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)

Tuning and Optimizing Models

In the last two chapters, we trained deep learning models for classification, regression, and image recognition tasks. In this chapter, we will discuss some important issues in regard to managing deep learning projects. While this chapter may seem somewhat theoretical, if any of the issues discussed are not correctly managed, it can derail your deep learning project. We will look at how to choose evaluation metrics and how to create an estimate of how well a deep learning model will perform before you begin modeling. Next, we will move onto data distribution and the mistakes often made in splitting data into correct partitions for training. Many machine learning projects fail in production use because the data distribution is different to what the model was trained with. We will look at data augmentation, a valuable method to enhance your model&apos...