Book Image

R Deep Learning Essentials

By : Joshua F. Wiley
Book Image

R Deep Learning Essentials

By: Joshua F. Wiley

Overview of this book

<p>Deep learning is a branch of machine learning based on a set of algorithms that attempt to model high-level abstractions in data by using model architectures. With the superb memory management and the full integration with multi-node big data platforms, the H2O engine has become more and more popular among data scientists in the field of deep learning.</p> <p>This book will introduce you to the deep learning package H2O with R and help you understand the concepts of deep learning. We will start by setting up important deep learning packages available in R and then move towards building models related to neural networks, prediction, and deep prediction, all of this with the help of real-life examples.</p> <p>After installing the H2O package, you will learn about prediction algorithms. Moving ahead, concepts such as overfitting data, anomalous data, and deep prediction models are explained. Finally, the book will cover concepts relating to tuning and optimizing models.</p>
Table of Contents (14 chapters)
R Deep Learning Essentials
Credits
About the Author
About the Reviewer
www.PacktPub.com
Preface
Bibliography
Index

Solutions for models with low accuracy


One of the most challenging, but also potentially important, aspects of optimizing a model is choosing the values for the hyperparameters. In theory, we want to choose the best combination and, although we are unlikely to ever truly find the global maximum, the techniques in this section can help to find better values for the hyperparameters. Better hyperparameters can often improve the accuracy of a model.

Sometimes, however, a model has poor accuracy due to lacking the variables required for good prediction or because there is not enough data to support training a complex enough model to accurately predict or classify the data. In these cases, either acquiring additional variables/features that can be used as predictors and/or additional cases may be required. This book cannot help you collect more data, but it can present ways to tune and optimize hyperparameters. We'll deal with this next.

Grid search

For more information on tuning hyperparameters...