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)

CNNs

CNNs are the cornerstone of image classification in deep learning. This section gives an introduction to them, explains the history of CNNs, and will explain why they are so powerful.

Before we begin, we will look at a simple deep learning architecture. Deep learning models are difficult to train, so using an existing architecture is often the best place to start. An architecture is an existing deep learning model that was state-of-the-art when initially released. Some examples are AlexNet, VGGNet, GoogleNet, and so on. The architecture we will look at is the original LeNet architecture for digit classification from Yann LeCun and others from the mid 1990s. This architecture was used for the MNIST dataset. This dataset is comprised of grayscale images of 28 x 28 size that contain the digits 0 to 9. The following diagram shows the LeNet architecture:

Figure 5.1: The LeNet...