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)

Using regularization to overcome overfitting

In the previous chapter, we saw the diminishing returns from further training iterations on neural networks in terms of their predictive ability on holdout or test data (that is, data not used to train the model). This is because complex models may memorize some of the noise in the data rather than learning the general patterns. These models then perform much worse when predicting new data. There are some methods we can apply to make our model generalize, that is, fit the overall patterns. These are called regularization and aim to reduce testing errors so that the model performs well on new data.

The most common regularization technique used in deep learning is dropout. However, we will also discuss two other regularization techniques that have a basis in regression and deep learning. These two regularization techniques are L1 penalty...