means it is not a standalone library as it requires a lower-level library in the backend, usually TensorFlow. The advantage of using Keras rather than TensorFlow is that it has a simpler interface. We will use Keras in later chapters in this book.

Both MXNet and TensorFlow are multipurpose numerical computation libraries that can use GPUs for mass parallel matrix operations. As such, multi-dimensional matrices are central to both libraries. In R, we are familiar with the vector, which is a one-dimensional array of values of the same type. The R data frame is a two-dimensional array of values, where each column can have different types. The R matrix is a two-dimensional array of values with the same type. Some machine...