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)

The problem of overfitting data – the consequences explained

A common issue in machine learning is overfitting data. Generally, overfitting is used to refer to the phenomenon where the model performs better on the data used to train the model than it does on data not used to train the model (holdout data, future real use, and so on). Overfitting occurs when a model memorizes part of the training data and fits what is essentially noise in the training data. The accuracy in the training data is high, but because the noise changes from one dataset to the next, this accuracy does not apply to unseen data, that is, we can say that the model does not generalize very well.

Overfitting can occur at any time, but tends to become more severe as the ratio of parameters to information increases. Usually, this can be thought of as the ratio of parameters to observations, but not always...